From 75227f74a1c3d18fbea2aeef781b2652b9b9105d Mon Sep 17 00:00:00 2001 From: kathweinschenkprophecy Date: Mon, 2 Dec 2024 15:16:45 -0500 Subject: [PATCH 01/14] Add information to output expression field (#453) --- .../gems/transform/bulk-column-expressions.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/Spark/gems/transform/bulk-column-expressions.md b/docs/Spark/gems/transform/bulk-column-expressions.md index 0b28e86750..50f96d4713 100644 --- a/docs/Spark/gems/transform/bulk-column-expressions.md +++ b/docs/Spark/gems/transform/bulk-column-expressions.md @@ -16,13 +16,13 @@ The BulkColumnExpressions Gem primarily lets you cast or change the data type of ## Parameters -| Parameter | Description | -| -------------------------------------------- | ------------------------------------------------------------------ | -| Data Type of the columns to do operations on | The data type of columns to select. | -| Selected Columns | The columns on which to apply transformations | -| Change output column name | An option to add a prefix or suffix to the selected column names | -| Change output column type | The data type that the columns will be transformed into | -| Output Expression | A Spark SQL expression that can be applied to the selected columns | +| Parameter | Description | +| -------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| Data Type of the columns to do operations on | The data type of columns to select. | +| Selected Columns | The columns on which to apply transformations. | +| Change output column name | An option to add a prefix or suffix to the selected column names. | +| Change output column type | The data type that the columns will be transformed into. | +| Output Expression | A Spark SQL expression that can be applied to the selected columns. This field is required. If you only want to select the column, use `column_value` as the expression. | ## Example From 30c4c37bfa3bbaff6c222880367b075165c40b3c Mon Sep 17 00:00:00 2001 From: Alexander Ahn Date: Mon, 2 Dec 2024 12:19:54 -0800 Subject: [PATCH 02/14] Update versions_support.md (#457) --- docs/release_notes/version_chart/versions_support.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/release_notes/version_chart/versions_support.md b/docs/release_notes/version_chart/versions_support.md index 054465dc7d..eacba65c21 100644 --- a/docs/release_notes/version_chart/versions_support.md +++ b/docs/release_notes/version_chart/versions_support.md @@ -32,7 +32,7 @@ You can expect a new Extended Maintenance release two to six weeks after each Ma ### Required resources -Extended Maintenance releases require additional resources, such as CPU and memory. Starting with `v3.4.1.0 EM`, SQL Sandbox is enabled, so every SQL Pipeline session will spin up an additional pod with the following configuration: +Starting with `v3.4.1.0 EM`, SQL Sandbox is enabled, so every SQL Pipeline session will spin up an additional pod with the following configuration: - CPI: 500m - Memory: 512Mi From a9bb417ded9ecf53402f04768a92e55bf500bf31 Mon Sep 17 00:00:00 2001 From: Anya Bida Date: Mon, 2 Dec 2024 14:26:42 -0600 Subject: [PATCH 03/14] add doc for troubleshooting dataproccluster connection (#456) * add doc for troubleshooting dataproccluster connection * put sections into code blocks * adjust title --- docs/Spark/fabrics/dataproc/_category_.json | 6 +++ docs/Spark/fabrics/dataproc/dataproc-tips.md | 44 +++++++++++++++++++ docs/Spark/fabrics/{ => dataproc}/dataproc.md | 23 ++++++---- 3 files changed, 65 insertions(+), 8 deletions(-) create mode 100644 docs/Spark/fabrics/dataproc/_category_.json create mode 100644 docs/Spark/fabrics/dataproc/dataproc-tips.md rename docs/Spark/fabrics/{ => dataproc}/dataproc.md (78%) diff --git a/docs/Spark/fabrics/dataproc/_category_.json b/docs/Spark/fabrics/dataproc/_category_.json new file mode 100644 index 0000000000..dc6d56bb7d --- /dev/null +++ b/docs/Spark/fabrics/dataproc/_category_.json @@ -0,0 +1,6 @@ +{ + "label": "Google Cloud Dataproc", + "position": 7, + "collapsible": true, + "collapsed": true +} diff --git a/docs/Spark/fabrics/dataproc/dataproc-tips.md b/docs/Spark/fabrics/dataproc/dataproc-tips.md new file mode 100644 index 0000000000..60ebe65e23 --- /dev/null +++ b/docs/Spark/fabrics/dataproc/dataproc-tips.md @@ -0,0 +1,44 @@ +--- +title: "Connectivity Tips" +id: gcp-dataproc-fabric-tips +description: If your cluster doesn't connect, try these tips +sidebar_position: 1 +tags: + - deployment + - configuration + - google + - gcp + - dataproc + - livy +--- + +:::tip +Sometimes the Livy Cluster cannot access the Scala or Python libraries. +::: + +### Error + +``` +Creating new Livy Session... +Using prophecy libs path...repo1.maven.org... +Using python libraries...files.pythonhosted.org... +... +org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)\n\nYARN Diagnostics: ","level":"error" +``` + +### Corrective Actions + +**Option 1:** +Adjust network settings on the Livy Cluster to allow traffic from the Scala Prophecy Library url +`repo1.maven.org` and the Python Prophecy Library url +`files.pythonhosted.org`. + +**Option 2:** +Configure the Scala and Python Library Paths as mentioned [here](./dataproc.md). +Configure Scala Library Path. +`gs://prophecy-public-gcp/prophecy-scala-libs/`. +Configure Python Library Path. +`gs://prophecy-public-gcp/prophecy-python-libs/`. + +**Option 3:** +Setup an GCS bucket internally. Create two folders as in the previous option, and add `prophecy-scala-libs` and `prophecy-python-libs` in those folders. diff --git a/docs/Spark/fabrics/dataproc.md b/docs/Spark/fabrics/dataproc/dataproc.md similarity index 78% rename from docs/Spark/fabrics/dataproc.md rename to docs/Spark/fabrics/dataproc/dataproc.md index b4bd727dcf..9459dd414d 100644 --- a/docs/Spark/fabrics/dataproc.md +++ b/docs/Spark/fabrics/dataproc/dataproc.md @@ -26,7 +26,7 @@ Livy is required for the Fabric. Prophecy provides a script required to deploy a 1. If you don't already have a private key, create a private key for the service account that you're using.

- dataproc security + dataproc security

2. Ensure you have the following permissions configured. @@ -79,35 +79,42 @@ gcloud config set account meitestserviceaccount@core-shard-398601.iam.gserviceac 1. Create a Fabric and select **Dataproc**.

- select dataproc + select dataproc

2. Fill out your **Project Name** and **Region**, and upload the **Private Key**.

- configure dataproc + configure dataproc

3. Click on **Fetch environments** and select the Dataproc **cluster** that you created earlier.

- select cluster + select cluster

4. Leave everything as default and provide the **Livy URL**. Locate the **External IP** of your cluster instance. Optionally, you may configure the DNS instead of using the IP. The URL is `http://:8998`.

- livy ip + livy ip

5. Configure the bucket associated with your cluster.

- bucket location + bucket location

6. Add the **Job Size**.

- Job Size + Job Size

7. Configure Scala Library Path. `gs://prophecy-public-gcp/prophecy-scala-libs/`. 8. Configure Python Library Path. `gs://prophecy-public-gcp/prophecy-python-libs/`.

- dependences + dependences

9. Click on **Complete**.

Run a simple Pipeline and make sure that the interim returns data properly. + +```mdx-code-block +import DocCardList from '@theme/DocCardList'; +import {useCurrentSidebarCategory} from '@docusaurus/theme-common'; + + +``` From 5b08d8ed387cfbb84407f3745da72e97c47b0613 Mon Sep 17 00:00:00 2001 From: kathweinschenkprophecy Date: Tue, 3 Dec 2024 09:37:28 -0500 Subject: [PATCH 04/14] update UDF docs to match UI (#455) * update UDF docs to match UI * Add sentence to locate in the UI --- docs/Spark/extensibility/img/add-function.png | Bin 0 -> 376269 bytes .../Spark/extensibility/img/call-function.png | Bin 0 -> 259834 bytes .../extensibility/img/define-function.png | Bin 0 -> 269264 bytes .../extensibility/user-defined-functions.md | 56 +++++++----------- static/img/udf/1.png | Bin 71788 -> 0 bytes static/img/udf/2.1.png | Bin 61204 -> 0 bytes static/img/udf/2.2.png | Bin 54475 -> 0 bytes static/img/udf/3.png | Bin 92346 -> 0 bytes 8 files changed, 21 insertions(+), 35 deletions(-) create mode 100644 docs/Spark/extensibility/img/add-function.png create mode 100644 docs/Spark/extensibility/img/call-function.png create mode 100644 docs/Spark/extensibility/img/define-function.png delete mode 100644 static/img/udf/1.png delete mode 100644 static/img/udf/2.1.png delete mode 100644 static/img/udf/2.2.png delete mode 100644 static/img/udf/3.png diff --git a/docs/Spark/extensibility/img/add-function.png b/docs/Spark/extensibility/img/add-function.png new file mode 100644 index 0000000000000000000000000000000000000000..a0f470eb10d5e8b7f493432df111c1c8953abf19 GIT binary patch literal 376269 zcmYIu2RNH;*mfjltF0YR71`MQgTd)U24bs#a{NwkQ&+ikdAoYS*k8HDYfP zJC)iaiM{=K-}n2z|IU#oM`_Zq(x;N+lfE>X%r

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Step 1 - Open UDF definition window

, - }, - { - "image":"/img/udf/2.1.png", - "description":

Step 2 (Python)- Define Python UDF

, - }, - { - "image":"/img/udf/2.2.png", - "description":

Step 2 (Scala) - Define Scala UDf

- }, - { - "image":"/img/udf/3.png", - "description":

