feat: support create table in BigQuery #263

tchow-zlai · 2025-01-23T03:43:57Z

Summary

https://app.asana.com/0/1208949807589885/1209206040434612/f
Support explicit bigquery table creation.

Checklist

Added Unit Tests
Covered by existing CI
Integration tested
Documentation update

To see the specific tasks where the Asana app for GitHub is being used, see below:
- https://app.asana.com/0/0/1209206040434612

Summary by CodeRabbit

New Features
- Enhanced BigQuery table creation functionality with improved schema and partitioning support.
- Streamlined table creation process in Spark's TableUtils.
Refactor
- Simplified table existence checking logic.
- Consolidated import statements for better readability.
- Removed unused import in BigQuery catalog test.
- Updated import statement in GcpFormatProviderTest for better integration with Spark BigQuery connector.
Bug Fixes
- Improved error handling for table creation scenarios.

coderabbitai · 2025-01-23T03:44:06Z

Walkthrough

This pull request enhances the BigQuery table creation functionality across multiple files. The BigQueryFormat class now implements a robust createTable method that supports schema conversion, partitioning, and table creation in BigQuery. Simultaneously, the TableUtils class streamlines its table creation logic by simplifying the existence check and creation process. A minor test file modification removes an unused import from the BigQuery catalog test.

Changes

File	Change Summary
`cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala`	Implemented `createTable` method with schema conversion, partitioning logic, and BigQuery table creation support
`cloud_gcp/src/test/scala/ai/chronon/integrations/cloud_gcp/BigQueryCatalogTest.scala`	Removed `GoogleCloudStorageFileSystem` import
`spark/src/main/scala/ai/chronon/spark/TableUtils.scala`	Simplified table existence check and creation logic
`cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/GcpFormatProvider.scala`	Streamlined import statements and refined optional value handling
`cloud_gcp/src/test/scala/ai/chronon/integrations/cloud_gcp/GcpFormatProviderTest.scala`	Modified import statement for BigQuery library

Possibly related PRs

feat: introduce BigQueryFormat #146: Introduces BigQueryFormat class
feat: support Format specific createTable #261: Adds createTable method to BigQueryFormat
for bigquery table spark jobs, add transient to unblock serialization issues and do not create table when attempting to insert #174: Modifies TableUtils createTable method

Suggested reviewers

nikhil-zlai
piyush-zlai
david-zlai

Poem

🚀 In clouds of data, tables take flight,
BigQuery's schema, now burning bright,
Partitions dance, imports take wing,
Code transforms with each new string!
Scala's magic makes data sing! 🌟

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cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala

tchow-zlai · 2025-01-23T22:01:08Z

cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala

 import ai.chronon.spark.format.Format
-import com.google.cloud.bigquery.BigQuery
 import com.google.cloud.bigquery.connector.common.BigQueryUtil
+import com.google.cloud.spark.bigquery.SchemaConverters


had to pull in all the shaded stuff from the bigquery connector in order to leverage some of the utils.

Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]>

Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]> Co-authored-by: Thomas Chow <[email protected]>

coderabbitai

Actionable comments posted: 0

🧹 Nitpick comments (1)

cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala (1)

59-59: Inner function usage.
This call is straightforward. Keep it if you see reusability; otherwise consider inlining.

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Plan: Pro (Legacy)

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Reviewing files that changed from the base of the PR and between 378e1eb and b3d765b.

📒 Files selected for processing (3)

cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala (2 hunks)
cloud_gcp/src/test/scala/ai/chronon/integrations/cloud_gcp/BigQueryCatalogTest.scala (0 hunks)
spark/src/main/scala/ai/chronon/spark/TableUtils.scala (1 hunks)

💤 Files with no reviewable changes (1)

cloud_gcp/src/test/scala/ai/chronon/integrations/cloud_gcp/BigQueryCatalogTest.scala

⏰ Context from checks skipped due to timeout of 90000ms (6)

GitHub Check: table_utils_delta_format_spark_tests
GitHub Check: other_spark_tests
GitHub Check: join_spark_tests
GitHub Check: fetcher_spark_tests
GitHub Check: mutation_spark_tests
GitHub Check: scala_compile_fmt_fix

🔇 Additional comments (3)

cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala (2)

5-10: Imports appear correct.

