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Board Review: Azure Synapse Data Plane Track2 SDK For Python #952
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@johanste - can you do a first review? |
Based on the champion scenarios, I think you can boil it down to two:
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Updated |
I have created an API view listing (sorry, internal only - we are working on making the tool available publicly) |
@johanste I just updated it with the latest from Azure/azure-rest-api-specs#9534 FYI |
Thank you @lmazuel |
Thank you for starting the process for approval of the client library for your Azure service. Thorough review of your client library ensures that your APIs are consistent with the guidelines and the consumers of your client library have a consistently good experience when using Azure.
** Before submitting, ensure you adjust the title of the issue appropriately **
To ensure consistency, all Tier-1 languages (C#, TypeScript, Java, Python) will generally be reviewed together. In expansive libraries, we will pair dynamic languages (Python, TypeScript) together, and strongly typed languages (C#, Java) together in separate meetings.
The Basics
About this client library
Artifacts required (per language)
We use an API review tool (apiview) to support .NET and Java API reviews. For Python and TypeScript, use the API extractor tool, then submit the output as a Draft PR to the relevant repository (azure-sdk-for-python or azure-sdk-for-js).
Python
Champion Scenarios
A champion scenario is a use case that the consumer of the client library is commonly expected to perform. Champion scenarios are used to ensure the developer experience is exemplary for the common cases. You need to show the entire code sample (including error handling, as an example) for the champion scenarios.
Champion Scenario 1: Create/Submit a spark batch job. Developer wants to run a Spark job Once the job is complete, they need to see the results
Champion Scenario 2: Administrator needs to maintain the pool of workers; for this they need to be able to list the jobs in queue, including finding which one is currently running and potentially cancel or delete them from the list.
Champion Scenario 2.1: Get a spark batch job
Champion Scenario 2.2: List all spark batch jobs under the specific spark pool
Champion Scenario 2.3: Cancel a spark batch job
Agenda for the review
A board review is generally split into two parts, with additional meetings as required
Part 1 - Introducing the board to the service:
After part 1, you may schedule additional meetings with architects to refine the API and work on implementation.
Part 2 - the "GA" meeting
Thank you for your submission
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