Step 3 - UDFs can now be called by their defined names

, - }, -]; - - -``` +1. Create a new function. You'll find the **Functions** section in the left sidebar of a project page. + +![Add a function to the pipeline](img/add-function.png) + +2. Define the function. + +![Define the function](img/define-function.png) + +3. Call the function. + +![Call the function](img/call-function.png) ````mdx-code-block import Tabs from '@theme/Tabs'; diff --git a/static/img/udf/1.png b/static/img/udf/1.png deleted file mode 100644 index cc45c3afd963d0660f06e94ff7f3f9c8355f3bf3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 71788 zcmZ^~1z1$ww?BSn=mtpzNdf6j36YRS>1Jq!GpR>{Y&C*Cr+=c@`_g4$<>lpkdvE} zn_dE&mX=n`)xt_tT}JL-cI2Hny^XuOvnUsrmzNi(7aymSt2Gyoh=>RmH!l}2F9(u> z!_CLh-PD`I(T(AsLH=tT8A~^FS6gRyTPH`_`*BUpoIKpc>FMtU{df6CPfKsx|B>YA z_ODq;1G(;dxOh0Zx&ALUcU!CfAGZ6Rf7t%o^^Z8Q`@uwC*?L>r>&nW3 z7Z4QtE9d{y^*@^a!>Z+G=_>8yfaG+S&@gwkM2=wNxT02$-`A4oy{$vzVKm#>sHW$*6pZfB) z^TXrwlasT{tDEDK)1#x4lhgCl^Gm+xnpam>`}@Z`yN8E|#|OtJ$H!*}N2kZfCkKZo zM@Pr!7q=Umd)L>u7Z+Dn&hPdQjyM18Z*K0aZ|ohNobT=(?e8D$?H%px9Gsq>xw!?N zonQXh+CMrvy}r3qQ8h?O$v!?l-QGF+v$eamy|=e_aC3W?kdTfPe|dR%dV2BUW9sSI z`Qq=@^z5?ko&iH6+l|fbnS~Wl(Ve;mWaQ`E+}xs|v`%d!{QUf4eSMRpXXxVc^5pbv 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zAVl501T+PEzYwpnbmOhcZ8FJV4-qF%nsj^qc#&7O*5Oxp64x@}f}`x&O{H{sbapAv zVo7+V7M)d(mxIX#PRkSVC+dB>g@!ay_}aM1Tq~{~F)8`YcFq=scyW7I1eI4=c`eZ zlg;RBr$JM)S*hd1%jpNP144I_tNMq&Ibu0*pz`I~@J^CjCl3Tf9hd3L;#Q8? z0Y0`kM0EhI;Z0k|0Bdybmp-wjLGLOjSc3fM%p?VZKpZ2#(V@A2xJJ zb@2ME7MO0Oyh^+a#{wEOa+06YQ;azO*o}DM+7eH`u&JIeY3KSnlE${2M7~67ztVYgl4eGSoLR>@YJw z^+hTU=sqX5URg!vITefo83`mP5rh3$`U>JJ-p7Za4y7Yv0L9Gnroo8xcMc4QOQ6Kl}EJupi|() MF$2xqqhRp=1C>ft*Z=?k From ccc16edb6e4c9b4edd593425a3a91f92a57b863b Mon Sep 17 00:00:00 2001 From: Alexander Ahn Date: Tue, 3 Dec 2024 08:23:37 -0800 Subject: [PATCH 05/14] Versions support typo fix (#459) * Update versions_support.md * Update versions_support.md --- docs/release_notes/version_chart/versions_support.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/release_notes/version_chart/versions_support.md b/docs/release_notes/version_chart/versions_support.md index eacba65c21..4e510ec97c 100644 --- a/docs/release_notes/version_chart/versions_support.md +++ b/docs/release_notes/version_chart/versions_support.md @@ -34,7 +34,7 @@ You can expect a new Extended Maintenance release two to six weeks after each Ma Starting with `v3.4.1.0 EM`, SQL Sandbox is enabled, so every SQL Pipeline session will spin up an additional pod with the following configuration: -- CPI: 500m +- CPU: 500m - Memory: 512Mi After upgrading to 3.4.1, you must enable SQL Sandbox Config in the UI by navigating to the **Sandbox Config** tab in the Config sub tab of the Admin Settings. From 7e0d38fd28e2e7e8dad545375d90a6e8f3da5c27 Mon Sep 17 00:00:00 2001 From: RJ <55955360+neontty@users.noreply.github.com> Date: Tue, 3 Dec 2024 11:04:42 -0600 Subject: [PATCH 06/14] Rj/fix patch version dates (#458) * adding fix for patch versions * syntax * tested output * fixed bug. start dates are back to normal --- .../version_chart/version_chart.md | 120 +++++++++--------- scripts/find_versions_from_git_tags.py | 15 ++- 2 files changed, 73 insertions(+), 62 deletions(-) diff --git a/docs/release_notes/version_chart/version_chart.md b/docs/release_notes/version_chart/version_chart.md index b8c7c0a66d..b8db79d7ff 100644 --- a/docs/release_notes/version_chart/version_chart.md +++ b/docs/release_notes/version_chart/version_chart.md @@ -28,74 +28,74 @@ Prophecy versions that are labeled with `EM` are Extended Maintenance releases. | Prophecy version | [Prophecy Scala libs](https://mvnrepository.com/artifact/io.prophecy/prophecy-libs) | [Prophecy Python libs](https://pypi.org/project/prophecy-libs/) | Release Date | End-of-support Date | | ---------------- | ----------------------------------------------------------------------------------- | --------------------------------------------------------------- | ------------ | ------------------- | | v3.4.1.0 EM | 8.5.0 | 1.9.24 | 2024/11/25 | 2025/11/25 | -| v3.4.0.3 | 8.4.0 | 1.9.24 | 2024/11/20 | 2025/05/20 | -| v3.4.0.2 | 8.4.0 | 1.9.24 | 2024/11/14 | 2025/05/14 | -| v3.4.0.1 | 8.4.0 | 1.9.24 | 2024/11/13 | 2025/05/13 | +| v3.4.0.3 | 8.4.0 | 1.9.24 | 2024/11/20 | 2025/04/29 | +| v3.4.0.2 | 8.4.0 | 1.9.24 | 2024/11/13 | 2025/04/29 | +| v3.4.0.1 | 8.4.0 | 1.9.24 | 2024/11/13 | 2025/04/29 | | v3.4.0.0 | 8.4.0 | 1.9.24 | 2024/10/29 | 2025/04/29 | -| v3.3.11.7 | 8.2.1 | 1.9.16 | 2024/10/24 | 2025/04/24 | -| v3.3.11.6 | 8.2.1 | 1.9.16 | 2024/10/18 | 2025/04/18 | -| v3.3.11.5 | 8.2.1 | 1.9.16 | 2024/10/09 | 2025/04/09 | -| v3.3.11.4 | 8.2.1 | 1.9.16 | 2024/10/01 | 2025/04/01 | -| v3.3.11.3 | 8.2.1 | 1.9.16 | 2024/09/30 | 2025/03/30 | -| v3.3.11.2 | 8.2.1 | 1.9.16 | 2024/09/24 | 2025/03/24 | +| v3.3.11.7 | 8.2.1 | 1.9.16 | 2024/10/24 | 2025/03/20 | +| v3.3.11.6 | 8.2.1 | 1.9.16 | 2024/10/18 | 2025/03/20 | +| v3.3.11.5 | 8.2.1 | 1.9.16 | 2024/10/09 | 2025/03/20 | +| v3.3.11.4 | 8.2.1 | 1.9.16 | 2024/10/01 | 2025/03/20 | +| v3.3.11.3 | 8.2.1 | 1.9.16 | 2024/09/30 | 2025/03/20 | +| v3.3.11.2 | 8.2.1 | 1.9.16 | 2024/09/24 | 2025/03/20 | | v3.3.11.1 | 8.2.1 | 1.9.16 | 2024/09/20 | 2025/03/20 | | v3.3.11.0 | 8.2.1 | 1.9.16 | 2024/09/20 | 2025/03/20 | -| v3.3.10.3 | 8.1.8 | 1.9.14 | 2024/09/11 | 2025/03/11 | -| v3.3.10.2 | 8.1.8 | 1.9.14 | 2024/09/06 | 2025/03/06 | -| v3.3.10.1 | 8.1.8 | 1.9.14 | 2024/09/05 | 2025/03/05 | +| v3.3.10.3 | 8.1.8 | 1.9.14 | 2024/09/11 | 2025/03/02 | +| v3.3.10.2 | 8.1.8 | 1.9.14 | 2024/09/06 | 2025/03/02 | +| v3.3.10.1 | 8.1.8 | 1.9.14 | 2024/09/05 | 2025/03/02 | | v3.3.10.0 | 8.1.4 | 1.9.14 | 2024/09/02 | 2025/03/02 | -| v3.3.9.7 | 8.0.31 | 1.9.9 | 2024/08/28 | 2025/02/28 | -| v3.3.9.6 | 8.0.31 | 1.9.9 | 2024/08/22 | 2025/02/22 | -| v3.3.9.5 | 8.0.31 | 1.9.9 | 2024/08/19 | 2025/02/19 | -| v3.3.9.4 | 8.0.31 | 1.9.9 | 2024/08/15 | 2025/02/15 | -| v3.3.9.3 | 8.0.31 | 1.9.9 | 2024/08/13 | 2025/02/13 | -| v3.3.9.2 | 8.0.31 | 1.9.9 | 2024/08/13 | 2025/02/13 | -| v3.3.8.7 | 8.0.29 | 1.9.9 | 2024/08/02 | 2025/02/02 | -| v3.3.9.1 | 8.0.29 | 1.9.9 | 2024/07/29 | 2025/01/29 | -| v3.3.8.6 | 8.0.29 | 1.9.9 | 2024/07/29 | 2025/01/29 | -| v3.3.8.5 | 8.0.29 | 1.9.9 | 2024/07/23 | 2025/01/23 | +| v3.3.9.7 | 8.0.31 | 1.9.9 | 2024/08/28 | 2025/01/22 | +| v3.3.9.6 | 8.0.31 | 1.9.9 | 2024/08/22 | 2025/01/22 | +| v3.3.9.5 | 8.0.31 | 1.9.9 | 2024/08/19 | 2025/01/22 | +| v3.3.9.4 | 8.0.31 | 1.9.9 | 2024/08/15 | 2025/01/22 | +| v3.3.9.3 | 8.0.31 | 1.9.9 | 2024/08/13 | 2025/01/22 | +| v3.3.9.2 | 8.0.31 | 1.9.9 | 2024/08/13 | 2025/01/22 | +| v3.3.8.7 | 8.0.29 | 1.9.9 | 2024/08/02 | 2025/01/08 | +| v3.3.9.1 | 8.0.29 | 1.9.9 | 2024/07/29 | 2025/01/22 | +| v3.3.8.6 | 8.0.29 | 1.9.9 | 2024/07/29 | 2025/01/08 | +| v3.3.8.5 | 8.0.29 | 1.9.9 | 2024/07/23 | 2025/01/08 | | v3.3.9.0 | 8.0.29 | 1.9.9 | 2024/07/22 | 2025/01/22 | -| v3.3.8.4 | 8.0.29 | 1.9.9 | 2024/07/22 | 2025/01/22 | -| v3.3.8.3 | 8.0.29 | 1.9.9 | 2024/07/17 | 2025/01/17 | -| v3.3.8.2 | 8.0.29 | 1.9.9 | 2024/07/16 | 2025/01/16 | -| v3.3.8.1 | 8.0.25 | 1.9.9 | 2024/07/10 | 2025/01/10 | +| v3.3.8.4 | 8.0.29 | 1.9.9 | 2024/07/22 | 2025/01/08 | +| v3.3.8.3 | 8.0.29 | 1.9.9 | 2024/07/17 | 2025/01/08 | +| v3.3.8.2 | 8.0.29 | 1.9.9 | 2024/07/16 | 2025/01/08 | +| v3.3.8.1 | 8.0.23 | 1.9.9 | 2024/07/09 | 2025/01/08 | | v3.3.8.0 | 8.0.23 | 1.9.9 | 2024/07/08 | 2025/01/08 | -| v3.3.7.13 | 8.0.11-1 | 1.9.7 | 2024/07/05 | 2025/01/05 | -| v3.3.7.12 | 8.0.11-1 | 1.9.7 | 2024/07/04 | 2025/01/04 | -| v3.3.7.11 | 8.0.11-1 | 1.9.5 | 2024/07/03 | 2025/01/03 | -| v3.3.7.10 | 8.0.11 | 1.9.5 | 2024/07/01 | 2025/01/01 | -| v3.3.7.9 | 8.0.11 | 1.9.5 | 2024/06/28 | 2024/12/28 | -| v3.3.7.8 | 8.0.11 | 1.9.5 | 2024/06/27 | 2024/12/27 | -| v3.3.7.7 | 8.0.11 | 1.9.5 | 2024/06/24 | 2024/12/24 | -| v3.3.7.6 | 8.0.11 | 1.9.5 | 2024/06/21 | 2024/12/21 | -| v3.3.7.5 | 8.0.11 | 1.9.5 | 2024/06/20 | 2024/12/20 | -| v3.3.7.4 | 8.0.11 | 1.9.5 | 2024/06/19 | 2024/12/19 | -| v3.3.7.3 | 8.0.11 | 1.9.5 | 2024/06/14 | 2024/12/14 | -| v3.3.7.2 | 8.0.11 | 1.9.5 | 2024/06/12 | 2024/12/12 | +| v3.3.7.13 | 8.0.11-1 | 1.9.7 | 2024/07/05 | 2024/12/10 | +| v3.3.7.12 | 8.0.11-1 | 1.9.7 | 2024/07/04 | 2024/12/10 | +| v3.3.7.11 | 8.0.11-1 | 1.9.5 | 2024/07/03 | 2024/12/10 | +| v3.3.7.10 | 8.0.11 | 1.9.5 | 2024/07/01 | 2024/12/10 | +| v3.3.7.9 | 8.0.11 | 1.9.5 | 2024/06/28 | 2024/12/10 | +| v3.3.7.8 | 8.0.11 | 1.9.5 | 2024/06/27 | 2024/12/10 | +| v3.3.7.7 | 8.0.11 | 1.9.5 | 2024/06/24 | 2024/12/10 | +| v3.3.7.6 | 8.0.11 | 1.9.5 | 2024/06/21 | 2024/12/10 | +| v3.3.7.5 | 8.0.11 | 1.9.5 | 2024/06/20 | 2024/12/10 | +| v3.3.7.4 | 8.0.11 | 1.9.5 | 2024/06/19 | 2024/12/10 | +| v3.3.7.3 | 8.0.11 | 1.9.5 | 2024/06/14 | 2024/12/10 | +| v3.3.7.2 | 8.0.11 | 1.9.5 | 2024/06/12 | 2024/12/10 | | v3.3.7.0 | 8.0.5 | 1.9.4 | 2024/06/10 | 2024/12/10 | -| v3.3.6.3 | 7.1.102 | 1.8.19 | 2024/06/06 | 2024/12/06 | -| v3.3.6.2 | 7.1.102 | 1.8.19 | 2024/06/04 | 2024/12/04 | -| v3.3.6.1 | 7.1.102 | 1.8.19 | 2024/05/27 | 2024/11/27 | +| v3.3.6.3 | 7.1.102 | 1.8.19 | 2024/06/06 | 2024/11/21 | +| v3.3.6.2 | 7.1.102 | 1.8.19 | 2024/06/04 | 2024/11/21 | +| v3.3.6.1 | 7.1.102 | 1.8.19 | 2024/05/27 | 2024/11/21 | | v3.3.6.0 | 7.1.97 | 1.8.19 | 2024/05/21 | 2024/11/21 | -| v3.3.5.7 | 7.1.96 | 1.8.15 | 2024/05/17 | 2024/11/17 | -| v3.3.5.6 | 7.1.96 | 1.8.15 | 2024/05/13 | 2024/11/13 | -| v3.3.5.5 | 7.1.95 | 1.8.15 | 2024/05/10 | 2024/11/10 | -| v3.3.5.4 | 7.1.95 | 1.8.15 | 2024/05/09 | 2024/11/09 | -| v3.3.5.3 | 7.1.95 | 1.8.15 | 2024/05/07 | 2024/11/07 | -| v3.3.5.2 | 7.1.95 | 1.8.15 | 2024/05/06 | 2024/11/06 | -| v3.3.5.1 | 7.1.93 | 1.8.15 | 2024/05/06 | 2024/11/06 | +| v3.3.5.7 | 7.1.96 | 1.8.15 | 2024/05/17 | 2024/10/30 | +| v3.3.5.6 | 7.1.96 | 1.8.15 | 2024/05/13 | 2024/10/30 | +| v3.3.5.5 | 7.1.95 | 1.8.15 | 2024/05/10 | 2024/10/30 | +| v3.3.5.4 | 7.1.95 | 1.8.15 | 2024/05/09 | 2024/10/30 | +| v3.3.5.3 | 7.1.95 | 1.8.15 | 2024/05/07 | 2024/10/30 | +| v3.3.5.2 | 7.1.95 | 1.8.15 | 2024/05/06 | 2024/10/30 | +| v3.3.5.1 | 7.1.93 | 1.8.15 | 2024/05/06 | 2024/10/30 | | v3.3.5.0 | 7.1.93 | 1.8.15 | 2024/04/30 | 2024/10/30 | -| v3.3.4.2 | 7.1.88 | 1.8.15 | 2024/04/17 | 2024/10/17 | -| v3.3.4.1 | 7.1.88 | 1.8.15 | 2024/04/15 | 2024/10/15 | +| v3.3.4.2 | 7.1.88 | 1.8.15 | 2024/04/17 | 2024/10/13 | +| v3.3.4.1 | 7.1.88 | 1.8.15 | 2024/04/15 | 2024/10/13 | | v3.3.4.0 | 7.1.88 | 1.8.15 | 2024/04/13 | 2024/10/13 | -| v3.3.3.2 | 7.1.83 | 1.8.13 | 2024/04/10 | 2024/10/10 | -| v3.3.3.1 | 7.1.83 | 1.8.13 | 2024/04/03 | 2024/10/03 | +| v3.3.3.2 | 7.1.83 | 1.8.13 | 2024/04/10 | 2024/10/01 | +| v3.3.3.1 | 7.1.83 | 1.8.13 | 2024/04/03 | 2024/10/01 | | v3.3.3.0 | 7.1.82 | 1.8.13 | 2024/04/01 | 2024/10/01 | -| v3.3.2.4 | 7.1.83 | 1.8.12 | 2024/03/27 | 2024/09/27 | -| v3.3.2.3 | 7.1.79 | 1.8.12 | 2024/03/26 | 2024/09/26 | -| v3.3.2.2 | 7.1.79 | 1.8.12 | 2024/03/21 | 2024/09/21 | -| v3.3.2.1 | 7.1.79 | 1.8.9 | 2024/03/15 | 2024/09/15 | -| v3.3.2.0 | 7.1.79 | 1.8.9 | 2024/03/14 | 2024/09/14 | -| v3.3.1.3 | 7.1.72-1 | 1.8.7 | 2024/03/08 | 2024/09/08 | -| v3.3.1.2 | 7.1.72-1 | 1.8.7 | 2024/03/07 | 2024/09/07 | -| v3.3.1.1 | 7.1.72 | 1.8.7 | 2024/03/05 | 2024/09/05 | +| v3.3.2.4 | 7.1.83 | 1.8.12 | 2024/03/27 | 2024/09/13 | +| v3.3.2.3 | 7.1.79 | 1.8.12 | 2024/03/25 | 2024/09/13 | +| v3.3.2.2 | 7.1.79 | 1.8.12 | 2024/03/20 | 2024/09/13 | +| v3.3.2.1 | 7.1.79 | 1.8.9 | 2024/03/15 | 2024/09/13 | +| v3.3.2.0 | 7.1.79 | 1.8.9 | 2024/03/13 | 2024/09/13 | +| v3.3.1.3 | 7.1.72-1 | 1.8.7 | 2024/03/08 | 2024/08/28 | +| v3.3.1.2 | 7.1.72-1 | 1.8.7 | 2024/03/07 | 2024/08/28 | +| v3.3.1.1 | 7.1.72 | 1.8.7 | 2024/03/05 | 2024/08/28 | diff --git a/scripts/find_versions_from_git_tags.py b/scripts/find_versions_from_git_tags.py index c39ac45781..a72b62b82c 100644 --- a/scripts/find_versions_from_git_tags.py +++ b/scripts/find_versions_from_git_tags.py @@ -12,11 +12,17 @@ "v3.4.1.0", ] +def get_commit_date(repo, tag_name): + try: + tag = repo.tags[tag_name] + return datetime.fromtimestamp(tag.commit.committed_date) + except git.exc.GitCommandError: + IOError(f"Error checking out tag '{tag_name}'.") + def get_versions_for_tag(repo, tag_name): deps_file_path = "project/Dependencies.scala" try: - tag = repo.tags[tag_name] file_contents = repo.git.show(f"{tag_name}:{deps_file_path}") prophecy_libs_version_regex = r'prophecyLibsVersion\s*=\s*"([^"]+)"' @@ -31,10 +37,15 @@ def get_versions_for_tag(repo, tag_name): #print("pythonProphecyLibsVersion not found.") return # ignore for now if we can't find missing old versions - create_date = datetime.fromtimestamp(tag.commit.committed_date) + + create_date = get_commit_date(repo, tag_name) if tag_name in LTS_VERSIONS: end_of_support_date = create_date + relativedelta(years=1) tag_name = tag_name + " EM" + elif not tag_name.endswith(".0"): + # for patch versions, set the same end date as associated minor version + minor_create_date = get_commit_date(repo, re.sub(r'(\d+)\.(\d+)\.(\d+)\.(\d+)$', r'\1.\2.\3.0', tag_name)) + end_of_support_date = minor_create_date + relativedelta(months=6) else: end_of_support_date = create_date + relativedelta(months=6) ver_dict = { From b6097ef10ab8bd129333da2344bb4267e11ea12b Mon Sep 17 00:00:00 2001 From: kathweinschenkprophecy Date: Tue, 3 Dec 2024 12:44:34 -0500 Subject: [PATCH 07/14] Update the docs landing page (#454) * Update the docs landing page * small fixes to udf file --- .../extensibility/user-defined-functions.md | 6 +- docs/img/gem-builder.png | Bin 496942 -> 0 bytes docs/index.md | 68 +++++++++--------- 3 files changed, 38 insertions(+), 36 deletions(-) delete mode 100644 docs/img/gem-builder.png diff --git a/docs/Spark/extensibility/user-defined-functions.md b/docs/Spark/extensibility/user-defined-functions.md index 027aba6b9c..b86594c661 100644 --- a/docs/Spark/extensibility/user-defined-functions.md +++ b/docs/Spark/extensibility/user-defined-functions.md @@ -16,15 +16,15 @@ Prophecy lets you create user-defined functions (UDFs) which can be used anywher | Parameter | Description | Required | | :---------------------- | :------------------------------------------------------------------------------------------------------------------------------------------ | :------- | | Function name | The name of the function as it appears in your project. | True | -| UDF Name | Name of the UDF that will register it. All calls to the UDF will use this name. | True | -| Definition | Definition of the UDF function.
Eg: `udf((value:Int)=>value*value)` | True | +| UDF Name | The name of the UDF that will register it. All calls to the UDF will use this name. | True | +| Definition | Definition of the UDF function.
For example, `udf((value:Int)=>value*value)` | True | | UDF initialization code | Code block that contains initialization of entities used by UDFs. This could, for example, contain any static mapping that a UDF might use. | False | ## Steps There are a few steps to take to create and use a new UDF. -1. Create a new function. You'll find the **Functions** section in the left sidebar of a project page. +1. Create a new function. You can find the **Functions** section in the left sidebar of a project page. ![Add a function to the pipeline](img/add-function.png) diff --git a/docs/img/gem-builder.png b/docs/img/gem-builder.png deleted file mode 100644 index 354f802156ccba530f58fbb218c5e67fe3005b4b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 496942 zcma&O1z4O*vOkPNfFL0R_XG&;&H%v)5(0y3$RI(6;5KN0;E>=pxD4)updm;gxVyW% z4DtSBm5K7n);%~e#8SP*3_By{8_NT`SsGU68rnH=e1G(-tW37O(w<+sSp z|7?STgcNLrg!a!idWh%4Pc-81q0c{_s2RxrJ7NaP|Fp&+&OrU2GOE!-x28=^#2S$v z*(>NeA|X9{@$iSN`1biBVl-VVO&up46=e}Kuq}t_d+<9DhnubaLoXyTHxWe97UX0~ z>t<_X=P2SPPX9*>5k&c+nv)%<>df7Sa5O+3k!2{@o@6+up?TqJG$FBnYyvtIWqiX zkpCG+2IOewU}f)Q1-7Go7}xY2*x5;(p8jE?fBpPpolaKf|2vbN<3G2B*dXV_H=NuY zT%7+J8_`wlp;koA!3uiv+_iXO4Q&%ow6aD9Wy#%7`ez2cT0 zy#%=zE>E$K3H17UUJfV!ov3nXo!|Fn1sKSDc;ixVn z`a19$D2H#=^}Uqk;sYB5dp=7D1Crzk%vCEC?;8fnsd>etM2*hVudy_QcB@$Zs68sS zroXGzChO8{(gq%e4JafH$X2zqo0=sxVh61je(Ba;yT7Sy_|aV10Q2I2Et7bGuAhnZ 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users to be productive with data engineering. It also replaces legacy ETL products. Following are the primary pillars of Prophecy Copilot: +Prophecy empowers users of all skill levels to excel in data engineering. +Anyone can use the visual interface, especially with the help of the [Data Copilot](./concepts/copilot/), to achieve results that go beyond traditional ETL tools. +Below, we highlight Prophecy's core pillars. -## Visual Interface +## Visual interface -Prophecy's designer provides a visual drag-and-drop canvas to develop data Pipelines, where business logic can be written as simple SQL expressions. We believe it is superior in every way to developing custom scripts: +Prophecy's designer provides a visual drag-and-drop canvas to develop data Pipelines. The visual interface: -- **Many data users:** Our users do not need to be experts in Spark or Airflow, and this enables all the data users - data engineers, visual ETL developers, data scientists and data analysts to succeed. -- **Productive development:** It is very quick to develop Pipelines - you can drag and drop visual blocks into a Pipeline and interactively run them to see the data after every step along the journey. Even coding data engineers are more productive with our product. +- **Is accessible to a variety users.** Users don't need to be experts in Spark or Airflow to leverage the visual interface. Anyone, including data engineers, visual ETL developers, data scientists and data analysts, can develop Pipelines this way. +- **Facilitates productivity.** The visual interface speeds up the Pipeline development process because you can interactively run your transformations at each phase of development. +- **Generates reusable code.** Each component of the Pipeline is automatically compiled into code that you can reuse and customize. ![Data Pipeline](./img/datapipeline.png) -## Code & DevOps - -Running at scale requires bringing the best software engineering practices to the refinement of data. Rapid development & deployment of data Pipeline can be achieved by code on Git & high test coverage, coupled with continuous integration & continuous deployment. Prophecy does the following to make this process work: +### Gems -- **Visual data Pipelines as code:** Prophecy's code editor stores visual data Pipelines as high-quality code on Git -- **High test coverage:** Prophecy makes test-generation & editing easy, and this results in high test coverage for our users after the switch -- **Metadata as code:** Much of the metadata from projects including workflows, schedules and datasets, and computed metadata such as column-level lineage are also stored on Git with Prophecy. +One of the core building blocks of visual data Pipelines is what we call [Gems](./concepts/project/gems). +Gems are predefined visual blocks (like Lego pieces) that let you perform a variety of operations including data transformations and storing data. -These provide the following benefits: +Prophecy provides dozens of Gems ready to use out of the box. Gems consist of Spark or SQL code, user-defined properties that are populated through the UI, and a function that specifies the visual layout of the Gem. Jump to the section on [Extensibility](#extensibility) to learn about Gem customization. -- **DevOps practices:** For data projects, the Pipeline code, schedules & tests are stored on Git - with every developer working on her branch. Every change gets reviewed, and on every commit tests are run. The code is then deployed to be run per the schedule. Bad changes can be rolled back reliably. This process enables data teams to quickly move new and edited changes to production with high confidence. -- **Zero lock-in:** Prophecy generated code is in 100% open-source format with data Pipelines in Apache Spark format and schedules in Apache Airflow format. This ensures freedom from lock-in and cost management. -- **Git versioning for time travel:** Given that data projects including metadata are stored together on Git, the user can traverse across time, and for example see how a value is computed today, and compare it with how it was computed a month earlier to understand why a breakage has occurred. +## Code and DevOps -## Complete Product +Running at scale requires applying strong software engineering practices to data refinement. Rapid development and deployment of data pipelines can be achieved by using code stored in Git, maintaining high test coverage, and implementing [continuous integration and continuous deployment](./tutorials/Orchestration/reliable-ci-cd/). In Prophecy, this looks like: -In the cloud, data engineering only has point products forcing the customer data platform teams to stitch together custom solutions. However, this means that development, deployment and metadata is spread across multiple systems - this is not sustainable over the medium term. +- **Pipelines stored as code.** Prophecy's code editor stores visual data Pipelines as high-quality code on Git. +- **High test coverage.** Prophecy makes test generation and editing easy. +- **Metadata as code.** Metadata is also stored as code on Git. This includes metadata from projects like workflows, schedules, and datasets. It also includes computed metadata such as column-level lineage. -Prophecy instead chooses to provide a complete product: +These provide the following benefits: -- Build data Pipelines on Spark -- Deploy & Schedule data Pipelines on Airflow -- Get unified metadata with search that includes business logic, datasets, execution information -- Column level lineage to see how values flow end-to-end +- **Alignment with DevOps practices.** You can follow DevOps practices in Prophecy such as collaboration, automation, and reviews because of the integration with Git. +- **Zero lock-in.** Prophecy generated code is in 100% open-source format with data Pipelines in Apache Spark format and schedules in Apache Airflow format. This ensures freedom from lock-in and cost management. +- **Git versioning for time travel.** Given that data projects including metadata are stored together on Git, you can traverse across time. For example, you can compare how a value was computed today against how it was computed a month earlier to understand why a breakage has occurred. -![Complete](./img/complete.png) +## Complete product -## Extensibility +In the cloud, data engineering often relies on point products, which forces customer data platform teams to create custom solutions. However, this approach leads to fragmented development, deployment, and metadata management across multiple systems, making it unsustainable in the long term. -Standardization is essential to scale but the scope of Data Engineering has increased quite beyond what traditional ETL or data integration products provide. It is no longer acceptable to only have a limited palette of visual components, where users get stuck if something does not fit the paradigm. +Prophecy instead chooses to provide a complete product that lets you: -Prophecy provides extensibility - not as an afterthought - but as the concept at the heart of our architecture. Prophecy provides a set of inbuilt visual operators - such as Spark standard library, and Delta lake library. New visual operators are defined by our customers - usually the data platform teams develop these extensions as data products for their organizations. These extensions include custom connectors and transforms such as an encryption library. Customers also ask us to develop new operators as they need them and we're happy to add new libraries as requested. +- Build data Pipelines on Spark. +- Deploy and schedule data Pipelines on Airflow. +- Access unified metadata with search that includes business logic, datasets, execution information. +- View column-level lineage to see how values flow from end to end in the Pipeline. -![Extensible](./img/extensible.png) +![Complete](./img/complete.png) -### Gems +## Extensibility {#extensibility} -Prophecy enables you to construct data Pipelines from predefined visual blocks (like Lego pieces), that we call Gems. Prophecy provides dozens of Gems ready to use out of the box. We also offer Gem customization. The custom Gems require users to be able to write Spark code and our customers often rely on Prophecy to help out. Gems include Spark code, properties that are blanks to be filled by the user from UI, and a function to describe the visual layout of the Gem. +Standardization is crucial for scaling, but it's no longer sufficient to rely on a limited set of components, where users are blocked when something falls outside the predefined paradigm. -In the Gem builder UI, the left half is where you write the template code for the Gem. The top right has a functional UI generated in real time from the template code. You can fill business logic values into this generated UI, and you can immediately see the generated code at the bottom right. You can run this generated code against input data and see the results to ensure everything is working well. +Because of this, extensibility is at the heart of Prophecy's architecture. In addition to built-in visual operators, custom operators can be defined at any time, including [custom Gems](./Spark/extensibility/gem-builder/). Usually, data platform teams develop these extensions as data products for their organizations. These extensions can include custom connectors and transforms such as an encryption library. Prophecy is also always building new operators to grow our functionality. -![Complete](./img/gem-builder.png) +![Extensible](./img/extensible.png) From 60db8a3521c2c12927e6c2920dcc01030deaadaf Mon Sep 17 00:00:00 2001 From: kathweinschenkprophecy Date: Wed, 4 Dec 2024 15:18:54 -0500 Subject: [PATCH 08/14] add requirements template (#460) --- docs/Spark/gems/_gem-requirements.mdx | 30 +++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 docs/Spark/gems/_gem-requirements.mdx diff --git a/docs/Spark/gems/_gem-requirements.mdx b/docs/Spark/gems/_gem-requirements.mdx new file mode 100644 index 0000000000..bb7f604ca6 --- /dev/null +++ b/docs/Spark/gems/_gem-requirements.mdx @@ -0,0 +1,30 @@ +export default function Requirements(props) { + return ( +

