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# NOTE: This Dockerfile is ONLY used to run certain tasks in CI. It is not used to run Kibana or as a distributable. | ||
# If you're looking for the Kibana Docker image distributable, please see: src/dev/build/tasks/os_packages/docker_generator/templates/dockerfile.template.ts | ||
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ARG NODE_VERSION=10.21.0 | ||
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FROM node:${NODE_VERSION} AS base | ||
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RUN apt-get update && \ | ||
apt-get -y install xvfb gconf-service libasound2 libatk1.0-0 libc6 libcairo2 libcups2 \ | ||
libdbus-1-3 libexpat1 libfontconfig1 libgcc1 libgconf-2-4 libgdk-pixbuf2.0-0 libglib2.0-0 \ | ||
libgtk-3-0 libnspr4 libpango-1.0-0 libpangocairo-1.0-0 libstdc++6 libx11-6 libx11-xcb1 libxcb1 \ | ||
libxcomposite1 libxcursor1 libxdamage1 libxext6 libxfixes3 libxi6 libxrandr2 libxrender1 libxss1 \ | ||
libxtst6 ca-certificates fonts-liberation libappindicator1 libnss3 lsb-release xdg-utils wget openjdk-8-jre && \ | ||
rm -rf /var/lib/apt/lists/* | ||
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RUN curl -sSL https://dl.google.com/linux/linux_signing_key.pub | apt-key add - \ | ||
&& sh -c 'echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google.list' \ | ||
&& apt-get update \ | ||
&& apt-get install -y rsync jq bsdtar google-chrome-stable \ | ||
--no-install-recommends \ | ||
&& rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* | ||
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RUN LATEST_VAULT_RELEASE=$(curl -s https://api.github.com/repos/hashicorp/vault/tags | jq --raw-output .[0].name[1:]) \ | ||
&& curl -L https://releases.hashicorp.com/vault/${LATEST_VAULT_RELEASE}/vault_${LATEST_VAULT_RELEASE}_linux_amd64.zip -o vault.zip \ | ||
&& unzip vault.zip \ | ||
&& rm vault.zip \ | ||
&& chmod +x vault \ | ||
&& mv vault /usr/local/bin/vault | ||
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RUN groupadd -r kibana && useradd -r -g kibana kibana && mkdir /home/kibana && chown kibana:kibana /home/kibana | ||
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COPY ./bash_standard_lib.sh /usr/local/bin/bash_standard_lib.sh | ||
RUN chmod +x /usr/local/bin/bash_standard_lib.sh | ||
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COPY ./runbld /usr/local/bin/runbld | ||
RUN chmod +x /usr/local/bin/runbld | ||
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USER kibana |
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[role="xpack"] | ||
[[machine-learning-integration]] | ||
=== integration | ||
=== Machine learning integration | ||
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++++ | ||
<titleabbrev>Integrate with machine learning</titleabbrev> | ||
++++ | ||
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The Machine Learning integration initiates a new job predefined to calculate anomaly scores on APM transaction durations. | ||
Jobs can be created per transaction type, and are based on the service's average response time. | ||
The Machine learning integration initiates a new job predefined to calculate anomaly scores on APM transaction durations. | ||
With this integration, you can quickly pinpoint anomalous transactions and see the health of | ||
any upstream and downstream services. | ||
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After a machine learning job is created, results are shown in two places: | ||
Machine learning jobs are created per environment, and are based on a service's average response time. | ||
Because jobs are created at the environment level, | ||
you can add new services to your existing environments without the need for additional machine learning jobs. | ||
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The transaction duration graph will show the expected bounds and add an annotation when the anomaly score is 75 or above. | ||
After a machine learning job is created, results are shown in two places: | ||
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* The transaction duration chart will show the expected bounds and add an annotation when the anomaly score is 75 or above. | ||
+ | ||
[role="screenshot"] | ||
image::apm/images/apm-ml-integration.png[Example view of anomaly scores on response times in the APM app] | ||
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Service maps will display a color-coded anomaly indicator based on the detected anomaly score. | ||
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* Service maps will display a color-coded anomaly indicator based on the detected anomaly score. | ||
+ | ||
[role="screenshot"] | ||
image::apm/images/apm-service-map-anomaly.png[Example view of anomaly scores on service maps in the APM app] | ||
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[float] | ||
[[create-ml-integration]] | ||
=== Create a new machine learning job | ||
=== Enable anomaly detection | ||
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To enable machine learning anomaly detection: | ||
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. From the Services overview, Traces overview, or Service Map tab, | ||
select **Anomaly detection**. | ||
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. Click **Create ML Job**. | ||
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To enable machine learning anomaly detection, first choose a service to monitor. | ||
Then, select **Integrations** > **Enable ML anomaly detection** and click **Create job**. | ||
. Machine learning jobs are created at the environment level. | ||
Select all of the service environments that you want to enable anomaly detection in. | ||
Anomalies will surface for all services and transaction types within the selected environments. | ||
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. Click **Create Jobs**. | ||
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That's it! After a few minutes, the job will begin calculating results; | ||
it might take additional time for results to appear on your graph. | ||
Jobs can be managed in *Machine Learning jobs management*. | ||
it might take additional time for results to appear on your service maps. | ||
Existing jobs can be managed in *Machine Learning jobs management*. | ||
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APM specific anomaly detection wizards are also available for certain Agents. | ||
See the machine learning {ml-docs}/ootb-ml-jobs-apm.html[APM anomaly detection configurations] for more information. | ||
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[float] | ||
[[warning-ml-integration]] | ||
=== Anomaly detection warning | ||
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To make machine learning as easy as possible to set up, | ||
the APM app will warn you when filtered to an environment without a machine learning job. | ||
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[role="screenshot"] | ||
image::apm/images/apm-anomaly-alert.png[Example view of anomaly alert in the APM app] |
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