feat: Support GB backfill #1014
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WalkthroughA new case branch for handling the Changes
Sequence Diagram(s)sequenceDiagram
participant Runner as BatchNodeRunner
participant Conf as Config
participant GroupBy as GroupBy
Runner->>Conf: getSetField
alt GROUP_BY_BACKFILL
Runner->>GroupBy: computeBackfill(groupByConf, endPartition, tableUtils, startPartitionOverride)
else Other node types
Runner->>...: Existing logic
end
Estimated code review effort🎯 2 (Simple) | ⏱️ ~7 minutes Possibly related PRs
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spark/src/main/scala/ai/chronon/spark/batch/BatchNodeRunner.scala(2 hunks)
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🧠 Learnings (2)
📓 Common learnings
Learnt from: nikhil-zlai
PR: zipline-ai/chronon#50
File: spark/src/main/scala/ai/chronon/spark/stats/drift/SummaryUploader.scala:19-47
Timestamp: 2024-11-03T14:51:40.825Z
Learning: In Scala, the `grouped` method on collections returns an iterator, allowing for efficient batch processing without accumulating all records in memory.
spark/src/main/scala/ai/chronon/spark/batch/BatchNodeRunner.scala (4)
Learnt from: nikhil-zlai
PR: #50
File: spark/src/main/scala/ai/chronon/spark/stats/drift/SummaryUploader.scala:19-47
Timestamp: 2024-11-03T14:51:40.825Z
Learning: In Scala, the grouped method on collections returns an iterator, allowing for efficient batch processing without accumulating all records in memory.
Learnt from: chewy-zlai
PR: #62
File: spark/src/main/scala/ai/chronon/spark/stats/drift/SummaryUploader.scala:9-10
Timestamp: 2024-11-06T21:54:56.160Z
Learning: In Spark applications, when defining serializable classes, passing an implicit ExecutionContext parameter can cause serialization issues. In such cases, it's acceptable to use scala.concurrent.ExecutionContext.Implicits.global.
Learnt from: nikhil-zlai
PR: #70
File: service/src/main/java/ai/chronon/service/ApiProvider.java:6-6
Timestamp: 2024-12-03T04:04:33.809Z
Learning: The import scala.util.ScalaVersionSpecificCollectionsConverter in service/src/main/java/ai/chronon/service/ApiProvider.java is correct and should not be flagged in future reviews.
Learnt from: tchow-zlai
PR: #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.
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- GitHub Check: scala_compile_fmt_fix
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🔇 Additional comments (1)
spark/src/main/scala/ai/chronon/spark/batch/BatchNodeRunner.scala (1)
13-13: LGTM!Clean import addition following existing patterns.
| case NodeContent._Fields.GROUP_BY_BACKFILL => | ||
| GroupBy.computeBackfill( | ||
| conf.getGroupByBackfill.groupBy, | ||
| range.end, | ||
| tableUtils, | ||
| overrideStartPartition = Option(range.start) | ||
| ) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add logging and validation for consistency.
Other case branches include logging and validation. Consider adding:
case NodeContent._Fields.GROUP_BY_BACKFILL =>
+ require(conf.getGroupByBackfill.isSetGroupBy, "GroupByBackfillNode must have a groupBy set")
+ logger.info(s"Running groupBy backfill for '${metadata.name}' for range: [${range.start}, ${range.end}]")
GroupBy.computeBackfill(
conf.getGroupByBackfill.groupBy,
range.end,
tableUtils,
overrideStartPartition = Option(range.start)
)
+ logger.info(s"Successfully completed groupBy backfill for '${metadata.name}'")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| case NodeContent._Fields.GROUP_BY_BACKFILL => | |
| GroupBy.computeBackfill( | |
| conf.getGroupByBackfill.groupBy, | |
| range.end, | |
| tableUtils, | |
| overrideStartPartition = Option(range.start) | |
| ) | |
| case NodeContent._Fields.GROUP_BY_BACKFILL => | |
| require( | |
| conf.getGroupByBackfill.isSetGroupBy, | |
| "GroupByBackfillNode must have a groupBy set" | |
| ) | |
| logger.info( | |
| s"Running groupBy backfill for '${metadata.name}' for range: [${range.start}, ${range.end}]" | |
| ) | |
| GroupBy.computeBackfill( | |
| conf.getGroupByBackfill.groupBy, | |
| range.end, | |
| tableUtils, | |
| overrideStartPartition = Option(range.start) | |
| ) | |
| logger.info( | |
| s"Successfully completed groupBy backfill for '${metadata.name}'" | |
| ) |
🤖 Prompt for AI Agents
In spark/src/main/scala/ai/chronon/spark/batch/BatchNodeRunner.scala around
lines 103 to 109, the GROUP_BY_BACKFILL case lacks logging and validation
present in other case branches. Add appropriate logging statements before and
after the GroupBy.computeBackfill call to track execution flow and outcomes.
Also, include validation checks on inputs such as
conf.getGroupByBackfill.groupBy and range values to ensure they meet expected
criteria before proceeding.
tchow-zlai
deleted the
thomas/zip-843-support-groupbybackfill-in-the-chronon-batchnoderunner
branch
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