+ ); +} From 6750bd8a67b64c0d43961f02401c981c68f92879 Mon Sep 17 00:00:00 2001 From: kathweinschenkprophecy Date: Mon, 9 Dec 2024 16:18:47 -0500 Subject: [PATCH 09/14] Add bootstrap badges for gems (#463) * Add bootstrap badges for gems * Add badges to all gems --- docs/SQL/gems/custom/custom.md | 2 ++ docs/SQL/gems/gems.md | 8 +++++--- docs/SQL/gems/joins.md | 6 ++++-- docs/SQL/gems/subgraph/subgraph.md | 4 +++- docs/SQL/gems/transform/aggregate.md | 2 ++ docs/SQL/gems/transform/deduplicate.md | 2 ++ docs/SQL/gems/transform/flattenschema.md | 2 ++ .../gems/custom/delta-table-operations.md | 2 ++ docs/Spark/gems/custom/file-operation.md | 2 ++ docs/Spark/gems/custom/rest-api-enrich.md | 2 ++ docs/Spark/gems/custom/script.md | 4 +++- docs/Spark/gems/custom/sql-statement.md | 2 ++ docs/Spark/gems/gems.md | 14 +++++++++----- docs/Spark/gems/join-split/compare-columns.md | 2 ++ docs/Spark/gems/join-split/join-split.md | 2 +- docs/Spark/gems/join-split/join.md | 2 ++ docs/Spark/gems/join-split/repartition.md | 2 ++ docs/Spark/gems/join-split/row-distributor.md | 2 ++ docs/Spark/gems/machine-learning/ml-openai.md | 18 +++++++++++------- .../machine-learning/ml-pinecone-lookup.md | 16 +++++++++------- .../machine-learning/ml-text-processing.md | 16 +++++++++------- .../gems/source-target/advanced/lookup.md | 2 ++ .../synthetic-data-generator.md | 2 ++ docs/Spark/gems/subgraph/basicSubgraph.md | 2 ++ docs/Spark/gems/subgraph/subgraph.md | 2 +- docs/Spark/gems/subgraph/tableIterator.md | 2 ++ docs/Spark/gems/transform/aggregate.md | 2 ++ .../gems/transform/bulk-column-expressions.md | 2 ++ .../Spark/gems/transform/bulk-column-rename.md | 2 ++ docs/Spark/gems/transform/data-cleansing.md | 2 ++ docs/Spark/gems/transform/deduplicate.md | 2 ++ docs/Spark/gems/transform/dynamic-select.md | 2 ++ docs/Spark/gems/transform/filter.md | 2 ++ docs/Spark/gems/transform/flattenschema.md | 2 ++ docs/Spark/gems/transform/limit.md | 4 +++- docs/Spark/gems/transform/order-by.md | 2 ++ docs/Spark/gems/transform/reformat.md | 2 ++ docs/Spark/gems/transform/schema-transform.md | 2 ++ docs/Spark/gems/transform/set-operation.md | 2 ++ docs/Spark/gems/transform/unpivot.md | 2 ++ docs/Spark/gems/transform/window-function.md | 2 ++ docusaurus.config.js | 1 + 42 files changed, 119 insertions(+), 36 deletions(-) diff --git a/docs/SQL/gems/custom/custom.md b/docs/SQL/gems/custom/custom.md index 1bb96c1775..15a32d2b28 100644 --- a/docs/SQL/gems/custom/custom.md +++ b/docs/SQL/gems/custom/custom.md @@ -8,6 +8,8 @@ tags: - sql --- +

SQL Gem

+ :::caution This page about Custom SQL Gems is under construction. Please pardon our dust. ::: diff --git a/docs/SQL/gems/gems.md b/docs/SQL/gems/gems.md index 147872099a..f91e14c7e6 100644 --- a/docs/SQL/gems/gems.md +++ b/docs/SQL/gems/gems.md @@ -1,5 +1,5 @@ --- -title: Gems +title: SQL Gems id: sql-gems description: Gems are data seeds, sources, transformations, and targets sidebar_position: 2 @@ -11,9 +11,11 @@ tags: - cte --- -In Prophecy and dbt, Data [Models](/docs/concepts/project/models.md) are SQL statements that build a single table or view. Prophecy visualizes Data Models to illustrate the many steps needed to generate a single table or view. Gems represent the individual steps. A Gem is a unit of functionality ranging from reading, transforming, writing, and various other ad-hoc operations on data. +In Prophecy and dbt, data [models](/docs/concepts/project/models.md) are groups of SQL statements used to create a single table or view. Prophecy simplifies data modeling by visualizing the data model as a series of steps, each represented by a [Gem](/docs/concepts/project/gems.md). Gems are functional units that perform tasks such as reading, transforming, writing, or handling other data operations. -Each Gem represents a SQL statement, and allows users to construct that statement by configuring a visual interface. Prophecy is smart about whether to construct a CTE or subquery for each Gem; users just configure the visual interface, and Prophecy includes the Gem's SQL statement as part of the Model. Here is a nice [overview](/docs/concepts/project/gems.md) of all the aspects of the Gem user interface. The table below outlines each Gem category: +Each Gem corresponds to a SQL statement, which users can construct through an intuitive visual interface. Prophecy handles the underlying complexity by deciding whether each Gem should generate a CTE or a subquery. Users simply configure the Gem's interface, and Prophecy integrates the resulting SQL into the larger data model. + +The table below outlines the different SQL Gem categories.
diff --git a/docs/SQL/gems/joins.md b/docs/SQL/gems/joins.md index a567b1d74a..cd012546ac 100644 --- a/docs/SQL/gems/joins.md +++ b/docs/SQL/gems/joins.md @@ -1,5 +1,5 @@ --- -title: Joins +title: Join id: data-joins description: Join data from multiple tables sidebar_position: 3 @@ -10,7 +10,9 @@ tags: - transformation --- -Upon opening the join Gem, you can see a pop-up which provides several helpful features. +

SQL Gem

+ +Upon opening the Join Gem, you can see a pop-up which provides several helpful features. ![Join definition](img/JoinCondition.png) diff --git a/docs/SQL/gems/subgraph/subgraph.md b/docs/SQL/gems/subgraph/subgraph.md index 3647f62a2b..7c226f92ae 100644 --- a/docs/SQL/gems/subgraph/subgraph.md +++ b/docs/SQL/gems/subgraph/subgraph.md @@ -8,7 +8,9 @@ tags: - SQL --- -Subgraph allows you to take multiple distinct Gems and wrap them under a single parent Gem. Doing so can help you decompose complex logic into more manageable components and simplify the Visual view of your model. +

SQL Gem

+ +Subgraph Gems let you take multiple different Gems and wrap them under a single reusable parent Gem. In other words, they allow you to decompose complex logic into reusable components and simplify the visual view of your data model. ## Basic Subgraph diff --git a/docs/SQL/gems/transform/aggregate.md b/docs/SQL/gems/transform/aggregate.md index c5d7925c50..3f010a6717 100644 --- a/docs/SQL/gems/transform/aggregate.md +++ b/docs/SQL/gems/transform/aggregate.md @@ -11,6 +11,8 @@ tags: - transformation --- +

SQL Gem

+ Together let's deconstruct a commonly used Transformation, the Aggregate Gem. Follow along in the `HelloWorld_SQL` Project. ## Using the Gem diff --git a/docs/SQL/gems/transform/deduplicate.md b/docs/SQL/gems/transform/deduplicate.md index c213748e2f..57b3912edf 100644 --- a/docs/SQL/gems/transform/deduplicate.md +++ b/docs/SQL/gems/transform/deduplicate.md @@ -10,6 +10,8 @@ tags: - unique --- +

SQL Gem

+ Removes rows with duplicate values of specified columns. ## Parameters diff --git a/docs/SQL/gems/transform/flattenschema.md b/docs/SQL/gems/transform/flattenschema.md index d6dd06b9c5..15d54b49dc 100644 --- a/docs/SQL/gems/transform/flattenschema.md +++ b/docs/SQL/gems/transform/flattenschema.md @@ -10,6 +10,8 @@ tags: - flatten --- +

SQL Gem

+ When processing raw data it can be useful to flatten complex data types like `Struct`s and `Array`s into simpler, flatter schemas. This allows you to preserve all schemas, and not just the first one. You can use FlattenSchema with Snowflake Models. ![The FlattenSchema gem](./img/flatten_gem.png) diff --git a/docs/Spark/gems/custom/delta-table-operations.md b/docs/Spark/gems/custom/delta-table-operations.md index 34d559a497..ee7763c954 100644 --- a/docs/Spark/gems/custom/delta-table-operations.md +++ b/docs/Spark/gems/custom/delta-table-operations.md @@ -9,6 +9,8 @@ tags: - devops --- +

Spark Gem

+ Helps perform the following operations on Delta tables. 1. Register table in catalog diff --git a/docs/Spark/gems/custom/file-operation.md b/docs/Spark/gems/custom/file-operation.md index a6b701f312..788de03799 100644 --- a/docs/Spark/gems/custom/file-operation.md +++ b/docs/Spark/gems/custom/file-operation.md @@ -8,6 +8,8 @@ tags: - dbfs --- +

Spark Gem

+ Helps perform file operations like `copy` and `move` on different file systems. ## Parameters diff --git a/docs/Spark/gems/custom/rest-api-enrich.md b/docs/Spark/gems/custom/rest-api-enrich.md index 7ed37da094..636ed09cd6 100644 --- a/docs/Spark/gems/custom/rest-api-enrich.md +++ b/docs/Spark/gems/custom/rest-api-enrich.md @@ -10,6 +10,8 @@ tags: - rest --- +

Spark Gem

+ Enriches the DataFrame by adding column(s) with content from REST API output based on the given configuration. ### Parameters diff --git a/docs/Spark/gems/custom/script.md b/docs/Spark/gems/custom/script.md index 0b10d8352f..0adffb4c0b 100644 --- a/docs/Spark/gems/custom/script.md +++ b/docs/Spark/gems/custom/script.md @@ -8,7 +8,9 @@ tags: - code --- -Provides a `SparkSession` and allows you to run custom code +

Spark Gem

+ +Provides a `SparkSession` and allows you to run custom code. ## Parameters diff --git a/docs/Spark/gems/custom/sql-statement.md b/docs/Spark/gems/custom/sql-statement.md index 4998e31ff6..5f50f7807a 100644 --- a/docs/Spark/gems/custom/sql-statement.md +++ b/docs/Spark/gems/custom/sql-statement.md @@ -9,6 +9,8 @@ tags: - custom --- +

Spark Gem

+ Create one or more DataFrame(s) based on provided SQL queries to run against one or more input DataFrames. ### Parameters diff --git a/docs/Spark/gems/gems.md b/docs/Spark/gems/gems.md index c1eb6b36e1..4efb3e58a3 100644 --- a/docs/Spark/gems/gems.md +++ b/docs/Spark/gems/gems.md @@ -1,11 +1,15 @@ --- -title: Standard Gems -id: gems -description: Prophecy standard gems -tags: [] +title: Spark Gems +id: spark-gems +description: Prophecy Spark Gems +tags: + - spark + - gem --- -Gem is a unit of functionality ranging from reading, transforming, writing and various other ad-hoc operations on data +[Gems](/docs/concepts/project/gems.md) are functional units in a [Pipeline](/docs/concepts/project/pipelines.md) that perform tasks such as reading, transforming, writing, or handling other data operations. + +The table below outlines the different Spark Gem categories.
diff --git a/docs/Spark/gems/join-split/compare-columns.md b/docs/Spark/gems/join-split/compare-columns.md index 186934bd71..221e1b908f 100644 --- a/docs/Spark/gems/join-split/compare-columns.md +++ b/docs/Spark/gems/join-split/compare-columns.md @@ -10,6 +10,8 @@ tags: - compare-columns --- +

Spark Gem

+ The CompareColumns Gem lets you compare columns between two DataFrames based on the key id columns defined. ## Parameters diff --git a/docs/Spark/gems/join-split/join-split.md b/docs/Spark/gems/join-split/join-split.md index 3d805ce119..bfd5ce0a45 100644 --- a/docs/Spark/gems/join-split/join-split.md +++ b/docs/Spark/gems/join-split/join-split.md @@ -5,7 +5,7 @@ description: The set of Gems that can split or join one or more DataFrames tags: [] --- -Includes the set of Gems that help with the process of merging or splitting DataFrame(s) to create new DataFrame(s). +Includes the set of Gems that help with the process of merging or splitting DataFrames to create new DataFrames. ```mdx-code-block import DocCardList from '@theme/DocCardList'; diff --git a/docs/Spark/gems/join-split/join.md b/docs/Spark/gems/join-split/join.md index c29c69afba..0653984318 100644 --- a/docs/Spark/gems/join-split/join.md +++ b/docs/Spark/gems/join-split/join.md @@ -14,6 +14,8 @@ tags: - merge --- +

Spark Gem

+ Joins 2 or more DataFrames based on the given configuration. ## Parameters diff --git a/docs/Spark/gems/join-split/repartition.md b/docs/Spark/gems/join-split/repartition.md index fd7e161c33..c84960a22e 100644 --- a/docs/Spark/gems/join-split/repartition.md +++ b/docs/Spark/gems/join-split/repartition.md @@ -11,6 +11,8 @@ tags: - coalesce --- +

Spark Gem

+ This will repartition or coalesce the input DataFrame based on the specified configuration. There are four different repartitioning options: ## Hash Repartitoning diff --git a/docs/Spark/gems/join-split/row-distributor.md b/docs/Spark/gems/join-split/row-distributor.md index a0272adde2..82bb81d5cd 100644 --- a/docs/Spark/gems/join-split/row-distributor.md +++ b/docs/Spark/gems/join-split/row-distributor.md @@ -10,6 +10,8 @@ tags: - row distributor --- +

Spark Gem

+ Use the RowDistributor Gem to create multiple DataFrames based on provided filter conditions from an input DataFrame. This is useful for cases where rows from the input DataFrame needs to be distributed into multiple DataFrames in different ways for downstream Gems. diff --git a/docs/Spark/gems/machine-learning/ml-openai.md b/docs/Spark/gems/machine-learning/ml-openai.md index 1b10e541ff..a6e6a363f1 100644 --- a/docs/Spark/gems/machine-learning/ml-openai.md +++ b/docs/Spark/gems/machine-learning/ml-openai.md @@ -16,13 +16,7 @@ tags: ] --- -
-
- -
- - -
+

Spark Gem

The OpenAI Gem allows the Prophecy user to interact with the OpenAI API using two different requests: @@ -35,6 +29,16 @@ Follow along to learn how to interact with the OpenAI API using Prophecy's easy- As with all applications that interface with Large Language Models (LLMs), the OpenAI Gem can generate results that are incorrect and/or misleading. The OpenAI Gem is subject to the same [limitations and risks](https://platform.openai.com/docs/guides/embeddings/limitations-risks) as those posed by OpenAI itself. ::: +
+ +
+
+ +
+ + +
+ ### 1. Compute text embeddings Given a question input, the OpenAI Gem will return a text embedding by calling the OpenAI [ada-002 model](https://platform.openai.com/docs/guides/embeddings/how-to-get-embeddings). View the input and output from this Gem to understand the data formats and sample. diff --git a/docs/Spark/gems/machine-learning/ml-pinecone-lookup.md b/docs/Spark/gems/machine-learning/ml-pinecone-lookup.md index 2b5f521fa2..f172971a7f 100644 --- a/docs/Spark/gems/machine-learning/ml-pinecone-lookup.md +++ b/docs/Spark/gems/machine-learning/ml-pinecone-lookup.md @@ -6,13 +6,7 @@ description: Lookup a vector embedding from a Pinecone Database tags: [generative-ai, machine-learning, llm, pinecone, openai] --- -
-
- -
- - -
+

Spark Gem

The PineconeLookup Gem identifies content that is similar to a provided vector embedding. The Gem calls the Pinecone API and returns a set of IDs with highest similarity to the provided embedding. @@ -26,6 +20,14 @@ The PineconeLookup Gem identifies content that is similar to a provided vector e Now let’s understand the Gem Parameters, Input, and Output in detail. +
+
+ +
+ + +
+ ### Gem Parameters ![Parameters](./img/pinecone_lookup_configure.png) diff --git a/docs/Spark/gems/machine-learning/ml-text-processing.md b/docs/Spark/gems/machine-learning/ml-text-processing.md index 5bccf79fbe..59a3a1a77e 100644 --- a/docs/Spark/gems/machine-learning/ml-text-processing.md +++ b/docs/Spark/gems/machine-learning/ml-text-processing.md @@ -15,13 +15,7 @@ tags: ] --- -
-
- -
- - -
+

Spark Gem

The TextProcessing Gem enables text data preparation for machine learning in two different ways: @@ -30,6 +24,14 @@ The TextProcessing Gem enables text data preparation for machine learning in two Follow along to see how to use the TextProcessing Gem. For an example set of Pipelines that uses this Gem to create a Generative AI Chatbot, see this [guide.](https://docs.prophecy.io/getting-started/gen-ai-chatbot) +
+
+ +
+ + +
+ ### 1. Load web URLs and Extract Text Given a column with web URLs, the `Load web URLs` operation will scrape the content from each URL, and output the content as a binary format or as a human readable text format, depending on the operation type selected. The figure below shows the `Load web URL and Extract Text` operation. diff --git a/docs/Spark/gems/source-target/advanced/lookup.md b/docs/Spark/gems/source-target/advanced/lookup.md index 17406bbd82..77ecec4d3d 100644 --- a/docs/Spark/gems/source-target/advanced/lookup.md +++ b/docs/Spark/gems/source-target/advanced/lookup.md @@ -8,6 +8,8 @@ tags: - lookup --- +

Spark Gem

+ Lookups are a special kind of Gem that allow you to mark a particular DataFrame as a _Broadcast_ DataFrame. Spark will ensure that this data is available on every computation node so that these lookups can be done without shuffling data. This is useful for looking up values in tables, hence the name. ![Lookup Gem](../../img/lookup.png) diff --git a/docs/Spark/gems/source-target/advanced/synthetic-data-generator/synthetic-data-generator.md b/docs/Spark/gems/source-target/advanced/synthetic-data-generator/synthetic-data-generator.md index ea136919ec..763532d218 100644 --- a/docs/Spark/gems/source-target/advanced/synthetic-data-generator/synthetic-data-generator.md +++ b/docs/Spark/gems/source-target/advanced/synthetic-data-generator/synthetic-data-generator.md @@ -11,6 +11,8 @@ tags: - generator --- +

Spark Gem

+ Generate synthetic data with this special kind of Source Gem. Generating mock data is crucial when building data Pipelines to simulate real-world scenarios for testing, validating, and optimizing Pipeline performance before using actual production data. It helps ensure the Pipeline handles various data formats, structures, and edge cases effectively, minimizing potential issues in a live environment. diff --git a/docs/Spark/gems/subgraph/basicSubgraph.md b/docs/Spark/gems/subgraph/basicSubgraph.md index 09b1768c6e..172d7d5ebe 100644 --- a/docs/Spark/gems/subgraph/basicSubgraph.md +++ b/docs/Spark/gems/subgraph/basicSubgraph.md @@ -9,6 +9,8 @@ tags: - group --- +

Spark Gem

+ In a **Basic Subgraph**, you have the capability to encapsulate multiple distinct Gems within a single, reusable parent Gem. This organizational approach enhances the visual clarity of your Pipeline by grouping various sections together under a common parent Gem. Additionally, it empowers you to break down intricate logic into modular components, thereby streamlining the Data Engineering processes. Furthermore, the ability to [Publish a Subgraph](/docs/package-hub/package-builder/ShareableSubgraphs.md) extends the utility beyond a singular Pipeline. By doing so, you can reuse the encapsulated logic in other Pipelines or Projects, promoting code re-usability and simplifying the overall development workflow. diff --git a/docs/Spark/gems/subgraph/subgraph.md b/docs/Spark/gems/subgraph/subgraph.md index 41db1bd9b4..c5b0391a40 100644 --- a/docs/Spark/gems/subgraph/subgraph.md +++ b/docs/Spark/gems/subgraph/subgraph.md @@ -6,7 +6,7 @@ tags: [] todo: true --- -Subgraph allows you to take multiple different Gems and wrap them under a single reusable parent Gem. It allows you to decompose complex logic into reusable components. See how to **create** and **publish** a Subgraph in the video below. +Subgraph Gems let you take multiple different Gems and wrap them under a single reusable parent Gem. In other words, they allow you to decompose complex logic into reusable components. See how to **create** and **publish** a Subgraph in the video below.
diff --git a/docs/Spark/gems/subgraph/tableIterator.md b/docs/Spark/gems/subgraph/tableIterator.md index 6ab8ab9440..d373d2bede 100644 --- a/docs/Spark/gems/subgraph/tableIterator.md +++ b/docs/Spark/gems/subgraph/tableIterator.md @@ -9,6 +9,8 @@ tags: - iterator --- +

Spark Gem

+ TableIterator allows you to iterate over one or more Gems for each row of the first input DataFrame. Let's see how to create a Basic Loop which loops over a Metadata Table, and for each row of the table will run the Gems inside the Subgraph. diff --git a/docs/Spark/gems/transform/aggregate.md b/docs/Spark/gems/transform/aggregate.md index 9aa7b4e2d5..a65b3f5833 100644 --- a/docs/Spark/gems/transform/aggregate.md +++ b/docs/Spark/gems/transform/aggregate.md @@ -11,6 +11,8 @@ tags: - count --- +

Spark Gem

+ Allows you to group the data and apply aggregation methods and pivot operation. ## Parameters diff --git a/docs/Spark/gems/transform/bulk-column-expressions.md b/docs/Spark/gems/transform/bulk-column-expressions.md index 50f96d4713..378d5f6d04 100644 --- a/docs/Spark/gems/transform/bulk-column-expressions.md +++ b/docs/Spark/gems/transform/bulk-column-expressions.md @@ -9,6 +9,8 @@ tags: - columns --- +

Spark Gem

+ The BulkColumnExpressions Gem primarily lets you cast or change the data type of multiple columns at once. It provides additional functionality, including: - Adding a prefix or suffix to selected columns. diff --git a/docs/Spark/gems/transform/bulk-column-rename.md b/docs/Spark/gems/transform/bulk-column-rename.md index 1c9c4ec698..13707241ef 100644 --- a/docs/Spark/gems/transform/bulk-column-rename.md +++ b/docs/Spark/gems/transform/bulk-column-rename.md @@ -9,6 +9,8 @@ tags: - columns --- +

Spark Gem

+ Use the BulkColumnRename Gem to rename multiple columns in your Dataset in a systematic way. ## Parameters diff --git a/docs/Spark/gems/transform/data-cleansing.md b/docs/Spark/gems/transform/data-cleansing.md index 011d5aaeeb..2ad79a7cab 100644 --- a/docs/Spark/gems/transform/data-cleansing.md +++ b/docs/Spark/gems/transform/data-cleansing.md @@ -9,6 +9,8 @@ tags: - format --- +

Spark Gem

+ Use the DataCleansing Gem to standardize data formats and address missing or null values in the data. ## Parameters diff --git a/docs/Spark/gems/transform/deduplicate.md b/docs/Spark/gems/transform/deduplicate.md index ed672e394f..3c06a35e38 100644 --- a/docs/Spark/gems/transform/deduplicate.md +++ b/docs/Spark/gems/transform/deduplicate.md @@ -10,6 +10,8 @@ tags: - unique --- +

Spark Gem

+ Removes rows with duplicate values of specified columns. ## Parameters diff --git a/docs/Spark/gems/transform/dynamic-select.md b/docs/Spark/gems/transform/dynamic-select.md index 8e62c61692..04aa158073 100644 --- a/docs/Spark/gems/transform/dynamic-select.md +++ b/docs/Spark/gems/transform/dynamic-select.md @@ -9,6 +9,8 @@ tags: - dynamic --- +