30-57: Handle multi-col partitions with a typed exception.
If multiple partition columns are detected, consider throwing a more descriptive error rather than using assert to avoid abrupt termination.

Validate tableId parse.
Ensure shadedTableId.getProject or .getDataset isn't empty if the user doesn't specify them.

spark/src/main/scala/ai/chronon/spark/TableUtils.scala (1)

288-295: Race condition possible.
If another process creates the table after tableReachable but before the createTable call, a conflict might occur.

coderabbitai

Actionable comments posted: 0

🧹 Nitpick comments (1)

cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala (1)

5-10: Consider documenting repackaged imports usage.

Document why repackaged BigQuery classes are used to help future maintainers.

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📒 Files selected for processing (1)

cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala (2 hunks)

🧰 Additional context used

📓 Learnings (1)

cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala (2)

Learnt from: tchow-zlai
PR: zipline-ai/chronon#263
File: cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala:29-60
Timestamp: 2025-01-24T23:55:30.256Z
Learning: In BigQuery integration, table existence check is performed outside the BigQueryFormat.createTable method, at a higher level in TableUtils.createTable.

Learnt from: tchow-zlai
PR: zipline-ai/chronon#263
File: cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala:56-57
Timestamp: 2025-01-24T23:55:40.650Z
Learning: For BigQuery table creation operations in BigQueryFormat.scala, allow exceptions to propagate directly without wrapping them in try-catch blocks, as the original BigQuery exceptions provide sufficient context.

⏰ Context from checks skipped due to timeout of 90000ms (6)

GitHub Check: join_spark_tests
GitHub Check: fetcher_spark_tests
GitHub Check: other_spark_tests
GitHub Check: mutation_spark_tests
GitHub Check: table_utils_delta_format_spark_tests
GitHub Check: scala_compile_fmt_fix

🔇 Additional comments (2)

cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/BigQueryFormat.scala (2)

38-39: Consider customizing schema conversion.

Default schema conversion config might not handle all edge cases.

29-59: Implementation looks solid.

Clean implementation with proper partition handling and schema conversion.

## Summary - With #263 we control table creation ourselves. We don't need to rely on indirect writes to then do the table creation (and partitioning) for us, we just simply use the storage API to write directly into the table we created. This should be much more performant and preferred over indirect writes because we don't need to stage data, then load as a temp BQ table, and it uses the BigQuery storage API directly. - Remove configs that are used only for indirect writes ## Checklist - [ ] Added Unit Tests - [ ] Covered by existing CI - [ ] Integration tested - [ ] Documentation update  ## Summary by CodeRabbit ## Release Notes - **Improvements** - Enhanced BigQuery data writing process with more precise configuration options. - Simplified table creation and partition insertion logic. - Improved handling of DataFrame column arrangements during data operations. - **Changes** - Updated BigQuery write method to use a direct writing approach. - Introduced a new option to prevent table creation if it does not exist. - Modified table creation process to be more format-aware. - Streamlined partition insertion mechanism. These updates improve data management and writing efficiency in cloud data processing workflows.   --------- Co-authored-by: Thomas Chow <[email protected]>

## Summary - https://app.asana.com/0/1208949807589885/1209206040434612/f - Support explicit bigquery table creation. ## Checklist - [ ] Added Unit Tests - [ ] Covered by existing CI - [ ] Integration tested - [ ] Documentation update --- - To see the specific tasks where the Asana app for GitHub is being used, see below: - https://app.asana.com/0/0/1209206040434612  ## Summary by CodeRabbit - **New Features** - Enhanced BigQuery table creation functionality with improved schema and partitioning support. - Streamlined table creation process in Spark's TableUtils. - **Refactor** - Simplified table existence checking logic. - Consolidated import statements for better readability. - Removed unused import in BigQuery catalog test. - Updated import statement in GcpFormatProviderTest for better integration with Spark BigQuery connector. - **Bug Fixes** - Improved error handling for table creation scenarios.   --------- Co-authored-by: Thomas Chow <[email protected]>