feat: support dataproc federated bigquery catalog

[tchow-zlai]

Summary

• https://app.asana.com/0/1208949807589885/1208951092959581
• this PR is the followup to enabling the spark-bigquery-connector. It introduces the minimal fallback where a dataproc cluster is configured with a federated endpoint, as described in https://docs.google.com/document/d/1bxNUeaVrWDRL07fyLWiBDwO6pLS9BUbPbMLSnbRDRCk/edit?tab=t.0#heading=h.917ddfitv46i . From this perspective, a bigquery catalog looks like a hive metastore. We can access it through typical catalog api's, and dataproc will proxy at the API service layer. Minimal code change!
• We do need a slight change to workaround a bug in the latest version of the connector though.

Checklist

• Added Unit Tests
• Covered by existing CI
• Integration tested
• Documentation update

Summary by CodeRabbit

• New Features

  • Introduced an online execution context for specific join operations.
  • Added a new test case for dynamic partitioning in Spark.

• Bug Fixes

  • Adjusted Spark session configuration for partition overwrite mode to ensure future compatibility.

• Chores

  • Removed unnecessary JAR file dependency from Spark job submission, simplifying the process.
  • Updated JAR URI configuration to a new storage location.

[coderabbitai]

Walkthrough

The changes in this pull request involve modifications to four main files: training_set.py, DataprocSubmitter.scala, SparkSessionBuilder.scala, and dataproc-submitter-conf.yaml. The training_set.py file sees adjustments in the configuration of join instances, particularly with the introduction of an online execution context for one of the joins. The DataprocSubmitter.scala file removes a commented-out JAR file URI for BigQuery from the job submission process, simplifying the configuration. The SparkSessionBuilder.scala file updates a Spark configuration setting to ensure compatibility with future updates. Additionally, the dataproc-submitter-conf.yaml file changes the JAR URI to a new storage location.

Changes

File Path	Change Summary
api/py/test/sample/joins/quickstart/training_set.py	Adjusted formatting of table parameter in EventSource; modified v2 Join to include online=True and limited GroupBy entities to purchases_v1 and returns_v1.
cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/DataprocSubmitter.scala	Removed commented-out JAR file URI for BigQuery from the submit method, simplifying job submission configuration.
spark/src/main/scala/ai/chronon/spark/SparkSessionBuilder.scala	Updated spark.sql.sources.partitionOverwriteMode from "dynamic" to "DYNAMIC" for compatibility with future updates.
cloud_gcp/src/main/resources/dataproc-submitter-conf.yaml	Changed jarUri configuration from a temporary bucket to a new storage location under gs://zipline-jars/.
spark/src/test/scala/ai/chronon/spark/test/JoinTest.scala	Added TestRow case class and an implicit Ordering for it; introduced a new test case for dynamic partition overwrite functionality.

Poem

In the fields where data plays,
Joins and submits in wondrous ways.
With Spark we dance, configurations bright,
Online we gather, in the soft moonlight.
Hops of code, a joyful spree,
Changes made, oh, let it be! 🐇✨

---

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[chewy-zlai]

A couple of small nits

[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (1)

spark/src/test/scala/ai/chronon/spark/test/JoinTest.scala (1)

72-109: Enhance test robustness and cleanup.

While the test case correctly verifies the dynamic partition overwrite functionality, consider the following improvements:

1. Add cleanup of test data to prevent test pollution
2. Replace generic assertions with more descriptive ones
3. Remove or guard debug show() statements with a flag

Consider applying these changes:

  test("testing basic spark dynamic partition overwrite") {
    import org.apache.spark.sql.SaveMode
    import spark.implicits._

+   // Clean up test table if exists
+   spark.sql(f"DROP TABLE IF EXISTS ${namespace}.table")

    val rows = List(
      TestRow("1", "a"),
      TestRow("2", "b"),
      TestRow("3", "c"),
      TestRow("4", "d"),
      TestRow("5", "e")
    )
    val data = spark.createDataFrame(rows) toDF ("ds", "value")
    data.write.mode(SaveMode.Overwrite).format("hive").partitionBy("ds").saveAsTable(f"${namespace}.table")
-   assertEquals(spark.table(f"${namespace}.table").as[TestRow].collect().toList.sorted, rows.sorted)
+   assertResult(rows.sorted, "Initial data write failed")(
+     spark.table(f"${namespace}.table").as[TestRow].collect().toList.sorted
+   )

-   spark.table(f"${namespace}.table").show(truncate = false)
+   if (debugEnabled) {
+     spark.table(f"${namespace}.table").show(truncate = false)
+   }

    val dynamicPartitions = List(
      TestRow("4", "y"),
      TestRow("5", "z")
    )
    val dynamicPartitionsDF = spark.createDataset(dynamicPartitions).select("value", "ds")

    dynamicPartitionsDF.write
      .format("hive")
      .mode(SaveMode.Overwrite)
      .insertInto(f"${namespace}.table")

-   spark.table(f"${namespace}.table").show(truncate = false)
+   if (debugEnabled) {
+     spark.table(f"${namespace}.table").show(truncate = false)
+   }

    val updatedExpected =
      (rows.map((r) => r.ds -> r.value).toMap ++ dynamicPartitions.map((r) => r.ds -> r.value).toMap).map {
        case (k, v) => TestRow(k, v)
      }.toList

-   assertEquals(updatedExpected.sorted, spark.table(f"${namespace}.table").as[TestRow].collect().toList.sorted)
+   assertResult(updatedExpected.sorted, "Dynamic partition overwrite failed")(
+     spark.table(f"${namespace}.table").as[TestRow].collect().toList.sorted
+   )

+   // Clean up test data
+   spark.sql(f"DROP TABLE IF EXISTS ${namespace}.table")
  }

📜 Review details

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📥 Commits

Reviewing files that changed from the base of the PR and between 8ac2096 and 6361df1.

📒 Files selected for processing (1)

• spark/src/test/scala/ai/chronon/spark/test/JoinTest.scala (2 hunks)

🔇 Additional comments (1)

spark/src/test/scala/ai/chronon/spark/test/JoinTest.scala (1)

46-55: LGTM! Well-structured case class and companion object.

The implementation is clean and follows Scala best practices. The implicit ordering is correctly implemented for sorting TestRow instances based on the ds field.

[tchow-zlai]

ptal @chewy-zlai | @piyush-zlai