Spark Gem

+ Use the DynamicSelect Gem to dynamically filter columns of your Dataset based on a set of conditions. ## Configuration diff --git a/docs/Spark/gems/transform/filter.md b/docs/Spark/gems/transform/filter.md index 9c8c1bada1..8b1e64cec6 100644 --- a/docs/Spark/gems/transform/filter.md +++ b/docs/Spark/gems/transform/filter.md @@ -9,6 +9,8 @@ tags: - where --- +

Spark Gem

+ Filters DataFrame based on the provided filter condition ### Parameters diff --git a/docs/Spark/gems/transform/flattenschema.md b/docs/Spark/gems/transform/flattenschema.md index 94b35b2027..aa6097e82d 100644 --- a/docs/Spark/gems/transform/flattenschema.md +++ b/docs/Spark/gems/transform/flattenschema.md @@ -10,6 +10,8 @@ tags: - flatten --- +

Spark Gem

+ When processing raw data it can be useful to flatten complex data types like structures and arrays into simpler, flatter schemas. ![The FlattenSchema gem](./img/flatten_gem.png) diff --git a/docs/Spark/gems/transform/limit.md b/docs/Spark/gems/transform/limit.md index 8ba68287cd..132d7efe7b 100644 --- a/docs/Spark/gems/transform/limit.md +++ b/docs/Spark/gems/transform/limit.md @@ -8,7 +8,9 @@ tags: - limit --- -Limits the number of rows in the output +

Spark Gem

+ +Limits the number of rows in the output. ### Parameters diff --git a/docs/Spark/gems/transform/order-by.md b/docs/Spark/gems/transform/order-by.md index dceeca1a87..d6879a0488 100644 --- a/docs/Spark/gems/transform/order-by.md +++ b/docs/Spark/gems/transform/order-by.md @@ -11,6 +11,8 @@ tags: - descending --- +

Spark Gem

+ Sorts a DataFrame on one or more columns in ascending or descending order. ### Parameters diff --git a/docs/Spark/gems/transform/reformat.md b/docs/Spark/gems/transform/reformat.md index 018190208a..2c6610da8d 100644 --- a/docs/Spark/gems/transform/reformat.md +++ b/docs/Spark/gems/transform/reformat.md @@ -10,6 +10,8 @@ tags: - transform --- +

Spark Gem

+ Transforms one or more column names or values by using expressions and/or functions. It's useful when we need to extract only the required columns or make changes column-wise. ## Parameters diff --git a/docs/Spark/gems/transform/schema-transform.md b/docs/Spark/gems/transform/schema-transform.md index c7513557e7..407ad54461 100644 --- a/docs/Spark/gems/transform/schema-transform.md +++ b/docs/Spark/gems/transform/schema-transform.md @@ -10,6 +10,8 @@ tags: - schema --- +

Spark Gem

+ SchemaTransform is used to add, edit, rename or drop columns from the incoming DataFrame. :::info diff --git a/docs/Spark/gems/transform/set-operation.md b/docs/Spark/gems/transform/set-operation.md index 2fd35f54b5..205ec5226c 100644 --- a/docs/Spark/gems/transform/set-operation.md +++ b/docs/Spark/gems/transform/set-operation.md @@ -11,6 +11,8 @@ tags: - difference --- +

Spark Gem

+ Use the SetOperation Gem to perform addition or subtraction of rows from DataFrames with identical schemas and different data. ### Parameters diff --git a/docs/Spark/gems/transform/unpivot.md b/docs/Spark/gems/transform/unpivot.md index 8cf0b289c5..b21b7b2693 100644 --- a/docs/Spark/gems/transform/unpivot.md +++ b/docs/Spark/gems/transform/unpivot.md @@ -9,6 +9,8 @@ tags: - longformat --- +

Spark Gem

+ Use the Unpivot Gem to transform your data from a wide format to a long format. :::note diff --git a/docs/Spark/gems/transform/window-function.md b/docs/Spark/gems/transform/window-function.md index f7dc7672f3..636d57a896 100644 --- a/docs/Spark/gems/transform/window-function.md +++ b/docs/Spark/gems/transform/window-function.md @@ -9,6 +9,8 @@ tags: - aggregate --- +

Spark Gem

+ The WindowFunction lets you define a **WindowSpec** and apply window functions on a DataFrame. ### Parameters diff --git a/docusaurus.config.js b/docusaurus.config.js index 7f5c4a7d2e..6ae73ad2f5 100644 --- a/docusaurus.config.js +++ b/docusaurus.config.js @@ -45,6 +45,7 @@ const config = { ], stylesheets: [ "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css", + "https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/css/bootstrap.min.css", ], themeConfig: From c2d4e573a9198c24dfedb8fdb08495c0eb19bc75 Mon Sep 17 00:00:00 2001 From: Alexander Ahn Date: Tue, 10 Dec 2024 21:56:43 -0800 Subject: [PATCH 10/14] Update mar2023.md (#466) --- docs/release_notes/2023/mar2023.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/docs/release_notes/2023/mar2023.md b/docs/release_notes/2023/mar2023.md index b2648f6925..3aae0e3f1c 100644 --- a/docs/release_notes/2023/mar2023.md +++ b/docs/release_notes/2023/mar2023.md @@ -13,14 +13,6 @@ tags: - Prophecy Python libs version: 1.4.8 - Prophecy Scala libs version: 7.0.16 -### Features {#UpdatesRelease2100} - -#### Prophecy metadata available through the API - -Now Prophecy users can access their metadata using the Prophecy API. -Just create a Personal Access Token [PAT](/docs/metadata/prophecyAPI.md). -One popular use case is to access the Prophecy metadata through automated scripts and integrate this data into an existing data visualization system. Use the Prophecy API to create a single pane of glass interface for data stewards and engineers to see pipelines, datasets, Lineage, etc. Please see [here](/docs/metadata/prophecyAPI.md) for more details on this feature. - ### Improvements {#ImprovementsRelease2100} #### Search box on available clusters From 35e5432fd752012ccabc2cb86f64c97b3f49bc52 Mon Sep 17 00:00:00 2001 From: kathweinschenkprophecy Date: Wed, 11 Dec 2024 09:02:12 -0500 Subject: [PATCH 11/14] Installation guide v1 (#464) * Installation guide v1 * Add upgrade information to docs * restructure per MS review * integrate SME review comments * update service reqs * remove instances of eg --- docs/SQL/gems/joins.md | 2 +- docs/Spark/fabrics/dataproc/_category_.json | 2 +- docs/Spark/fabrics/dataproc/dataproc.md | 2 +- docs/Spark/fabrics/diagnostics.md | 2 +- .../deployment => Spark/fabrics}/emr.md | 2 +- docs/Spark/fabrics/livy.md | 4 +- docs/Spark/fabrics/synapsefabric.md | 2 +- docs/architecture/architecture.md | 84 +++++ docs/architecture/deployment/_category_.json | 4 +- docs/architecture/deployment/deployment.md | 87 +---- .../private-saas/configure-backup.md | 356 ------------------ .../deployment/private-saas/private-saas.md | 127 ------- .../{deployment => }/img/arch_databricks.png | Bin .../{deployment => }/img/arch_ide.png | Bin .../{deployment => }/img/arch_snowflake.png | Bin docs/architecture/self-hosted/_category_.json | 6 + .../authentication/_category_.json | 0 .../authentication/active-directory.md | 0 .../authentication/authentication.md | 0 .../authentication/azure-ad.md | 0 .../authentication/azuread-scim.md | 0 .../authentication/img/Admin_Settings.png | Bin .../authentication/img/Keytab.png | Bin .../authentication/img/app_assign.png | Bin .../img/azure_enterpriseapp_sso.png | Bin .../img/okta_app_embed_link.png | Bin .../authentication/img/okta_dl_cert.png | Bin .../authentication/img/okta_example.png | Bin .../img/okta_idp_metadata_xml.png | Bin .../authentication/img/okta_xml.png | Bin .../authentication/img/proxy-settings.png | Bin .../img/push_groups_by_name.png | Bin .../authentication/img/push_groups_find.png | Bin .../authentication/img/scim_provisioning.png | Bin .../authentication/img/sso_settings.png | Bin .../img/sso_settings_detailed.png | Bin .../img/sso_settings_detailed_azuread.png | Bin .../img/sso_settings_with_token.png | Bin .../authentication/saml-okta.md | 0 .../authentication/security-settings.md} | 24 +- .../configurations/_category_.json | 6 + .../configurations/configurations.md | 19 + .../configurations}/configure-alerts.md | 0 .../configurations}/configure-audit-logs.md | 2 +- .../configurations}/configure-object-store.md | 0 .../configurations}/sandbox-configuration.md | 0 .../download-logs.md | 2 +- .../generate-api-key.md | 0 .../self-hosted/img/upgrade-version.png | Bin 0 -> 75426 bytes .../installation-helm/_category_.json | 6 + .../installation-helm/_helm-values.mdx} | 101 +---- .../installation-helm/install-on-aws.mdx | 112 ++++++ .../installation-helm/installation-helm.mdx | 99 +++++ docs/architecture/self-hosted/self-hosted.md | 48 +++ .../self-hosted/upgrade-backup-restore.md | 186 +++++++++ docs/getting-started/genaichatbot.md | 4 +- .../package-builder/gem-builder.md | 4 +- docs/package-hub/package-hub.md | 8 +- docs/release_notes/2023/june2023.md | 2 +- .../2024/August_2024/August_2024.md | 2 +- docs/release_notes/2024/nov2024.md | 4 +- package-lock.json | 19 - src/css/custom.css | 8 + 63 files changed, 610 insertions(+), 726 deletions(-) rename docs/{architecture/deployment => Spark/fabrics}/emr.md (99%) delete mode 100644 docs/architecture/deployment/private-saas/configure-backup.md delete mode 100644 docs/architecture/deployment/private-saas/private-saas.md rename docs/architecture/{deployment => }/img/arch_databricks.png (100%) rename docs/architecture/{deployment => }/img/arch_ide.png (100%) rename docs/architecture/{deployment => }/img/arch_snowflake.png (100%) create mode 100644 docs/architecture/self-hosted/_category_.json rename docs/architecture/{ => self-hosted}/authentication/_category_.json (100%) rename docs/architecture/{ => self-hosted}/authentication/active-directory.md (100%) rename docs/architecture/{ => self-hosted}/authentication/authentication.md (100%) rename docs/architecture/{ => self-hosted}/authentication/azure-ad.md (100%) rename docs/architecture/{ => self-hosted}/authentication/azuread-scim.md (100%) rename docs/architecture/{ => self-hosted}/authentication/img/Admin_Settings.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/Keytab.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/app_assign.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/azure_enterpriseapp_sso.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/okta_app_embed_link.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/okta_dl_cert.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/okta_example.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/okta_idp_metadata_xml.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/okta_xml.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/proxy-settings.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/push_groups_by_name.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/push_groups_find.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/scim_provisioning.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/sso_settings.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/sso_settings_detailed.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/sso_settings_detailed_azuread.png (100%) rename docs/architecture/{ => self-hosted}/authentication/img/sso_settings_with_token.png (100%) rename docs/architecture/{ => self-hosted}/authentication/saml-okta.md (100%) rename docs/architecture/{authentication/admin-settings.md => self-hosted/authentication/security-settings.md} (66%) create mode 100644 docs/architecture/self-hosted/configurations/_category_.json create mode 100644 docs/architecture/self-hosted/configurations/configurations.md rename docs/architecture/{deployment/private-saas => self-hosted/configurations}/configure-alerts.md (100%) rename docs/architecture/{deployment/private-saas => self-hosted/configurations}/configure-audit-logs.md (96%) rename docs/architecture/{deployment/private-saas => self-hosted/configurations}/configure-object-store.md (100%) rename docs/architecture/{deployment/private-saas => self-hosted/configurations}/sandbox-configuration.md (100%) rename docs/architecture/{deployment/private-saas => self-hosted}/download-logs.md (99%) rename docs/architecture/{deployment/private-saas => self-hosted}/generate-api-key.md (100%) create mode 100644 docs/architecture/self-hosted/img/upgrade-version.png create mode 100644 docs/architecture/self-hosted/installation-helm/_category_.json rename docs/architecture/{deployment/installation-guide.md => self-hosted/installation-helm/_helm-values.mdx} (81%) create mode 100644 docs/architecture/self-hosted/installation-helm/install-on-aws.mdx create mode 100644 docs/architecture/self-hosted/installation-helm/installation-helm.mdx create mode 100644 docs/architecture/self-hosted/self-hosted.md create mode 100644 docs/architecture/self-hosted/upgrade-backup-restore.md diff --git a/docs/SQL/gems/joins.md b/docs/SQL/gems/joins.md index cd012546ac..afcfbffb71 100644 --- a/docs/SQL/gems/joins.md +++ b/docs/SQL/gems/joins.md @@ -22,7 +22,7 @@ To fill-in our **(5) Join condition** within the **(4) Conditions** section, sta When you’re writing your join conditions, you’ll see available functions and columns to speed up your development. When the autocomplete appears, press ↑, ↓ to navigate between the suggestions and press tab to accept the suggestion. -Select the **(6)Join Type** according to the provider, eg [Databricks](https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-qry-select-join.html) or [Snowflake.](https://docs.snowflake.com/en/user-guide/querying-joins) +Select the **(6)Join Type** according to the provider, e.g. [Databricks](https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-qry-select-join.html) or [Snowflake.](https://docs.snowflake.com/en/user-guide/querying-joins) The **(7) Expressions** tab allows you to define the set of output columns that are going to be returned from the Gem. Here we leave it empty, which by default passes through all the input columns, from both of the joined sources, without any modifications. diff --git a/docs/Spark/fabrics/dataproc/_category_.json b/docs/Spark/fabrics/dataproc/_category_.json index dc6d56bb7d..470bf5fdce 100644 --- a/docs/Spark/fabrics/dataproc/_category_.json +++ b/docs/Spark/fabrics/dataproc/_category_.json @@ -1,6 +1,6 @@ { "label": "Google Cloud Dataproc", - "position": 7, + "position": 8, "collapsible": true, "collapsed": true } diff --git a/docs/Spark/fabrics/dataproc/dataproc.md b/docs/Spark/fabrics/dataproc/dataproc.md index 9459dd414d..f6fbbb9dba 100644 --- a/docs/Spark/fabrics/dataproc/dataproc.md +++ b/docs/Spark/fabrics/dataproc/dataproc.md @@ -2,7 +2,7 @@ title: "Google Cloud Dataproc" id: gcp-dataproc-fabric-guide description: Configuring GCP Dataproc Fabric -sidebar_position: 7 +sidebar_position: 8 tags: - deployment - configuration diff --git a/docs/Spark/fabrics/diagnostics.md b/docs/Spark/fabrics/diagnostics.md index 0a9f739f38..fe46eaaa59 100644 --- a/docs/Spark/fabrics/diagnostics.md +++ b/docs/Spark/fabrics/diagnostics.md @@ -2,7 +2,7 @@ title: "Diagnostics" id: fabric-diagnostics description: Troubleshooting Fabrics using diagnostics -sidebar_position: 8 +sidebar_position: 9 tags: - diagnostics - fabric diff --git a/docs/architecture/deployment/emr.md b/docs/Spark/fabrics/emr.md similarity index 99% rename from docs/architecture/deployment/emr.md rename to docs/Spark/fabrics/emr.md index 6bfedf563a..49d3fd91ba 100644 --- a/docs/architecture/deployment/emr.md +++ b/docs/Spark/fabrics/emr.md @@ -2,7 +2,7 @@ title: "Amazon EMR with Apache Livy" id: EMR-livy-installation-guide description: Installation Guide -sidebar_position: 3 +sidebar_position: 6 tags: - deployment - installation diff --git a/docs/Spark/fabrics/livy.md b/docs/Spark/fabrics/livy.md index 4e0bda2cf7..7bff3462db 100644 --- a/docs/Spark/fabrics/livy.md +++ b/docs/Spark/fabrics/livy.md @@ -53,8 +53,8 @@ Prophecy supports **three** Authentication modes for Livy. - **Kerberos** - For Kerberos authentication, Prophecy authenticates with Livy via Kerberos as a service. - This is used when you have Kerebrised Hadoop set up. Prophecy will use the key tabs configured for this Livy URL by an Admin User in [Admin Settings](../../architecture/authentication/admin-settings#keytabs-for-kerberos-authentication). - To enable user-level authorization in Kerberized Hadoop, you can enable Impersonation. When **Impersonation using proxy-user** is enabled, Prophecy will use the Proxy User obtained from LDAP/AAD as per [proxy-user setting](../../architecture/authentication/admin-settings#proxy-user-settings-per-user) by an Admin user when talking to Livy. + This is used when you have Kerebrised Hadoop set up. Prophecy will use the key tabs configured for this Livy URL by an Admin User in [Security settings](docs/architecture/self-hosted/authentication/security-settings.md#keytabs-for-kerberos-authentication). + To enable user-level authorization in Kerberized Hadoop, you can enable Impersonation. When **Impersonation using proxy-user** is enabled, Prophecy will use the Proxy User obtained from LDAP/AAD as per [proxy-user setting](docs/architecture/self-hosted/authentication/security-settings.md#proxy-user-settings-per-user) by an Admin user when talking to Livy. ![kerberos](img/kerberos-settings.png) diff --git a/docs/Spark/fabrics/synapsefabric.md b/docs/Spark/fabrics/synapsefabric.md index d5d204fe75..144b3bb4c1 100644 --- a/docs/Spark/fabrics/synapsefabric.md +++ b/docs/Spark/fabrics/synapsefabric.md @@ -2,7 +2,7 @@ title: "Azure Synapse Analytics" id: azure-synapse-fabric-guide description: Configuring Synapse Fabric -sidebar_position: 6 +sidebar_position: 7 tags: - deployment - configuration diff --git a/docs/architecture/architecture.md b/docs/architecture/architecture.md index aa3d6e7bcf..9fb4452be2 100644 --- a/docs/architecture/architecture.md +++ b/docs/architecture/architecture.md @@ -14,6 +14,90 @@ built to be multi-tenant. | [Private SaaS (Customer VPC)](./deployment#private-saas-customer-vpc) | Enterprises in the Cloud | | [On-Premise](./deployment#on-premise-deployment) | Large Enterprises in the middle of cloud migration (rare cases) | +## High-Level Architecture + +There are four components of a successful Prophecy deployment: + +| Component | Description | +| ---------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **Prophecy IDE** | The development environment, including Prophecy microservices and cloud infrastructure, that is deployed. | +| **Data engine** | The [SQL](#sql) or [Spark](#spark) execution environment, like Snowflake or Databricks. This is set up by a customer and connected to Prophecy through a secure and performant interface. No customer data is stored on Prophecy’s environment. | +| **Source control** | Prophecy works similar to code-first IDEs by natively integrating with [Git](#git) and platforms like Bitbucket. An encrypted copy of customer’s code is stored within Prophecy’s environment for fast access, while the source-of-truth code is stored on Git. | +| **Identity management (optional)** | For simple user authentication and permission control, Prophecy can connect your identity provider of choice. | + +### Prophecy IDE + +A user who logs into Prophecy has access to the integrated development environment (IDE). This includes everything needed to enable all data users to transform raw data into reliable, analytics-ready data using visual data pipelines. + +![Prophecy IDE](./img/arch_ide.png) + +Teams are the primary mechanism of ownership. Teams own Projects where Pipelines, Datasets, and Jobs live. Teams also own execution fabrics that provide the execution and storage resources for execution including on SQL Warehouses and Spark clusters. + +### SQL + +To allow for SQL query execution Prophecy can connect to Snowflake and Databricks warehouses. Connectors for additional SQL warehouses will be announced as they are added. + +#### Snowflake + +To connect with data stored in a SQL Warehouse, or to allow for interactive SQL execution, Prophecy can connect to an existing Snowflake execution environment through secure and performant [Snowpark](https://docs.snowflake.com/en/developer-guide/snowpark/index) or [Snowflake](https://docs.snowflake.com/en/developer-guide/sql-api/reference) APIs. + +Each [Fabric](../../concepts/fabrics) defined in Prophecy connects to a single Snowflake Warehouse and each user is required to provide credentials to authenticate to it. + +![Arch_Diagram](./img/arch_snowflake.png) + +Notice the data provider (e.g. Snowflake) matches up to a Fabric. For another scenario, consider the same architecture diagram where the Fabric connects to a Databricks SQL warehouse instead of Snowflake. + +### Spark + +To allow for interactive code execution Prophecy can connect to either [Databricks](#databricks) or any other Spark through [Apache Livy](https://livy.apache.org/) (e.g. MapR, CDP, HDP, Spark on Kubernetes). + +#### Databricks + +![Prophecy to Databricks Connectivity](./img/arch_databricks.png) + +Prophecy connects to Databricks using [Rest API](https://docs.databricks.com/dev-tools/api/latest/index.html). Each [Fabric](../../concepts/fabrics) defined in Prophecy connects to a single [Databricks workspace](https://docs.databricks.com/workspace/index.html) and each user is required to provide a [personal access token](https://docs.databricks.com/dev-tools/api/latest/authentication.html) to authenticate to it. + +Security-conscious enterprises that use Databricks with limited network access have to additionally add the **Prophecy Data Plane IP address** (`3.133.35.237`) to the Databricks allowed [access list](https://docs.databricks.com/security/network/ip-access-list.html#add-an-ip-access-list). + +Primarily Prophecy uses Databricks for the following functionalities: + +- **Interactive Execution** - Prophecy allows its users to spin up new clusters or connect to existing clusters. When a cluster connection exists, Prophecy allows the user to run their code in the interactive mode. Interactive code queries are sent to Databricks using the [Databricks Command API 1.2](https://docs.databricks.com/dev-tools/api/1.2/index.html). +- **Scheduling** - Prophecy allows the user to build and orchestrate Databricks Jobs. This works through the [Databricks Jobs API 2.1](https://docs.databricks.com/dev-tools/api/latest/jobs.html). + +By default, Prophecy does not store any data samples when executing code using Databricks. Data samples can be optionally stored for observability purposes (execution metrics). + +:::note +When using **Active Directory**, Prophecy takes care of auto-generation and refreshing of the Databricks personal access tokens. Read more about it [here](https://docs.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/aad/). +::: + +### Git + +While all code generated by Prophecy is stored in a User’s Git repository, we temporarily store some of the generated code used during Interactive development in an encrypted cache. + +Supported Git providers: + +- **Prophecy Managed** - Prophecy automatically sets up the connectivity between itself and the repositories. Prophecy Managed is based on open-source [GitTea](https://github.com/go-gitea/gitea). +- **GitHub** (including GitHub Enterprise) - authenticates using per-user personal access tokens. [How to generate PAT?](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token) +- **Bitbucket** (including Bitbucket self-hosted) - authenticates using per-user personal access tokens. [How to generate PAT?](https://confluence.atlassian.com/bitbucketserver072/personal-access-tokens-1005335924.html) +- **GitLab** (including GitLab self-hosted) - authenticates using per-user personal access tokens. [How to generate PAT?](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html) +- **Azure DevOps** - authenticates using per-user personal access tokens. [How to generate PAT?](https://docs.microsoft.com/en-us/azure/devops/organizations/accounts/use-personal-access-tokens-to-authenticate?view=azure-devops&tabs=Windows) + +Security-conscious enterprises that use Git Providers within private networks behind firewalls have to add the Prophecy Control Plane IP address (`3.133.35.237`) to the private network allow-list or to the Git provider [allow-list](https://github.blog/2019-12-12-ip-allow-lists-now-in-public-beta/). + +## Security and Privacy Practices + +The Prophecy team employs top-notch industry practices to safeguard the security of their application and maintain the privacy of customer data. Below are just a few components of our comprehensive security strategy and system structure: + +- **General** - An annual penetration test is performed to validate Prophecy’s posture and identify vulnerabilities. Our latest penetration test report was issued in November 2022. Prophecy maintains SOC-2 compliance as audited by PrescientAssurance. + +- **Public SaaS** - Prophecy IDE is hosted on secure servers on AWS. All storage systems are encrypted, and all servers are tightly access controlled and audited. Data is encrypted in transit at all times. + +- **Private SaaS** - Alternatively, Prophecy’s IDE can be installed within an Enterprise network as desired. Prophecy’s IDE accesses your environment through a single IP address dedicated to you, allowing you to protect access to your data resources at the network level. The credentials are stored per user, and only a fully authenticated user can access their environment. + +- **On-Premise** - Prophecy complies with your security requirements on-premise; [reach out](https://www.prophecy.io/request-a-demo) to start the discussion. + +Read more details on Prophecy’s security and compliance posture at our Security Portal [here.](https://security.prophecy.io/) + ## What's next To learn more about Prophecy's architecture, see the following pages: diff --git a/docs/architecture/deployment/_category_.json b/docs/architecture/deployment/_category_.json index 57a4d1b8d4..30d2fb3e40 100644 --- a/docs/architecture/deployment/_category_.json +++ b/docs/architecture/deployment/_category_.json @@ -1,6 +1,6 @@ { - "label": "Prophecy deployment", - "position": 2, + "label": "Prophecy Deployment", + "position": 1, "collapsible": true, "collapsed": true } diff --git a/docs/architecture/deployment/deployment.md b/docs/architecture/deployment/deployment.md index ce76c8c3c9..9e26d72524 100644 --- a/docs/architecture/deployment/deployment.md +++ b/docs/architecture/deployment/deployment.md @@ -27,7 +27,7 @@ This option is heavily used by customers to try Prophecy. Our startup and midsiz ### Private SaaS (Customer VPC) -Customers in segments that deal with very sensitive data primarily use this option. Here, Prophecy runs within the **Customer VPC** and connects to the identity, Spark clusters and the scheduler within the VPC. For more information read the [private SAAS installation documentation](private-saas/private-saas.md) or reach out to our team by using [request a demo](https://www.prophecy.io/request-a-demo). +Customers in segments that deal with very sensitive data primarily use this option. Here, Prophecy runs within the **Customer VPC** and connects to the identity, Spark clusters and the scheduler within the VPC. For more information read the [private SAAS installation documentation](docs/architecture/self-hosted/self-hosted.md) or reach out to our team by using [request a demo](https://www.prophecy.io/request-a-demo). ![Customer VPC Architecture](img/arch_customervpc.png) @@ -39,91 +39,6 @@ Ready to setup Prophecy to run in your VPC? Login into your preferred cloud mark On rare occasions, Prophecy will deploy on-premise for large customers who are moving to the cloud. Often the order is that the organizations will move Pipelines from on-premise legacy ETL tools to Spark, then move it to Spark on the cloud. -## High-Level Architecture - -There are four components of a successful Prophecy deployment: - -- **Prophecy IDE** - The development environment, including Prophecy Microservices and Cloud Infrastructure, which is deployed using one of these options: [Public SaaS](#public-saas), [Private SaaS](#private-saas-customer-vpc), or (rarely) [On-Premise](#on-premise-deployment). - -- **Data Engine (eg Snowflake or Databricks)** - [SQL](#sql) or [Spark](#spark) execution environment. This is setup by a customer and connected to Prophecy through a secure and performant interface. No customer’s data is stored on Prophecy’s environment. - -- **Source Control (e.g. Bitbucket)** - Prophecy works similar to code-first IDEs, by natively integrating with [Git](#git) for best software engineering practices. An encrypted copy of customer’s code is stored within Prophecy’s environment for fast access, while the source-of-truth code is stored on Git. - -- **Identity Management (optional)** - For simple user authentication and permission control, Prophecy can connect your identity provider of choice. - -### Prophecy IDE - -A user who logs into Prophecy has access to the integrated development environment (IDE). This includes everything needed to enable all data users to transform raw data into reliable, analytics-ready data using visual data pipelines. - -![Prophecy IDE](./img/arch_ide.png) - -Teams are the primary mechanism of ownership. Teams own Projects where Pipelines, Datasets, and Jobs live. Teams also own execution fabrics that provide the execution and storage resources for execution including on SQL Warehouses and Spark clusters. - -### SQL - -To allow for SQL query execution Prophecy can connect to Snowflake and Databricks warehouses. Connectors for additional SQL warehouses will be announced as they are added. - -#### Snowflake - -To connect with data stored in a SQL Warehouse, or to allow for interactive SQL execution, Prophecy can connect to an existing Snowflake execution environment through secure and performant [Snowpark](https://docs.snowflake.com/en/developer-guide/snowpark/index) or [Snowflake](https://docs.snowflake.com/en/developer-guide/sql-api/reference) APIs. - -Each [Fabric](../../concepts/fabrics) defined in Prophecy connects to a single Snowflake Warehouse and each user is required to provide credentials to authenticate to it. - -![Arch_Diagram](./img/arch_snowflake.png) - -Notice the data provider (eg Snowflake) matches up to a Fabric. For another scenario, consider the same architecture diagram where the Fabric connects to a Databricks SQL warehouse instead of Snowflake. - -### Spark - -To allow for interactive code execution Prophecy can connect to either [Databricks](#databricks) or any other Spark through [Apache Livy](https://livy.apache.org/) (e.g. MapR, CDP, HDP, Spark on Kubernetes). - -#### Databricks - -![Prophecy to Databricks Connectivity](./img/arch_databricks.png) - -Prophecy connects to Databricks using [Rest API](https://docs.databricks.com/dev-tools/api/latest/index.html). Each [Fabric](../../concepts/fabrics) defined in Prophecy connects to a single [Databricks workspace](https://docs.databricks.com/workspace/index.html) and each user is required to provide a [personal access token](https://docs.databricks.com/dev-tools/api/latest/authentication.html) to authenticate to it. - -Security-conscious enterprises that use Databricks with limited network access have to additionally add the **Prophecy Data Plane IP address** (`3.133.35.237`) to the Databricks allowed [access list](https://docs.databricks.com/security/network/ip-access-list.html#add-an-ip-access-list). - -Primarily Prophecy uses Databricks for the following functionalities: - -- **Interactive Execution** - Prophecy allows its users to spin up new clusters or connect to existing clusters. When a cluster connection exists, Prophecy allows the user to run their code in the interactive mode. Interactive code queries are sent to Databricks using the [Databricks Command API 1.2](https://docs.databricks.com/dev-tools/api/1.2/index.html). -- **Scheduling** - Prophecy allows the user to build and orchestrate Databricks Jobs. This works through the [Databricks Jobs API 2.1](https://docs.databricks.com/dev-tools/api/latest/jobs.html). - -By default, Prophecy does not store any data samples when executing code using Databricks. Data samples can be optionally stored for observability purposes (execution metrics). - -:::note -When using **Active Directory**, Prophecy takes care of auto-generation and refreshing of the Databricks personal access tokens. Read more about it [here](https://docs.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/aad/). -::: - -### Git - -While all code generated by Prophecy is stored in a User’s Git repository, we temporarily store some of the generated code used during Interactive development in an encrypted cache. - -Supported Git providers: - -- **Prophecy Managed** - Prophecy automatically sets up the connectivity between itself and the repositories. Prophecy Managed is based on open-source [GitTea](https://github.com/go-gitea/gitea). -- **GitHub** (including GitHub Enterprise) - authenticates using per-user personal access tokens. [How to generate PAT?](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token) -- **Bitbucket** (including Bitbucket self-hosted) - authenticates using per-user personal access tokens. [How to generate PAT?](https://confluence.atlassian.com/bitbucketserver072/personal-access-tokens-1005335924.html) -- **GitLab** (including GitLab self-hosted) - authenticates using per-user personal access tokens. [How to generate PAT?](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html) -- **Azure DevOps** - authenticates using per-user personal access tokens. [How to generate PAT?](https://docs.microsoft.com/en-us/azure/devops/organizations/accounts/use-personal-access-tokens-to-authenticate?view=azure-devops&tabs=Windows) - -Security-conscious enterprises that use Git Providers within private networks behind firewalls have to add the Prophecy Control Plane IP address (`3.133.35.237`) to the private network allow-list or to the Git provider [allow-list](https://github.blog/2019-12-12-ip-allow-lists-now-in-public-beta/). - -## Security and Privacy Practices - -The Prophecy team employs top-notch industry practices to safeguard the security of their application and maintain the privacy of customer data. Below are just a few components of our comprehensive security strategy and system structure: - -- **General** - An annual penetration test is performed to validate Prophecy’s posture and identify vulnerabilities. Our latest penetration test report was issued in November 2022. Prophecy maintains SOC-2 compliance as audited by PrescientAssurance. - -- **Public SaaS** - Prophecy IDE is hosted on secure servers on AWS. All storage systems are encrypted, and all servers are tightly access controlled and audited. Data is encrypted in transit at all times. - -- **Private SaaS** - Alternatively, Prophecy’s IDE can be installed within an Enterprise network as desired. Prophecy’s IDE accesses your environment through a single IP address dedicated to you, allowing you to protect access to your data resources at the network level. The credentials are stored per user, and only a fully authenticated user can access their environment. - -- **On-Premise** - Prophecy complies with your security requirements on-premise; [reach out](https://www.prophecy.io/request-a-demo) to start the discussion. - -Read more details on Prophecy’s security and compliance posture at our Security Portal [here.](https://security.prophecy.io/) - ## What's next To continue with your Prophecy deployment, see the following pages: diff --git a/docs/architecture/deployment/private-saas/configure-backup.md b/docs/architecture/deployment/private-saas/configure-backup.md deleted file mode 100644 index 892b48913d..0000000000 --- a/docs/architecture/deployment/private-saas/configure-backup.md +++ /dev/null @@ -1,356 +0,0 @@ ---- -title: Backup/Restore Configuration -id: backup-restore -description: Prophecy installations backup-restore being synced to object stores like S3, Azure Blob Storage etc. -sidebar_position: 4 -tags: - - backup - - restore - - disaster recovery - - s3 - - azure blob - - NFS ---- - -Prophecy provides a reliable backup mechanism for safeguarding all critical data utilized by the Prophecy app. Users can consistently back up essential data through their preferred object store, such as AWS S3, Azure Blob Storage, or local storage (which could be backed up by a NFS). This systematic backup process guarantees the accessibility of data for future restoration needs. Prophecy leverages the advanced capabilities of these object stores to seamlessly synchronize the backups, ensuring a robust and efficient data protection strategy. - -:::note - -Certain [object store level configurations](./configure-object-store.md) are shared with [audit event logs configurations here](./configure-audit-logs.md). Make sure to configure the [object store level configurations](./configure-object-store.md) before proceeding below. - -::: - -## Use case - -- Prophecy, by design, does not store or own any customer data or code. Instead, it focuses on maintaining essential metadata related to various projects, user-created gems, users, and team information. -- Prophecy manages a persistence storage system, responsible for holding uncommitted changes, as well as other relevant data. -- To safeguard against potential disasters or unrecoverable failures, Prophecy offers a backup feature. This feature allows users to dump the database to remote cloud storage, ensuring the ability to restore it to a new cluster or the same cluster when necessary. -- The current backup support always performs full backups, providing comprehensive data protection. - This backup functionality has been available since the release of Prophecy version 3.1.2.0. - -:::tip - -Note this doc is constantly updated with new features/options and hence it is better to always go with the latest version of Prophecy. - -::: - -## Configuration - -There are certain environment variables that need to be configured in Athena based on the kind of backup-restore required. - -### Navigate to the Backup config UI - -To configure object store settings in the Prophecy UI, follow these steps: - -1. Log in to the Prophecy UI as an admin user. -1. Click on the `three dots` at the bottom left corner and select the `settings icon` from the submenu. -1. Navigate to the `Admin` main tab. -1. Within the Admin main tab, access the `Config` sub tab. -1. Finally, click on the `backupConfig` sub tab to configure the backup settings. - -### JSON format - -Below are JSON configurations within the Prophecy UI that need to be enabled to support this functionality. You will have to configure only the options which you require. Make sure to maintain a JSON format mentioned below while configuring the different options. - -``` -{ - "backupFrequency": "0 0 0 * * *", - "backupRetentionCount": "30", - "enableRegularBackups": false, -} -``` - -### Supported Configuration Variables - -| Configuration variable name | Description | Default value | -| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------- | -| `backupFrequency` | How frequently to purge old user events from the internal database. Defaults to daily at 00:00. Uses [6-digit CRON](https://pkg.go.dev/github.com/robfig/cron#hdr-CRON_Expression_Format) | `0 0 0 * * *` | -| `backupRetentionCount` | No. of last `N` backups to retain. | `30` | -| `enableRegularBackups` | Set to `true` to enable backups | `false` | - -## How to Backup - -There are two ways to perform backup of data. Choose either one of the below options. - -1. Set `enableRegularBackups` to `true` - To enable perodic backup of data according to CRON provided or on every athena pod restart (safety feature). -1. Trigger the backup through the backup API below. - -### Trigger Backup API - -:::note - -To trigger a backup/restore manually you would require a API key. [Follow the API Key generation document to generate the same](./generate-api-key) before proceeding below. - -::: - -#### Trigger a backup - -By default when you set the `ENABLE_REGULAR_BACKUPS` to `true`, backups are taken automatically at the configured `BACKUP_FREQUENCY` CRON schedule. However, we you would like to manually trigger a backup, you may use the API below. - -To trigger an on-demand backup, make a REST call like so: - -``` -curl --location --request POST 'https://{prophecy-url}/api/backup' \ ---header 'Cookie: prophecy-token={api-key}' \ ---header 'Content-Type: application/json' \ ---data-raw '{}' -``` - -You should get a response like: - -``` -{ - "code": 202, - "message": "Request Accepted with timestamp 2023-02-02t16-00-00" -} -``` - -The timestamp `2023-02-02t16-00-00` above would be the key used to trigger a restore in future. - -On-demand backup is an asynchronous API. There are two APIs which can be used to query the status of a current or previous backup. - -#### Get Latest Backup details - -This API returns the status current/last backup operation triggered. It doesn't expect any paramters. - -Sample API call - -``` -curl --location --request GET 'https://{prophecy-url}/api/backup/latest' \ ---header 'Cookie: prophecy-token={api-key}' \ ---header 'Content-Type: application/json' \ ---data-raw '{}' -``` - -Sample response when there is no ongoing backup. It returns the last backup operation status. - -``` -{ - "backupurl": "https://s3.console.aws.amazon.com/s3/buckets/{your-backup-bucket}?region=us-west-2&prefix=app/2023-02-02t16-00-00/&showversions=false", - "namespace": "app", - "status": "Success", - "timestamp": "2023-02-02t16-00-00" -} -``` - -Sample response when there is an ongoing backup. When all (almost) all pending DBs and pending Volumes move to completed list, then the backup completes. - -``` -{ - "status":"backup/restore operation ongoing with pending DBs: [airflow gogs exec metadata] and pending Volumes: [gitserver metagraph openidfederator] and completed DBs: [asp] and completed Volumes: [artifactory edweb]" -} -``` - -#### Get Backup Status - -This API returns the status of the backup for which timestamp is passed. It expects one paramter as `timestamp`. -If there is no timestamp passed and there is an ongoing backup, the status for the same is returned. - -Sample API call - -``` -curl --location --request GET 'https://{prophecy-url}/api/backup/status' \ ---header 'Cookie: prophecy-token={api-key}' \ ---header 'Content-Type: application/json' \ ---data-raw '{ -"timestamp": "2023-02-02t16-00-00" -}' -``` - -Sample response when the required `timestamp` of backup is passed. - -``` -{ - "backupurl": "https://s3.console.aws.amazon.com/s3/buckets/{your-backup-bucket}?region=us-west-2&prefix=app/2023-02-02t16-00-00/&showversions=false", - "namespace": "app", - "status": "Success", - "timestamp": "2023-02-02t16-00-00" -} -``` - -Sample response when there is an ongoing backup and when there is no `timestamp` passed. When all (almost) all pending DBs and pending Volumes move to completed list, then the backup completes. - -``` -{ - "status":"backup/restore operation ongoing with pending DBs: [airflow gogs exec metadata] and pending Volumes: [gitserver metagraph openidfederator] and completed DBs: [asp] and completed Volumes: [artifactory edweb]" -} -``` - -#### Get List of Available Backups - -This API returns the list of available backups. It doesn't expect any parameters. - -Sample API call - -``` -curl --location --request GET 'https://{prophecy-url}/api/backup/list' \ ---header 'Cookie: prophecy-token={api-key}' \ ---header 'Content-Type: application/json' \ ---data-raw '{}' -``` - -Sample response - -``` -{ - "backup-list": [ - { - "backupurl": "https://s3.console.aws.amazon.com/s3/buckets/{your-backup-bucket}?region=us-west-2&prefix=app/2023-02-02t16-00-00/&showversions=false", - "namespace": "app", - "status": "Success", - "timestamp": "2023-02-02t16-00-00" - }, - { - "backupurl": "https://s3.console.aws.amazon.com/s3/buckets/{your-backup-bucket}?region=us-west-2&prefix=app/2023-02-02t17-00-00/&showversions=false", - "namespace": "app", - "status": "Success", - "timestamp": "2023-02-02t17-00-00" - }, - { - "backupurl": "https://s3.console.aws.amazon.com/s3/buckets/{your-backup-bucket}?region=us-west-2&prefix=app/2023-02-02t18-00-00/&showversions=false", - "namespace": "app", - "status": "Success", - "timestamp": "2023-02-02t18-00-00" - } - ] -} -``` - -#### Delete Backup Entry - -This API attempts the delete the backup data (local and upstream) and also the metadata (database entries) associated with it. Note that in case of `enableRegularBackups` set to `true`, backups are older than `backupRetentionCount` in reverse order are garbage collected automatically. - -Sample API call - -``` -curl --location --request GET 'https://{prophecy-url}/api/backup/delete' \ ---header 'Cookie: prophecy-token={api-key}' \ ---header 'Content-Type: application/json' \ ---data-raw '{ -"timestamp": "2023-02-02t16-00-00" -}' -``` - -Sample response - -``` -{"success":true,"message":"Deleted backup entry successfully"} -``` - -## How to Restore - -Restore is an on-demand based overwrite of the whole configuration to reflect the state at which backup is taken. - -:::danger -This API should be used with extreme caution as triggering this will lead to loss of current state/data. -::: - -Note the following: - -- If backup was taken in Athena’s local Persistent Volume, it needs to be copied to Athena’s Persistent Volume in the destination cluster before the restore operation can be performed. -- Restore operation always assumes a running destination Prophecy cluster where the data and the configuration of source cluster will be restored. - -### Start a Restore - -:::note - -To trigger a backup/restore manually you would require a API key. [Follow the API Key generation document to generate the same](./generate-api-key) before proceeding below. - -::: - -The below API is used to trigger a store opertion. It expects one parameter which is the `timestamp` of a successful backup. - -``` -curl --location --request POST 'https://{prophecy-url}/api/restore' \ ---header 'Cookie: prophecy-token={api-key}' \ ---header 'Content-Type: application/json' \ ---data-raw '{ - "timestamp": "2022-11-22t10-00-00", - "sourceNamespace": "{Source cluster controlplane namespace}" -}' -``` - -Sample API call with disable of gitserver restore. You may use similar options for `artifactory` / `edweb` / `metagraph` / `openidfederator`. - -``` -curl --location --request POST 'https://{prophecy-url}/api/restore' \ ---header 'Cookie: prophecy-token={api-key}' \ ---header 'Content-Type: application/json' \ ---data-raw '{ - "timestamp": "2022-11-22t10-00-00", - "sourceNamespace": "{Source cluster controlplane namespace}", - "svcs": { - "gitserver": { - "disable": false - }, - } -}' -``` - -In the above API: - -- `timestamp` is the timestamp of the backup to use to perform the restore -- `sourceNamespace` is the namespace in which source cluster’s control plane was installed. -- `svcs`: This JSON object needs to be set only when you wish to skip restore of any particular service by setting `disabled` as `true`. Otherwise, you can skip `svcs` field entirely. - -Sample response - -``` -{ - "code": 202, - "message": "Request Accepted" -} -``` - -### Restore status - -This API returns the status of the restore operation for which timestamp is passed. It expects one paramter as `timestamp`. -If there is no timestamp passed and there is an ongoing restore, the status for the same is returned. - -Sample API call - -``` -curl --location --request GET 'https://{prophecy-url}/api/restore/status' \ ---header 'Cookie: prophecy-token={api-key}' \ ---header 'Content-Type: application/json' \ ---data-raw '{ -"timestamp": "2023-02-02t16-00-00" -}' -``` - -Sample response when the required `timestamp` of backup is passed. - -``` -{ - "backupurl": "/backup", - "namespace": "app", - "status": "error copying tar of gitserver data from backup location to data directory: exit status 1", - "timestamp": "2023-02-02t16-00-00" -} - -``` - -Sample response when there is an ongoing backup and when there is no `timestamp` passed. When all (almost) all pending DBs and pending Volumes move to completed list, then the backup completes. - -``` -{"status":"backup/restore operation ongoing with pending DBs: [airflow gogs exec metadata] and pending Volumes: [gitserver metagraph openidfederator] and completed DBs: [asp] and completed Volumes: [artifactory edweb]"} -``` - -## Backup/Restore guidelines - -1. Take backup regularly, preferably to cloud storage -2. Create a Disaster Recovery Kubernetes in a different region -3. Install Prophecy in the remote Kubernetes and keep it in standby. (i.e scale down all pods) -4. Disaster Recovery restore can be initiated from the remote region when the primary goes down -5. Once the restore is done the Disaster Recovery site is available for work to continue. - -## Migrate to different cluster - -If there is a requirement to migrate to a different Kubernetes cluster, you can leverage the backups for that: - -1. Create a new Kubernetes cluster and install Prophecy based on Prophecy installation requirements -2. Take backup of source cluster (see above) -3. Restore the backup into new cluster -4. Check that everything works as expected -5. Plan a downtime for the source cluster, initiate a fresh backup and restore it to the new cluster -6. If the new cluster needs to use the old DNS, the DNS entry of old cluster should point to new cluster's `Loadbalancer` and the `Ingress` of the new cluster needs to be changed to use the old name. Contact support if you require assistance with these steps. diff --git a/docs/architecture/deployment/private-saas/private-saas.md b/docs/architecture/deployment/private-saas/private-saas.md deleted file mode 100644 index 4fe3120e43..0000000000 --- a/docs/architecture/deployment/private-saas/private-saas.md +++ /dev/null @@ -1,127 +0,0 @@ ---- -title: Private SAAS -id: private SAAS -description: Private SAAS deployments. Bring Your Own Hardware -sidebar_position: 2 -tags: - - deployment - - private SAAS - - cdp - - hdp - - mapr ---- - -Prophecy can be deployed private SAAS for customers who require maximum security or want to run on their own private SAAS -Spark versions (e.g. CDP, HDP, MapR). As for the cloud deployments, also for private SAAS, Prophecy is deployed on a -Kubernetes cluster. With that setup, Prophecy seamlessly integrates with the rest of your infrastructure. - -Prophecy is installed through a Prophecy Kubernetes operator, which automatically takes care of the common ops tasks, -like maintenance of the service, health checks, updates, version rollback etc. - -![Customer VPC deployment](../img/arch_customervpc.png) - -## Requirements - -To install Prophecy private SAAS the following infrastructure requirements must be satisfied: - -### Cluster - -- **Kubernetes Version (minimum):** 1.21 -- **Images:** Accessible from a secure container registry available at `gcr.io`. This location can be configured to a customer's internal container registry. - -Minimum service requirements: - -| Namespace | Description | # Cores | # RAM | # Block Storage | -| ------------- | --------------------------------------------------------------------------- | -------- | ----- | --------------- | -| Control Plane | Main services (front-end, code editor, metadata, lineage, etc) | 34 Cores | 68GB | 150GB | -| Data Plane | Services serving as a bridge between Spark and Prophecy UI | 6 Cores | 10GB | 10GB | -| Platform | Backup(Twice-a-day, configurable) , Monitoring, logging services (optional) | 4 Cores | 8GB | 200GB | - -\*Platform Services can also run on File-based storage - -### Storage - -Prophecy requires: - -- **Block storage** needs to be available in the Kubernetes cluster -- Kubernetes cluster configured in either **multi-AZ** or **single-AZ** mode. For multi-AZ mode, the block storage has - to have the volume binding mode set to `waitforfirstconsumer` -- **Dynamic provisioning** of block and file storage -- Prophecy supports any storage class out of the box. - -### Role - -Prophecy requires: - -- `ClusterRole` permissions are optional for CRD installation -- If not available, the customer can create the required CRDs by deploying a single helm chart (shared on request) - -### Networking - -Prophecy requires: - -- **Ingress** - - - Prophecy supports a Prophecy-managed or customer-managed nginx ingress controller. Either Kubernetes `nginx` or official - `nginx` controllers of any version are supported. We recommend the latest stable version. - - Non-`nginx` controller configuration is possible, however, not recommended. - - Prophecy supports Istio-based ingress gateways as well. - -- **Certificates** - Wildcard certificates for path-based routing. Certificates are to be managed by the `cert-manager`, - which can be Prophecy or customer-managed. Certificates are automatically generated for every ingress resource - created. -- **External-DNS** - To successfully resolve the service host names. Prophecy supports a Prophecy-managed or - customer-managed `external-dns` or equivalent. - -### Authentication - -Prophecy supports all of the most popular **authentication providers** as per the [Authentication](../../authentication/authentication.md) documentation. - -### Spark - -For interactive development, Prophecy requires: - -- **Databricks API 1.2** - for Databricks-based Spark deployments or -- **Livy 0.7.x** - for any other Spark-deployment support (e.g. CDP, HDP, MapR, Spark on Kubernetes) - -### Airflow - -For interactive and jobs deployment to Airflow, Prophecy requires a customer-managed **Airflow deployment version 2.x.x** (latest recommended). **Astronomer**'s managed Airflow offering is supported. - -### Logging / Metrics - -- Prophecy comes with a built-in **lightweight infrastructure for monitoring** (based on Loki & Grafana) - and **logging** (based on Prometheus, Grafana and alert-manager, etc.). -- A Customer can optionally connect to it to **redirect the logs and metrics** to their own logging services. - -## Scaling - -The requirements outlined [above](#cluster) can support up to 25 concurrent developers (users actively developing -pipelines at the same time). To enable more concurrent users, Prophecy supports vertical and horizontal scaling. - -Scaling is taken care of by the **Autoscaler** component in our platform. Therefore, e.g., if the number of -users increases by 10 times, the Prophecy deployment can be scaled appropriately. - -The following are estimated recommended cluster sizes depending on the number of concurrent users: - -| Number of users | 25 | 50 | 150 | -| ------------------------- | -------- | -------- | --------- | -| CPUs | 44 vCPUs | 80 vCPUs | 230 vCPUs | -| Memory | 86 GB | 160 GB | 460 GB | -| Disk space (with backups) | 360 GB | 720 GB | 1440 GB | - -:::info -The recommended resource may vary based on the intensity of the usage of each developer. The numbers -presented above are based on the average recorded usage of Prophecy customers. -::: - -## What's next - -To continue with your private SAAS deployment, see the following pages: - -```mdx-code-block -import DocCardList from '@theme/DocCardList'; -import {useCurrentSidebarCategory} from '@docusaurus/theme-common'; - - -``` diff --git a/docs/architecture/deployment/img/arch_databricks.png b/docs/architecture/img/arch_databricks.png similarity index 100% rename from docs/architecture/deployment/img/arch_databricks.png rename to docs/architecture/img/arch_databricks.png diff --git a/docs/architecture/deployment/img/arch_ide.png b/docs/architecture/img/arch_ide.png similarity index 100% rename from docs/architecture/deployment/img/arch_ide.png rename to docs/architecture/img/arch_ide.png diff --git a/docs/architecture/deployment/img/arch_snowflake.png b/docs/architecture/img/arch_snowflake.png similarity index 100% rename from docs/architecture/deployment/img/arch_snowflake.png 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b/docs/architecture/self-hosted/authentication/security-settings.md @@ -1,35 +1,31 @@ --- sidebar_position: 1 -title: Admin Settings -description: admin settings -id: admin-settings +title: Security +description: security +id: security-settings tags: - admin - settings - keytab + - security --- -A user given Admin Permissions on the Prophecy environment would see an additional Page to access Admin Settings. -Admins can see all users, teams, fabrics, etc., in their Prophecy environment. - -:::info -This is only available for Private Saas/On-prem installations of Prophecy. -::: - -## Security - -An admin user can configure Keytab files and Proxy User settings under the Security tab of the Admin settings. +The **Security** page of the Admin settings in Prophecy lets you configure Keytab files and Proxy User settings. These connect to Kerberised Livy Setup while setting up Kerberos Auth in the [Livy Fabric](/docs/Spark/fabrics/livy.md). ![admin_settings](img/Admin_Settings.png) +:::info +This is only available for admin users on Private Saas/On-prem installations of Prophecy. +::: + ### Keytabs for Kerberos Authentication Here, the Admins can upload Keytab files for secure Authentication to Livy Fabrics. These Keytabs are stored in Prophecy's metadata storage in encrypted format. Simply click on the `Add Keytab` button and provide the Livy URL, Kerberos Principal, and Keytab File for the given Livy URL. :::info -Any changes in the Kerberos Authentication section would require a restart of the execution service for the Prophecy Installation. +Any changes in the Kerberos Authentication section would require a restart of the execution service for the Prophecy installation. ::: ![keytab](img/Keytab.png) diff --git a/docs/architecture/self-hosted/configurations/_category_.json b/docs/architecture/self-hosted/configurations/_category_.json new file mode 100644 index 0000000000..7ff2416d6b --- /dev/null +++ b/docs/architecture/self-hosted/configurations/_category_.json @@ -0,0 +1,6 @@ +{ + "label": "Configurations", + "position": 3, + "collapsible": true, + "collapsed": true +} diff --git a/docs/architecture/self-hosted/configurations/configurations.md b/docs/architecture/self-hosted/configurations/configurations.md new file mode 100644 index 0000000000..acbb116c95 --- /dev/null +++ b/docs/architecture/self-hosted/configurations/configurations.md @@ -0,0 +1,19 @@ +--- +title: Configurations +id: configurations +description: Admin settings configurations +sidebar_position: 1 +tags: + - admin + - settings + - configurations +--- + +Admin users have to ability to change certain Configs in the Admin settings of their Prophecy environment. + +```mdx-code-block +import DocCardList from '@theme/DocCardList'; +import {useCurrentSidebarCategory} from '@docusaurus/theme-common'; + + +``` diff --git a/docs/architecture/deployment/private-saas/configure-alerts.md b/docs/architecture/self-hosted/configurations/configure-alerts.md similarity index 100% rename from docs/architecture/deployment/private-saas/configure-alerts.md rename to docs/architecture/self-hosted/configurations/configure-alerts.md diff --git a/docs/architecture/deployment/private-saas/configure-audit-logs.md b/docs/architecture/self-hosted/configurations/configure-audit-logs.md similarity index 96% rename from docs/architecture/deployment/private-saas/configure-audit-logs.md rename to docs/architecture/self-hosted/configurations/configure-audit-logs.md index 651065f2af..8c648d9202 100644 --- a/docs/architecture/deployment/private-saas/configure-audit-logs.md +++ b/docs/architecture/self-hosted/configurations/configure-audit-logs.md @@ -17,7 +17,7 @@ Prophecy offers robust support for storing audit events (logs) on two of the ind :::note -Certain [object store level configurations](./configure-object-store.md) are shared with [backup restore configurations](./configure-backup.md). Make sure to configure the [object store level configurations](./configure-object-store.md) before proceeding below. +Certain [object store level configurations](./configure-object-store.md) are shared with [backup restore configurations](../upgrade-backup-restore.md). Make sure to configure the [object store level configurations](./configure-object-store.md) before proceeding below. ::: diff --git a/docs/architecture/deployment/private-saas/configure-object-store.md b/docs/architecture/self-hosted/configurations/configure-object-store.md similarity index 100% rename from docs/architecture/deployment/private-saas/configure-object-store.md rename to docs/architecture/self-hosted/configurations/configure-object-store.md diff --git a/docs/architecture/deployment/private-saas/sandbox-configuration.md b/docs/architecture/self-hosted/configurations/sandbox-configuration.md similarity index 100% rename from docs/architecture/deployment/private-saas/sandbox-configuration.md rename to docs/architecture/self-hosted/configurations/sandbox-configuration.md diff --git a/docs/architecture/deployment/private-saas/download-logs.md b/docs/architecture/self-hosted/download-logs.md similarity index 99% rename from docs/architecture/deployment/private-saas/download-logs.md rename to docs/architecture/self-hosted/download-logs.md index 287df59096..f5fba6e6ce 100644 --- a/docs/architecture/deployment/private-saas/download-logs.md +++ b/docs/architecture/self-hosted/download-logs.md @@ -2,7 +2,7 @@ title: Download logs id: download-logs description: Download Prophecy support logs -sidebar_position: 3 +sidebar_position: 5 tags: - download - support logs diff --git a/docs/architecture/deployment/private-saas/generate-api-key.md b/docs/architecture/self-hosted/generate-api-key.md similarity index 100% rename from docs/architecture/deployment/private-saas/generate-api-key.md rename to docs/architecture/self-hosted/generate-api-key.md diff --git a/docs/architecture/self-hosted/img/upgrade-version.png b/docs/architecture/self-hosted/img/upgrade-version.png new file mode 100644 index 0000000000000000000000000000000000000000..481931ad78c7b22926c93c92b9ade08050780f47 GIT binary patch literal 75426 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zL(^cmeIl!tI~@9K{&Mm0fTG^6Z?sLQjR%rQ6cwW`{p8t+Tet&T@9WGv+Deh`^%)t#aQG{~S=2dIdL0!wA+V=-T2{Jv zNm|;R`Ay|I!f|kGHR%WcF!03VGl$A6cRAWd3l=$LwHxyL1+x7K~~D#+!S=2>#XHS1jR;C7r;U install prophecy prophecy/prophecy-installer --version ---set global.customer.name= --set global.prophecy.rootUrl= -``` - -#### When the certificate is provided by the Customer and there is no internet connectivity - -``` -helm -n install prophecy prophecy/prophecy-installer --version ---set global.customer.name= --set global.prophecy.rootUrl= --set global.prophecy.wildcardCert.useExternal=true ---set global.prophecy.wildcardCert.name= --set athena.controlcenter.disabled=true ---set global.repository= --set global.prophecy.imagePullSecret= --set athena.isDarkCluster=true -``` - -#### When an external SQL database is provided - -1. Create a external SQL Database (e.g. Google SQL instance). - -2. Run the following commands by connecting to the external SQL Database. - -``` -CREATE USER sdl WITH PASSWORD ''; - -CREATE DATABASE gogs; -GRANT ALL PRIVILEGES ON DATABASE gogs TO sdl; - -CREATE DATABASE federator; -GRANT ALL PRIVILEGES ON DATABASE federator TO sdl; -``` - -3. Run the following helm command to complete the installation. - -``` -helm -n upgrade -i prophecy-installer prophecy/prophecy-installer --version 3.3.1-1 --set version=3.3.1.1 - --set global.customer.cluster={cluster-name} --set global.prophecy.rootUrl={cluster-name}-{customer-name}.dev.cloud.prophecy.io - --set global.customer.name={customer-name} --set postgres.isExternalPostgres=true --set postgres.host={googlesql-dns-name} - --set postgres.user={google-sql-user-name} --set postgres.password={google-sql-user-password} --debug -``` diff --git a/docs/architecture/self-hosted/installation-helm/install-on-aws.mdx b/docs/architecture/self-hosted/installation-helm/install-on-aws.mdx new file mode 100644 index 0000000000..290b3fed2a --- /dev/null +++ b/docs/architecture/self-hosted/installation-helm/install-on-aws.mdx @@ -0,0 +1,112 @@ +--- +title: Installation on AWS +id: install-on-aws +description: Use this guide to help when installing Prophecy on AWS. +tags: + - deployment + - installation + - aws +--- + +import TOCInline from "@theme/TOCInline"; + +If you are deploying Prophecy with the private SaaS option, you need to install Prophecy in your Virtual Private Cloud (VPC). Use this guide to help when installing Prophecy on AWS. + + + +## Get started + +In this guide, we'll walk you through how to install Prophecy on an [EKS cluster](https://aws.amazon.com/eks/) in AWS. Note that this is just one way to perform the installation. + +## Connect to your Kubernetes cluster + +Let's start by connecting to the Kubernetes cluster where you will install Prophecy. + +1. Throughout this guide, you'll need to interact with the command line interface. Make sure you have these command line tools: + + - [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) + - [kubectl](https://kubernetes.io/docs/tasks/tools/) + - [Helm](https://helm.sh/docs/intro/install/) + +2. [Configure your AWS CLI](https://docs.aws.amazon.com/cli/latest/reference/configure/) if you have not already. +3. Configure `kubectl` to connect to your cluster using the following command. + + ``` + aws eks --region update-kubeconfig --name --alias + ``` + +Now, your EKS cluster should be included in your local Kubernetes configuration. + +## Validate your Kubernetes configurations + +The Kubernetes cluster on which you will install Prophecy must meet a set of requirements. + +1. Kubernetes must be version 1.21 or later. Check your Kubernetes server version using the following command. + + ``` + kubectl version + ``` + +2. Your Kubernetes cluster must have [persistant storage with dynamic provisioning](https://kubernetes.io/docs/concepts/storage/persistent-volumes/). To make sure you have at least one [StorageClass](https://kubernetes.io/docs/concepts/storage/storage-classes/) that has a provisioner and reclaimPolicy set, run: + + ``` + kubectl get storageclass + ``` + +3. You must have permission to install [custom resources](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/), or CRDs, to your Kubernetes cluster. CRDs will be installed during installation with the Helm command. You can check if you have this permission with the following command. + + ``` + kubectl auth can-i create customresourcedefinitions --all-namespaces + ``` + + If you do not have this permission, contact your Kubernetes admin or EKS owner to grant you `cluster-admin` or equivalent permissions. Alternatively, you can deploy a single Helm chart instead that can be shared on request. + +4. You need a namespace in your Kubernetes cluster where you will install Prophecy. Create a namespace using the command: + + ``` + kubectl create namespace + ``` + + A common namespace to use is `prophecy`. To check if you namespace was created, run: + + ``` + kubectl get namespaces + ``` + +## Define network configurations + +There are certain configurations that control how Prophecy's services are exposed, secured, and accessed in a networked environment. Network configurations can be managed by Prophecy, or they can be self-managed. + +### Ingress controller + +Prophecy can install its own Nginx ingress controller to expose services externally. However, you can also use an Nginx ingress controller already installed on your Kubernetes cluster if it supports external service exposure via a LoadBalancer. You'll have to specify a ingress controller during the [Helm installation](#helm-installation) if you are not using the Prophecy ingress controller. + +### Hostname resolution + +If you use a Prophecy domain in the format `*.cloud.prophecy.io`, Prophecy handles the hostname resolution. SSL certificates are managed using [Let's Encrypt](https://letsencrypt.org/how-it-works/), and the DNS configuration is done for you. + +If you want to use your own domain: + +1. Obtain SSL certificates for your domain. +2. Install the certificates in your Kubernetes cluster. +3. Add DNS entries for the services in your domain's DNS zone. +4. Ensure the entries point to the external IP address of the Nginx ingress controller LoadBalancer. + +## Install Prophecy with Helm {#helm-installation} + +At this point, you should be ready to install Prophecy using Helm. + +1. Add the Prophecy Helm chart repository: + + ``` + helm repo add prophecy https://prophecy-chart.s3.us-west-2.amazonaws.com + ``` + +2. Run the Helm install command. Below is the most basic configuration command that only includes required Helm chart values: + + ``` + helm -n install prophecy prophecy/prophecy-installer --version + --set global.customer.name= --set global.prophecy.rootUrl= + ``` + +For a full list of Helm chart values that you can set during the installation, visit [Helm chart values](installation-helm.mdx#helm-chart-values). diff --git a/docs/architecture/self-hosted/installation-helm/installation-helm.mdx b/docs/architecture/self-hosted/installation-helm/installation-helm.mdx new file mode 100644 index 0000000000..e0501d246e --- /dev/null +++ b/docs/architecture/self-hosted/installation-helm/installation-helm.mdx @@ -0,0 +1,99 @@ +--- +title: "Installation via Helm" +id: installation-helm +description: Installation via Helm +sidebar_position: 1 +tags: + - deployment + - installation + - helm +--- + +import HelmValues from "./_helm-values.mdx"; + +One way to install Prophecy is to use [Helm](https://helm.sh/docs/intro/quickstart/). +Installation requirements and Helm chart values are outlined in this page. + +For an example step-by-step installation, visit [Installation on AWS](install-on-aws). + +## Requirements + +To install Prophecy, you need: + +- Kubernetes version 1.21 or later. +- A namespace in Kubernetes cluster where you will install Prophecy. +- Permission to install custom resources, or CRDs. Alternatively, you can deploy a single Helm chart that can be shared on request. +- Block storage in the Kubernetes cluster. +- Kubernetes cluster configured in either **multi-AZ** or **single-AZ** mode. For multi-AZ mode, the block storage has to have the volume binding mode set to `waitforfirstconsumer`. +- Persistent storage with dynamic provisioning (like `gp2`). +- [Helm](https://helm.sh/docs/intro/quickstart/) for Prophecy installation. + +Minimum service requirements: + +| Namespace | Description | # Cores | # RAM | # Block Storage | +| ------------- | --------------------------------------------------------------------------- | -------- | ----- | --------------- | +| Control Plane | Main services (front-end, code editor, metadata, lineage, etc.) | 40 Cores | 78GB | 160GB | +| Platform | Backup (twice a day, configurable), monitoring, logging services (optional) | 4 Cores | 8GB | 200GB | + +## Install Prophecy + +The `helm install` command installs Prophecy via the provided Helm chart. + +1. To retrieve the Prophecy Helm chart, run the command: + + ``` + helm repo add prophecy https://prophecy-chart.s3.us-west-2.amazonaws.com + ``` + +2. If you want to install Prophecy using all of the provided values, the simplest command to run is: + + ``` + helm -n install prophecy prophecy/prophecy-installer --version --set global.customer.name= --set global.prophecy.rootUrl= + ``` + +Read through the rest of this page to understand how to customize the installation command. + +## Helm chart values {#helm-chart-values} + +Prophecy is installed via Helm chart called the Prophecy-installer. The following are the commonly used values which can be configured for this helm chart. The Helm chart values can be broadly classified into 5 different sections: + +- **Athena:** Configurations related to the management plane. +- **Global:** Configurations for all common values between components like Athena, Postgres, and Platform. +- **Postgres:** Configurations related to Postgres or at the database level. +- **Platform:** Configurations regarding various platform components like elastic search, Prometheus, Grafana, etc. +- **Version:** The Prophecy version you want to deploy. + +
+ Complete table of Helm values + +
+ +### Common configurations + +Here are some configurations that are commonly changed from the default. + +#### Internal repository + +During the Helm installation, docker images are pulled from Prophecy's public Google Container Registry `gcr.io/prophecy-share`. If you want to use your own internal container registry, you can specify it by setting the `global.repository` value during installation. Make sure to download the images from the public GCR first. + +#### Nginx ingress controller + +By default, Prophecy will install its own managed Nginx ingress controller during the Helm installation. If you want to use your own Nginx ingress controller already installed on your Kubernetes cluster, you can do so if it supports external service exposure via a LoadBalancer. You'll have to set `global.disableIngressCreation` to true, and set `global.ingressController.class` to the appropriate name. + +#### Custom domain names + +If you are using Prophecy's domain, you don't have to worry about hostname resolution. However, if you want to use your own domain, you need to create SSL certificates, set up the correct DNS configurations, and include that information during the Helm installation. + +## Additional example Helm install commands + +- Using your own SSL certificates without internet connectivity: + + ``` + helm -n install prophecy prophecy/prophecy-installer --version --set global.customer.name= --set global.prophecy.rootUrl= --set global.prophecy.wildcardCert.useExternal=true --set global.prophecy.wildcardCert.name= --set athena.controlcenter.disabled=true --set global.repository= --set global.prophecy.imagePullSecret= --set athena.isDarkCluster=true + ``` + +- Using an external SQL database: + + ``` + helm -n upgrade -i prophecy-installer prophecy/prophecy-installer --version 3.3.1-1 --set version=3.3.1.1 --set global.customer.cluster={cluster-name} --set global.prophecy.rootUrl={cluster-name}-{customer-name}.dev.cloud.prophecy.io --set global.customer.name={customer-name} --set postgres.isExternalPostgres=true --set postgres.host={googlesql-dns-name} --set postgres.user={google-sql-user-name} --set postgres.password={google-sql-user-password} --debug + ``` diff --git a/docs/architecture/self-hosted/self-hosted.md b/docs/architecture/self-hosted/self-hosted.md new file mode 100644 index 0000000000..a75c925a0e --- /dev/null +++ b/docs/architecture/self-hosted/self-hosted.md @@ -0,0 +1,48 @@ +--- +title: Self Hosted +id: self-hosted +description: Self Hosted +tags: + - deployment + - installation + - self-hosted +--- + +Prophecy is written as a set of microservices that run on a Kubernetes cluster. Our recommended platforms to run Kubernetes are: + +- Amazon Web Services managed EKS +- Microsoft Azure managed AKS +- Google Cloud Platform managed GKE + +There are a couple of ways to install the self-managed version of Prophecy: + +- Installation via [Helm](https://helm.sh/docs/intro/quickstart/). +- Installation via Marketplaces. + +Once Prophecy is installed, you'll have to manually perform upgrades, backups, restores, etc. + +### Spark + +After installation, Prophecy requires the following for interactive development: + +- **Databricks API 1.2** for Databricks-based Spark deployments, or +- **Livy 0.7.x** for any other Spark deployment support (like CDP, HDP, MapR, Spark on Kubernetes). + +### Airflow + +For interactive and jobs deployment to Airflow, Prophecy requires a customer-managed **Airflow deployment version 2.x.x** (latest recommended). **Astronomer**'s managed Airflow offering is supported. + +### Logging / Metrics + +- Prophecy comes with a built-in **lightweight infrastructure for monitoring** (based on Loki & Grafana) + and **logging** (based on Prometheus, Grafana and alert-manager, etc.). +- You can optionally **redirect the logs and metrics** to your own logging services. + +To learn more about installation methods and management, see the following pages: + +```mdx-code-block +import DocCardList from '@theme/DocCardList'; +import {useCurrentSidebarCategory} from '@docusaurus/theme-common'; + + +``` diff --git a/docs/architecture/self-hosted/upgrade-backup-restore.md b/docs/architecture/self-hosted/upgrade-backup-restore.md new file mode 100644 index 0000000000..53098b8d95 --- /dev/null +++ b/docs/architecture/self-hosted/upgrade-backup-restore.md @@ -0,0 +1,186 @@ +--- +title: Upgrades and backups +id: upgrade-backup-restore +description: Learn how to upgrade, back up, and restore Prophecy. +sidebar_position: 4 +tags: + - upgrade + - backup + - restore + - self-managed +--- + +This page outlines different actions you may perform to maintain your Prophecy installation. + +## Upgrade + +:::note +You may want to back up Prophecy before you upgrade. +::: + +To upgrade Prophecy, you can do so in **Settings > Admin**. + +1. Navigate to the **Admin** tab of the Prophecy **Settings** page. +2. Click on **Upgrade Version**. +3. Choose the version you would like to upgrade to in the **Version** dropdown. +4. Make sure that **Disable Rollback** toggle is on. + +![Upgrade version in Admin settings](img/upgrade-version.png) + +## Backup + +Backups can be triggered manually via the API, or they can be configured to run automatically. + +### On-demand backups + +You can use the Backup API to start a backup. See [Generate API Key](./generate-api-key/) if you need an API key. + +Example: + +``` +curl --location --request POST 'https://{prophecy-url}/api/backup' \ +--header 'Cookie: prophecy-token={api-key}' \ +--header 'Content-Type: application/json' \ +--data-raw '{}' +``` + +Response: + +``` +{ +"code": 202, +"message": "Request Accepted with timestamp 2023-02-02t16-00-00" +} +``` + +### Regular automatic backups + +You can configure regular backups in Prophecy in settings. + +1. Log in to Prophecy as an admin user. +2. Click on the three dots at the bottom left corner. +3. Navigate to **Settings > Admin > Config > backupConfig**. +4. Make sure `enableRegularBackups` is set to true. +5. Edit other variables to fit your requirements. +6. Save your changes. + +Below is a list of supported variables that you can change. + +| Configuration variable name | Description | Default value | +| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------- | +| `backupFrequency` | How frequently to purge old user events from the internal database. Defaults to daily at 00:00. Uses [6-digit CRON](https://pkg.go.dev/github.com/robfig/cron#hdr-CRON_Expression_Format) | `0 0 0 * * *` | +| `backupRetentionCount` | Number of last `N` backups to retain. | `30` | +| `enableRegularBackups` | State of automated backup creation. | `false` | + +### Additional backup APIs + +Here is a list of additional APIs for backups. One sample call may look like: + +``` + curl --location --request POST 'https://{prophecy-url}/api/backup' \ + --header 'Cookie: prophecy-token={api-key}' \ + --header 'Content-Type: application/json' \ + --data-raw '{}' +``` + +| API | Description | Parameters | +| -------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------- | +| **GET** `https://{prophecy-url}/api/backup/latest` | This API returns the status current/last backup operation triggered. | None expected | +| **GET** `https://{prophecy-url}/api/backup/status` | This API returns the status of the backup with a certain timestamp. If there is no timestamp passed and there is an ongoing backup, the status for ongoing backup is returned. | `timestamp` | +| **GET** `https://{prophecy-url}/api/backup/list` | This API returns the list of available backups. | None expected | +| **GET** `https://{prophecy-url}/api/backup/delete` | This API attempts the delete the backup data (local and upstream) and also the metadata (database entries) associated with it. Note that in case of `enableRegularBackups` set to true, backups are older than `backupRetentionCount` in reverse order are garbage collected automatically. | `timestamp` | + +## Restore + +Restore is an on-demand based overwrite of the whole Prophecy configuration to reflect the state of a particular backup. +The restore operation always assumes a running destination Prophecy cluster where the data and the configuration of source cluster will be restored. + +:::note +If backup was taken in Athena's local Persistent Volume, it needs to be copied to Athena's Persistent Volume in the destination cluster before the restore operation can be performed. +::: + +### On-demand restore + +You can restore using the Restore API. See [Generate API Key](./generate-api-key/) if you need an API key. + +:::danger +This API should be used with extreme caution as triggering this will lead to loss of current state/data. +::: + +The below API is used to trigger a restore operation. It expects one parameter which is the `timestamp` of a successful backup. + +``` +curl --location --request POST 'https://{prophecy-url}/api/restore' \ +--header 'Cookie: prophecy-token={api-key}' \ +--header 'Content-Type: application/json' \ +--data-raw '{ + "timestamp": "2022-11-22t10-00-00", + "sourceNamespace": "{Source cluster controlplane namespace}" +}' +``` + +Sample API call with disable of gitserver restore. You may use similar options for `artifactory` / `edweb` / `metagraph` / `openidfederator`. + +``` +curl --location --request POST 'https://{prophecy-url}/api/restore' \ +--header 'Cookie: prophecy-token={api-key}' \ +--header 'Content-Type: application/json' \ +--data-raw '{ + "timestamp": "2022-11-22t10-00-00", + "sourceNamespace": "{Source cluster controlplane namespace}", + "svcs": { + "gitserver": { + "disable": false + }, + } +}' +``` + +In the above API: + +- `timestamp` is the timestamp of the backup to use to perform the restore +- `sourceNamespace` is the namespace in which source cluster's control plane was installed. +- `svcs`: This JSON object needs to be set only when you wish to skip restore of any particular service by setting `disabled` as `true`. Otherwise, you can skip `svcs` field entirely. + +Sample response + +``` +{ + "code": 202, + "message": "Request Accepted" +} +``` + +### Additional restore APIs + +Here is a list of additional APIs for restore. One sample call may look like: + +``` +curl --location --request POST 'https://{prophecy-url}/api/backup' +--header 'Cookie: prophecy-token={api-key}' +--header 'Content-Type: application/json' +--data-raw '{}' +``` + +| API | Description | Parameters | +| --------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------- | +| **GET** `https://{prophecy-url}/api/restore/status` | This API returns the status of the restore operation with a certain timestamp. If there is no timestamp passed and there is an ongoing restore, the status for the ongoing restore is returned. | `timestamp` | + +## Backup and restore guidelines + +1. Take backups regularly, preferably to cloud storage. +2. Create a Disaster Recovery Kubernetes in a different region. +3. Install Prophecy in the remote Kubernetes and keep it in standby. In other words, scale down all pods. +4. Disaster Recovery restore can be initiated from the remote region when the primary goes down. +5. Once the restore is done, the Disaster Recovery site is available for work to continue. + +## Migrate to different cluster + +If there is a requirement to migrate to a different Kubernetes cluster, you can leverage the backups for that: + +1. Create a new Kubernetes cluster and install Prophecy based on Prophecy installation requirements. +2. Back up the source cluster. +3. Restore the backup into new cluster. +4. Check that everything works as expected. +5. Plan a downtime for the source cluster, initiate a fresh backup and restore it to the new cluster. +6. If the new cluster needs to use the old DNS, the DNS entry of old cluster should point to new cluster's `Loadbalancer` and the `Ingress` of the new cluster needs to be changed to use the old name. Contact support if you require assistance with these steps. diff --git a/docs/getting-started/genaichatbot.md b/docs/getting-started/genaichatbot.md index f9be5b5f4b..f6ed3ab510 100644 --- a/docs/getting-started/genaichatbot.md +++ b/docs/getting-started/genaichatbot.md @@ -106,7 +106,7 @@ Find `setup_Databricks.sh` [here](https://github.com/prophecy-samples/gen-ai-cha ``` Variable definitions -Resources created, eg catalog tables, Databricks scopes, secrets +Resources created, like catalog tables, Databricks scopes, secrets ``` 6. Verify `setup_Databricks.sh` creates the needed resources using these commands described [here](https://docs.databricks.com/en/archive/dev-tools/cli/unity-catalog-cli.html) and [here](https://docs.databricks.com/en/archive/dev-tools/cli/secrets-cli.html): @@ -159,7 +159,7 @@ Continuing with the goal of ingesting and vectorizing our web content, here we h ![Web Vectorize Pipeline](img/genai_web_vectorize.png) -Once the document chunks have each been assigned a vector, these “embeddings” are stored in the Unity Catalog and to a [vector database](https://www.pinecone.io/learn/vector-database/) (eg Pinecone). We can use this Knowledge Warehouse to construct our LLM prompt in the next Pipeline. +Once the document chunks have each been assigned a vector, these “embeddings” are stored in the Unity Catalog and to a [vector database](https://www.pinecone.io/learn/vector-database/) (e.g. Pinecone). We can use this Knowledge Warehouse to construct our LLM prompt in the next Pipeline. #### 2b.i Configuring the Web Vectorize Pipeline diff --git a/docs/package-hub/package-builder/gem-builder.md b/docs/package-hub/package-builder/gem-builder.md index b7a2d08ef9..e98fa98bf7 100644 --- a/docs/package-hub/package-builder/gem-builder.md +++ b/docs/package-hub/package-builder/gem-builder.md @@ -68,7 +68,7 @@ To see the UI, click **(1) Preview**. The **(2) UI** appears basic and is ready Errors are surfaced in the GemBuilder UserInterface (UI). If there’s an error on any of the [required] methods, the UI will inform you this way: ![5](img/5-gm.png) -If there is a **(1) typo / error** in a required method, eg dialog1 instead of dialog, Prophecy will direct your attention to a particular line of code. Click the **(2) underlined code**, and notice a **(3) detailed error message**. Click **(4) Preview** to see how the Gem will look as rendered in the UI. If a value is incorrect, the Gem will not be able to be parsed, and a very clear error will appear **(5) here** as well. Click the **(6) Errors** button to see the **(7) full error message**. +If there is a **(1) typo / error** in a required method (e.g. dialog1 instead of dialog), Prophecy will direct your attention to a particular line of code. Click the **(2) underlined code**, and notice a **(3) detailed error message**. Click **(4) Preview** to see how the Gem will look as rendered in the UI. If a value is incorrect, the Gem will not be able to be parsed, and a very clear error will appear **(5) here** as well. Click the **(6) Errors** button to see the **(7) full error message**. :::caution @@ -498,7 +498,7 @@ Looking to read or write to a data format beyond the (filetypes, warehouses, and Let’s see how to use the ColumnsLayout UI element in detail: ![8](img/8-gm.png) -Open the **(1) SparkBasics** package dependency for Python or Scala. Explore and scroll to find a Gem, eg **(2) OrderBy** with the desired visual component, in this case ColumnsLayout. Find the **(3) relevant code** for the visual element and click **(4) Preview** to see how this visual element is rendered. Try it out! Click on the **(5)customer_id and email** columns, and note these columns now **(6) appear** in the Order Columns list. +Open the **(1) SparkBasics** package dependency for Python or Scala. Explore and scroll to find a Gem, e.g. **(2) OrderBy** with the desired visual component, in this case ColumnsLayout. Find the **(3) relevant code** for the visual element and click **(4) Preview** to see how this visual element is rendered. Try it out! Click on the **(5)customer_id and email** columns, and note these columns now **(6) appear** in the Order Columns list. If you like the column layout, then add the ColumnsLayout element to your custom Gem. Each time you edit the code, you can click “Preview” to test the change in the UI. diff --git a/docs/package-hub/package-hub.md b/docs/package-hub/package-hub.md index 99adb7eccd..88f842e015 100644 --- a/docs/package-hub/package-hub.md +++ b/docs/package-hub/package-hub.md @@ -25,7 +25,7 @@ Open the helloworld project or create a new project. Notice the dependencies inc ![2](img/2-ph.png) -On opening a NewPipeline, notice all the transformation dropdown Gems are from `SparkBasics` package. Drag and drop a Gem, eg Deduplicate, from SparkBasics package to use in a NewProject Pipeline. +On opening a NewPipeline, notice all the transformation dropdown Gems are from `SparkBasics` package. Drag and drop a Gem like Deduplicate from SparkBasics package to use in a NewProject Pipeline. ![3](img/3-ph.png) Already you are employing an engineering best practice: package dependencies! When we say that a project USES a package, another way to say it is “a Project has a dependency on the Package.” `NewProject` has a dependency on SparkBasics Package. But don’t stop there. You’ll want to use packages shared by your teammates! @@ -67,7 +67,7 @@ Configuring Pipelines is just one way to take advantage of the shared, reusable ![9](img/9-ph.png) -If the MarketingAnalytics team wishes to use one of the custom Gems from `Framework` in a new Pipeline, just **(1)drag-and-drop the Gem**, eg DataMasking , to the Pipeline canvas. **(2)Visually configure** the Gem and execute the Pipeline as you would for any Transformation Gem. +If the MarketingAnalytics team wishes to use one of the custom Gems from `Framework` in a new Pipeline, just **(1)drag-and-drop the Gem**, e.g. DataMasking , to the Pipeline canvas. **(2)Visually configure** the Gem and execute the Pipeline as you would for any Transformation Gem. Likewise, new Dataset Format Gems from `Framework` can be used in a new Pipeline. @@ -139,7 +139,7 @@ With Package Hub, Platform teams can build solutions once, and re-use the logic A: The share “invitation” is not sent via email. Rather, the team would be able to add that project as a dependency to their new or existing projects. **Q: I shared a dependency to a team, but they don’t see the package listed when they try to add as a dependency.** -A: Be sure the new project and dependent project are using the same language, eg Scala or Python. If the new project is a Scala project, only Scala Packages can be added as dependencies. +A: Be sure the new project and dependent project are using the same language, like Scala or Python. If the new project is a Scala project, only Scala Packages can be added as dependencies. **Q: How do I use subgraphs from a dependency?** A: Like Pipelines, Subgraphs can be used by dependent projects via [configurable variables.](https://docs.prophecy.io/package-hub/package-builder/shareable-subgraphs/#configurable-subgraphs) @@ -150,5 +150,5 @@ A: One excellent option here is to **Clone** the Project instead of use as a Pac ![18](img/18-ph.png) Just **(1)Clone** the `Framework` Project, and **(2)provide** a new name, team, Git account, and repository. -Now the MarketingAnalytics team has their own Project, `MarketingFramework`, to evolve in their own direction. Notice the Pipelines are not templates, rather the `GenericCleanup` Pipeline can be edited by adding a new Gem, eg Repartition: +Now the MarketingAnalytics team has their own Project, `MarketingFramework`, to evolve in their own direction. Notice the Pipelines are not templates, rather the `GenericCleanup` Pipeline can be edited by adding a new Gem, like Repartition: ![19](img/19-ph.png) diff --git a/docs/release_notes/2023/june2023.md b/docs/release_notes/2023/june2023.md index 7b5dfd5b14..5a962246f2 100644 --- a/docs/release_notes/2023/june2023.md +++ b/docs/release_notes/2023/june2023.md @@ -92,7 +92,7 @@ Introduced a new parameter in Pipeline Settings called "Config Package Name," al #### EMR Support (Beta) -Users may connect to Amazon EMR clusters by creating [EMR Fabrics](/docs/architecture/deployment/emr.md). +Users may connect to Amazon EMR clusters by creating [EMR Fabrics](docs/Spark/fabrics/fabrics.md). #### Databricks support enhancements diff --git a/docs/release_notes/2024/August_2024/August_2024.md b/docs/release_notes/2024/August_2024/August_2024.md index 341d5d9fa8..efeeaaa4d1 100644 --- a/docs/release_notes/2024/August_2024/August_2024.md +++ b/docs/release_notes/2024/August_2024/August_2024.md @@ -22,7 +22,7 @@ tags: - **Support for config resolution inside call_func()**: Config variables inside of the `call_function()` and `call_func()` are now supported. The new syntax is `call_function("{{catalogName}}.{{database}}.{{funcName}}", "{{firstArg}}")`. -- **Support for proxy-user impersonation in Kerberos Livy Fabric Auth**: As an admin user, you can configure multiple Keytab accounts in the [Admin Settings](../../../architecture/authentication/admin-settings.md). You can upload the Keytab files and set up proxy-user impersonation in Kerberos for secure authentication to Livy Fabrics. +- **Support for proxy-user impersonation in Kerberos Livy Fabric Auth**: As an admin user, you can configure multiple Keytab accounts in the [Security settings](docs/architecture/self-hosted/authentication/security-settings.md). You can upload the Keytab files and set up proxy-user impersonation in Kerberos for secure authentication to Livy Fabrics. #### SQL Copilot Enhancements {#SQL339} diff --git a/docs/release_notes/2024/nov2024.md b/docs/release_notes/2024/nov2024.md index 87a33ab5b3..2704be6ac5 100644 --- a/docs/release_notes/2024/nov2024.md +++ b/docs/release_notes/2024/nov2024.md @@ -24,7 +24,7 @@ Prophecy now offers a reliable solution for configuring alerts to monitor resour It enables proactive alerting when resource utilization approaches defined thresholds, ensuring timely intervention before limits are reached. Additionally, it supports the suspension of critical services in the event of resource overflows. -For more information, see [Alerts Configuration](/docs/architecture/deployment/private-saas/configure-alerts.md). +For more information, see [Alerts Configuration](docs/architecture/self-hosted/configurations/configure-alerts.md). #### Download Logs @@ -34,7 +34,7 @@ As an admin user, you can download your environment logs from Prophecy without n This reduces the delay in debugging any issues with your Prophecy services. You can share the compressed log file with your Prophecy support engineer by uploading it to Zendesk. -For more information, see [Download Logs](/docs/architecture/deployment/private-saas/download-logs.md). +For more information, see [Download Logs](docs/architecture/self-hosted/download-logs.md). #### Extended Maintenance release diff --git a/package-lock.json b/package-lock.json index 0e2c304802..aec1514ac8 100644 --- a/package-lock.json +++ b/package-lock.json @@ -4641,25 +4641,6 @@ "lodash.uniq": "^4.5.0" } }, - "node_modules/caniuse-lite": { - "version": "1.0.30001615", - "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001615.tgz", - "integrity": "sha512-1IpazM5G3r38meiae0bHRnPhz+CBQ3ZLqbQMtrg+AsTPKAXgW38JNsXkyZ+v8waCsDmPq87lmfun5Q2AGysNEQ==", - "funding": [ - { - "type": "opencollective", - "url": "https://opencollective.com/browserslist" - }, - { - "type": "tidelift", - "url": "https://tidelift.com/funding/github/npm/caniuse-lite" - }, - { - "type": "github", - "url": "https://github.com/sponsors/ai" - } - ] - }, "node_modules/ccount": { "version": "2.0.1", "resolved": "https://registry.npmjs.org/ccount/-/ccount-2.0.1.tgz", diff --git a/src/css/custom.css b/src/css/custom.css index bff1386d5d..e87df70fbd 100644 --- a/src/css/custom.css +++ b/src/css/custom.css @@ -162,3 +162,11 @@ Gems table page .gems-table table th:nth-of-type(2) { width: 150px; } + +/* Summary or details style*/ + +.alert--info { + --ifm-alert-background-color: var(--ifm-color-secondary-contrast-background); + --ifm-alert-border-color: rgb(216, 219, 230); + --ifm-code-background: inherit; +} From 9a00ac928b9102ba15e4b5c55e7097dc79b10176 Mon Sep 17 00:00:00 2001 From: kathweinschenkprophecy Date: Wed, 11 Dec 2024 11:09:19 -0500 Subject: [PATCH 12/14] Consolidate EMR docs (#468) * consolidate Amazon EMR fabric docs * delete redundant files and unused images * installation edits * link livy fabric --- docs/Spark/fabrics/emr-fabric-serverless.md | 101 -------- docs/Spark/fabrics/emr-fabric.md | 87 ------- docs/Spark/fabrics/emr.md | 68 ----- docs/Spark/fabrics/emr.mdx | 244 ++++++++++++++++++ .../Screenshot 2023-08-19 at 7.31.17 PM.png | Bin 115627 -> 0 bytes docs/Spark/fabrics/img/add-sizes.png | Bin 312848 -> 0 bytes docs/Spark/fabrics/img/arch_customervpc.png | Bin 67236 -> 0 bytes docs/Spark/fabrics/img/arch_general.png | Bin 64033 -> 0 bytes docs/Spark/fabrics/img/arch_separate_vpc.png | Bin 70066 -> 0 bytes docs/Spark/fabrics/img/attach.png | Bin 302690 -> 0 bytes docs/Spark/fabrics/img/complete-f-abric.png | Bin 97498 -> 0 bytes .../Spark/fabrics/img/configure-j-ob-size.png | Bin 75318 -> 0 bytes .../fabrics/img/connectivity-databricks.png | Bin 479282 -> 0 bytes .../fabrics/img/create-entity-emr-fabric.png | Bin 193048 -> 0 bytes docs/Spark/fabrics/img/create-entity-emr.png | Bin 193048 -> 0 bytes docs/Spark/fabrics/img/emr-aws-cred.png | Bin 114131 -> 0 bytes docs/Spark/fabrics/img/emr-cluster-select.png | Bin 233564 -> 0 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-> 0 bytes docs/Spark/fabrics/img/name-emr.png | Bin 58634 -> 0 bytes docs/Spark/fabrics/img/proclivyurl.png | Bin 99226 -> 0 bytes docs/Spark/fabrics/img/prophecy_azure.png | Bin 892029 -> 0 bytes docs/Spark/fabrics/img/rbac.png | Bin 169739 -> 0 bytes docs/Spark/fabrics/img/reader.png | Bin 146583 -> 0 bytes docs/Spark/fabrics/img/sparkversion.png | Bin 159591 -> 0 bytes docs/Spark/fabrics/img/storageiam.png | Bin 296357 -> 0 bytes .../installation-helm/install-on-aws.mdx | 2 +- docs/architecture/self-hosted/self-hosted.md | 10 +- docs/concepts/fabrics/fabrics.md | 2 +- 43 files changed, 251 insertions(+), 263 deletions(-) delete mode 100644 docs/Spark/fabrics/emr-fabric-serverless.md delete mode 100644 docs/Spark/fabrics/emr-fabric.md delete mode 100644 docs/Spark/fabrics/emr.md create mode 100644 docs/Spark/fabrics/emr.mdx delete mode 100644 docs/Spark/fabrics/img/Screenshot 2023-08-19 at 7.31.17 PM.png delete mode 100644 docs/Spark/fabrics/img/add-sizes.png delete mode 100644 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a/docs/Spark/fabrics/emr-fabric-serverless.md b/docs/Spark/fabrics/emr-fabric-serverless.md deleted file mode 100644 index 11106c4a1c..0000000000 --- a/docs/Spark/fabrics/emr-fabric-serverless.md +++ /dev/null @@ -1,101 +0,0 @@ ---- -title: "Amazon EMR Serverless" -id: EMR-serverless-fabric-configuration -description: Configuring the EMR Serverless Fabric -sidebar_position: 5 -tags: - - deployment - - fabric - - emr - - serverless ---- - -In the context of Spark execution engines, you have the flexibility to opt for Amazon EMR Serverless. This guide offers step-by-step instructions on creating a Fabric that enables seamless connectivity to the EMR Serverless environment. - -## Create Amazon EMR Serverless cluster with Apache Livy - -Navigate to Amazon EMR Serverless service and create a cluster. Under **Application bundle** select **Custom**. - -Choose appropriate applications to include in your installation. At a minimum, make sure sure **Livy** and **Spark** are included in the install. - -EMR Serverless create cluster - -### Allow network connectivity between Amazon EMR Serverless and Prophecy - -To configure the necessary network settings for seamless integration, specific modifications to the security groups of your EMR Serverless cluster are required. Follow the instructions outlined below: -If you intend to utilize Prophecy as a SaaS (Software as a Service) solution, note that the Prophecy public IP is `3.133.35.237`. Ensure that the Core security group's outbound rule allows connections to this IP address. - -1. Modify the **Primary Node** security group: - - Allow incoming connections to port `8998` from the Prophecy IP. - - This can be achieved by adding an inbound rule to the Master security group that permits incoming traffic on port `8998` from the Prophecy IP address. -2. Modify the **Core Node** security group: - - Allow outgoing connections to the Prophecy public IP over HTTPS. - - To enable this, add an outbound rule to the Core security group that allows outgoing traffic over HTTPS protocol to the Prophecy public IP. - -By implementing these adjustments to your EMR Serverless cluster's security groups, you will establish the necessary network configuration for effective integration with Prophecy. - -## Create a Fabric to connect Prophecy to EMR Serverless - -Navigate to Prophecy's UI and click on **Create Fabric**. The Fabric will establish a connection with your EMR Serverless cluster and use it as the execution engine for your Pipelines. -EMR Serverless create Fabric -
-
- -**Name** your EMR Serverless Fabric and click on **Continue**. - -
-
- -EMR Serverless Fabric name - -Choose **EMR** as your **Provider**. At this point, there is no distinction between creating an EMR and an EMR Serverless Fabric. - -EMR Serverless Provider - -Before proceeding, it is crucial to ensure that all the required settings are properly configured. If you are uncertain about the necessary configurations, we recommend reaching out to your cloud infrastructure team for additional guidance and assistance. They will be able to provide you with the specific information and support needed to ensure a successful setup. - -Enter your AWS credentials under **Access Key** and **Secret Key**. Choose the **Region** that your EMR Serverless cluster is running in. - -Click on **Fetch environments**. -EMR Serverless cred - -Select your EMR Serverless cluster that you would like to connect to under **Spark Environment**. The EMR cluster listing also shows Serverless clusters. From the list of active clusters, you can identify your EMR Serverless cluster by finding `emr-serverless-services` within the Livy URL. - -EMR Serverless select - -Most of the fields should be automatically populated after selecting a EMR Serverless cluster. - -For the Authentication type, select `AWS Sig V4`. This is the only option that will work for EMR Serverless. -EMR Serverless Authentication type - -Select your Runtime Role. The role must have enough permissions to use the selected Serverless App. -EMR Serverless Runtime Role - -Enter the S3 path that points to the location where you would like your logs and Pipeline artifacts to persist. - -EMR Serverless dependencies - -Add the Job size to your environment by clicking on **Add Job Size**. Configure your Job size and click on **Add**. -EMR Job size - -Configure the Prophecy Library: - -:::note - -The Spark Version and Scala Version are fixed. - -::: - -Select **File System** under **Scala Resolution mode** and provide the following path: - -`s3://prophecy-public-bucket/prophecy-libs` - -Select **File System** under **Python Resolution mode** and provide the following path: - -`s3://prophecy-public-bucket/python-prophecy-libs` - -EMR Serverless Library - -Click on **Complete** and your EMR Serverless Fabric is ready! - -Run a simple Pipeline and make sure that the interim returns data properly. diff --git a/docs/Spark/fabrics/emr-fabric.md b/docs/Spark/fabrics/emr-fabric.md deleted file mode 100644 index 23e8278dcb..0000000000 --- a/docs/Spark/fabrics/emr-fabric.md +++ /dev/null @@ -1,87 +0,0 @@ ---- -title: "Amazon EMR" -id: EMR-fabric-configuration -description: Configuring the EMR Fabric -sidebar_position: 4 -tags: - - deployment - - fabric - - emr ---- - -In the context of Spark execution engines, you have the flexibility to opt for Amazon EMR. This guide offers step-by-step instructions on creating a Fabric that enables seamless connectivity to the EMR environment. - -## Create Amazon EMR cluster with Apache Livy - -Navigate to Amazon EMR service and create a cluster. Under **Application bundle** select **Custom**. - -Choose appropriate applications to include in your installation. At a minimum, make sure sure **Livy** and **Spark** are included in the install. - -EMR create cluster - -### Allow network connectivity between Amazon EMR and Prophecy - -To configure the necessary network settings for seamless integration, specific modifications to the security groups of your EMR cluster are required. Follow the instructions outlined below: -If you intend to utilize Prophecy as a SaaS (Software as a Service) solution, note that the Prophecy public IP is `3.133.35.237`. Ensure that the Core security group's outbound rule allows connections to this IP address. - -1. Modify the **Primary Node** security group: - - Allow incoming connections to port `8998` from the Prophecy IP. - - This can be achieved by adding an inbound rule to the Master security group that permits incoming traffic on port `8998` from the Prophecy IP address. -2. Modify the **Core Node** security group: - - Allow outgoing connections to the Prophecy public IP over HTTPS. - - To enable this, add an outbound rule to the Core security group that allows outgoing traffic over HTTPS protocol to the Prophecy public IP. - -By implementing these adjustments to your EMR cluster's security groups, you will establish the necessary network configuration for effective integration with Prophecy. - -## Create a Fabric to connect Prophecy to EMR - -Navigate to Prophecy's UI and click on **Create Fabric**. The Fabric will establish a connection with your EMR cluster and use it as the execution engine for your Pipelines. -EMR create cluster -
-
- -**Name** your EMR Fabric and click on **Continue**. - -
-
- -EMR create Fabric - -Choose **EMR** as your **Provider**. - -EMR create Fabric - -Before proceeding, it is crucial to ensure that all the required settings are properly configured. If you are uncertain about the necessary configurations, we recommend reaching out to your cloud infrastructure team for additional guidance and assistance. They will be able to provide you with the specific information and support needed to ensure a successful setup. - -Enter your AWS credentials under **Access Key** and **Secret Key**. Choose the **Region** that your EMR cluster is running in. - -Click on **Fetch environments**. -EMR cred - -Select your EMR cluster that you would like to connect to under **Spark Environment**. - -EMR select - -Most of the fields should be automatically populated after selecting a EMR cluster. Enter the S3 path that points to the location where you would like your logs to persist. - -EMR dependencies - -Add the Job size to your environment by clicking on **Add Job Size**. Configure your Job size and click on **Add**. -EMR Job size - -Configure the Prophecy Library: - -Select **File System** under **Scala Resolution mode** and provide the following path: - -`s3://prophecy-public-bucket/prophecy-libs` - -Select **File System** under **Python Resolution mode** and provide the following path: - -`s3://prophecy-public-bucket/python-prophecy-libs` - -EMR Job size - -Click on **Complete** and your EMR Fabric is ready! -EMR create cluster - -Run a simple Pipeline and make sure that the interim returns data properly. diff --git a/docs/Spark/fabrics/emr.md b/docs/Spark/fabrics/emr.md deleted file mode 100644 index 49d3fd91ba..0000000000 --- a/docs/Spark/fabrics/emr.md +++ /dev/null @@ -1,68 +0,0 @@ ---- -title: "Amazon EMR with Apache Livy" -id: EMR-livy-installation-guide -description: Installation Guide -sidebar_position: 6 -tags: - - deployment - - installation - - emr - - livy ---- - -In the context of Spark execution engines, users have the flexibility to opt for Amazon EMR while utilizing Prophecy. This comprehensive documentation aims to provide users with a clear understanding of the installation process for Amazon EMR and Apache Livy. Additionally, it offers step-by-step guidance on creating a Fabric that enables seamless connectivity to the EMR environment via Livy. - -Feel free to explore the following sections to gain insights into the integration of Amazon EMR and Apache Livy with Prophecy. - -## Create Amazon EMR cluster with Apache Livy - -:::caution Warning -Livy does not work with Python version 3.8 and above. -::: - -Navigate to Amazon EMR service and create a cluster. Under **Application bundle** select **Custom**. - -Choose appropriate applications to include in your installation. At a minimum, make sure sure **Livy** and **Spark** are included in the install. - -EMR create cluster - -### Configure network connectivity between Livy and Prophecy - -To configure the necessary network settings for seamless integration, specific modifications to the security groups of your EMR cluster are required. Follow the instructions outlined below: -If you intend to utilize Prophecy as a SaaS (Software as a Service) solution, note that the Prophecy public IP is `3.133.35.237`. Ensure that the Core security group's outbound rule allows connections to this IP address. - -1. Modify the **Primary Node** security group: - - Allow incoming connections to port `8998` from the Prophecy IP. - - This can be achieved by adding an inbound rule to the Master security group that permits incoming traffic on port `8998` from the Prophecy IP address. -2. Modify the **Core Node** security group: - - Allow outgoing connections to the Prophecy public IP over HTTPS. - - To enable this, add an outbound rule to the Core security group that allows outgoing traffic over HTTPS protocol to the Prophecy public IP. - -By implementing these adjustments to your EMR cluster's security groups, you will establish the necessary network configuration for effective integration with Prophecy. - -## Create a Fabric to connect Prophecy to Livy - -Navigate to Prophecy's UI and click on **Create Fabric**. The Fabric will establish a connection with your EMR cluster and utilize it as the execution engine for your Pipelines. - -EMR create cluster - -Choose **Livy** as your **Provider**. - -EMR create cluster - -Before proceeding, it is crucial to ensure that all the required settings are properly configured. If you are uncertain about the necessary configurations, we recommend reaching out to your cloud infrastructure team for additional guidance and assistance. They will be able to provide you with the specific information and support needed to ensure a successful setup. - -You may find your **Livy URL** under the **Applications** tab on your EMR Cluster page. -EMR create cluster - -Copy/paste your Livy URL under **Spark Connection** and choose the appropriate **Spark Version** and _Scala Version_ You may find the Spark version from your EMR console. Refer to [Spark documentation](https://spark.apache.org/docs) to find the Scala version in relation to your Spark version. -EMR create cluster - -Configure appropriate **Prophecy Library** based on your Spark and Scala version. and click on **Complete**. - -EMR create cluster - -Your Fabric for Amazon EMR is configured! Try creating a cluster using the Fabric that you've just created and attach to it. -EMR create cluster - -Run a simple Pipeline and make sure that the interim returns data properly. diff --git a/docs/Spark/fabrics/emr.mdx b/docs/Spark/fabrics/emr.mdx new file mode 100644 index 0000000000..3b8a5f6594 --- /dev/null +++ b/docs/Spark/fabrics/emr.mdx @@ -0,0 +1,244 @@ +--- +title: Amazon EMR +id: EMR-livy-installation-guide +description: This page outlines how to use Amazon EMR via Livy as your Spark execution engine in Prophecy. +sidebar_position: 6 +tags: + - deployment + - installation + - emr + - livy + - fabric + - serverless +--- + +import Tabs from "@theme/Tabs"; +import TabItem from "@theme/TabItem"; + +This page outlines how to use Amazon EMR via Livy as your Spark execution engine in Prophecy. + +These instructions work for both **Amazon EMR** and **Amazon EMR Serverless**. + +## Create Amazon EMR cluster with Apache Livy + +In your Amazon EMR service, create a cluster. When doing so: + +1. Under **Application bundle** select **Custom**. +2. When selecting applications, make sure **Livy** and **Spark** are included in the install. + +EMR create cluster + +## Configure network settings + +To make sure that EMR can communicate with Prophecy, you need to configure specific network settings. Specifically, you need to modify the security groups of your EMR cluster. + +1. Modify the **Primary Node** security group to allow incoming connections to port `8998` from the Prophecy IP. You can do so by adding an inbound rule to the Master security group that permits incoming traffic on port `8998` from the Prophecy IP address. +2. Modify the **Core Node** security group to allow outgoing connections to the Prophecy public IP `3.133.35.237` over HTTPS. Do this by adding an outbound rule to the Core security group that allows outgoing traffic over HTTPS protocol to the Prophecy public IP. + +## Create a Fabric + +To connect EMR and Prophecy, you must create a Fabric. You can either create an EMR Fabric (suggested), or a [Livy Fabric](livy.md). + + + +

To create an EMR Fabric:

+
    +
  1. + Open Prophecy and click Create Entity from the left + navigation menu. Then, click on the Fabric tile. +
  2. +
  3. + Name your Fabric and click Continue. +
  4. +
  5. + Keep the Provider Type as Spark, and choose{" "} + EMR as the Provider. +
  6. +
    + EMR Provider +
  7. + Enter your AWS credentials under Access Key and{" "} + Secret Key. Then, enter the region that your EMR + cluster is running in. +
  8. +
  9. + Click on Fetch environments. +
  10. +
  11. + Under Spark Environment, select the EMR cluster that + you would like to connect to. +
  12. +
  13. + Enter the S3 path that points to the location where you would like your + logs to persist. +
  14. +
    + EMR dependencies +
  15. + Add the Job size to your environment by clicking on{" "} + Add Job Size. Configure your Job size and click on{" "} + Add. +
  16. +
  17. + Select File System under Scala Resolution mode and + input s3://prophecy-public-bucket/prophecy-libs +
  18. +
  19. + Select File System under Python Resolution mode and + input s3://prophecy-public-bucket/python-prophecy-libs +
  20. +
+

+ Click Complete to save your new EMR Fabric. +

+
+ +

To create an EMR Fabric for your serverless cluster:

+
    +
  1. + Open Prophecy and click Create Entity from the left + navigation menu. Then, click on the Fabric tile. +
  2. +
  3. + Name your Fabric and click Continue. +
  4. +
  5. + Keep the Provider Type as Spark, and choose{" "} + EMR as the Provider. +
  6. +
    + EMR Provider +
  7. + Enter your AWS credentials under Access Key and{" "} + Secret Key. Then, enter the region that your EMR + cluster is running in. +
  8. +
  9. + Click on Fetch environments. +
  10. +
  11. + Under Spark Environment, select the EMR serverless + cluster that you would like to connect to. From the list of active + clusters, you can identify your EMR Serverless cluster by finding{" "} + emr-serverless-services within the Livy URL. This will + automatically populate some of the following fields. +
  12. +
    + EMR Serverless select +
  13. + For the Authentication type, select{" "} + AWS Sig V4. This is the only option that will work for EMR + Serverless. +
  14. +
  15. + Select your Runtime Role. The role must have enough + permissions to use the selected Serverless App. +
  16. +
  17. + Enter the S3 path that points to the location where you would like your + logs and Pipeline artifacts to persist. +
  18. +
  19. + Add the Job size to your environment by clicking on{" "} + Add Job Size. Configure your Job size and click on{" "} + Add. +
  20. +
  21. Note that the Spark and Scala versions are fixed.
  22. +
  23. + Select File System under Scala Resolution mode and input{" "} + s3://prophecy-public-bucket/prophecy-libs +
  24. +
  25. + Select File System under Python Resolution mode and input{" "} + s3://prophecy-public-bucket/python-prophecy-libs +
  26. +
+

+ Click Complete to save your new EMR Fabric. +

+
+ +

+ + Note: Livy does not work with Python version 3.8 and above. + +
+
+ To create a Livy Fabric: +

+
    +
  1. + Open Prophecy and click Create Entity from the left + navigation menu. Then, click on the Fabric tile. +
  2. +
  3. + Name your Fabric and click Continue. +
  4. +
  5. + Keep the Provider Type as Spark, and choose{" "} + Livy as the Provider. +
  6. +
    + Livy provider +
  7. + Paste your Livy URL. You can find your Livy URL under the{" "} + Applications tab on your EMR cluster page. +
  8. +
  9. Choose your Authentication type.
  10. +
  11. + Click Test Connection. +
  12. +
+

+ Once your connection is tested, you'll have to configure a few more + settings: +

+
+

+ Click Complete to save your new Livy Fabric. +

+ + + +At this point, you can test your Fabric. 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    +
  1. + Fill out the Spark and Scala version based on the values found in your + EMR console. Refer to{" "} + Spark documentation to find + the Scala version in relation to your Spark version. +
  2. +
  3. + For the Prophecy Library configurations, choose the File System + Resolution mode and provide the S3 folder names for dependencies. +
  4. +
    + EMR create cluster +

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zazJbAK%B>rl~qN`nW^p2^4RM$K+(Hc{3n}7WDJ{*CFLXHalp}=_VI0;?=-A~B>mG2 zNsm&ecE@f)Ev@&owyc_A9*izn7oWhH)=u(QK=bJ6F{$whMe^(n0b_8zi^idozM$CM z9HuUxIwoIoN2p4SK6nG^<+XPuqU6W?^&UQ_tkXS$D65&><$ifZ>fNCIA^iXy#kr^u zZ_RBU{I-awZ0koYBn=B8X|Zn%8Qrf(+V9L@7Q{@?Z>9$heK(G zM+;H(yLI2!1U$Eale7=7^6IrOp0i1$u$N$QAbdGIw5K>tG#KF*FB5ab_W+Zm)I0Kw zhuf4r0Z%6ns>2CX121X?)`pcD^Bk}hf_sNMdYc8n Eg: `udf((value:Int)=>value*value)` | True | -| UDF initialization code | Code block that contains initialization of entities used by UDFs. This could for example contain any static mapping that a UDF might use | False | +| Parameter | Description | Required | +| :---------------------- | :------------------------------------------------------------------------------------------------------------------------------------------ | :------- | +| Function name | The name of the function as it appears in your project. | True | +| UDF Name | Name of the UDF that will register it. All calls to the UDF will use this name. | True | +| Definition | Definition of the UDF function.
Eg: `udf((value:Int)=>value*value)` | True | +| UDF initialization code | Code block that contains initialization of entities used by UDFs. This could, for example, contain any static mapping that a UDF might use. | False | -### Examples +## Steps ---- +There are a few steps to take to create and use a new UDF. -#### Defining and Using UDF - -```mdx-code-block -import App from '@site/src/components/slider'; - -export const ImageData = [ - { - "image":"/img/udf/1.png", - "description":