Summary upload #50
Summary upload #50
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Actionable comments posted: 78
🧹 Outside diff range and nitpick comments (45)
spark/src/main/scala/ai/chronon/spark/stats/drift/SummaryUploader.scala (2)
7-7: Add parameter validation and class documentation.Consider adding:
- Parameter validation to prevent null inputs
- Scaladoc explaining the class purpose and parameters
+/** + * Uploads summary statistics to a key-value store by processing a DataFrame containing + * keyBytes and valueBytes columns. + * + * @param summaryDF DataFrame containing summary statistics with keyBytes and valueBytes columns + * @param kvStore Key-value store instance for storing the statistics + */ class SummaryUploader(summaryDF: DataFrame, kvStore: KVStore) { + require(summaryDF != null, "summaryDF cannot be null") + require(kvStore != null, "kvStore cannot be null")
8-8: Consider adding logging.Adding logging would help with monitoring and debugging the upload process.
+import org.apache.spark.internal.Logging + -class SummaryUploader(summaryDF: DataFrame, kvStore: KVStore) { +class SummaryUploader(summaryDF: DataFrame, kvStore: KVStore) extends Logging { def run(): Unit = { + logInfo(s"Starting summary upload for DataFrame with ${summaryDF.count()} rows")online/src/test/scala/ai/chronon/online/test/DataRangeTest.scala (4)
14-14: Remove unnecessary comment.This comment is redundant as the imports already indicate the existence of these classes.
- // Assuming you have a PartitionSpec and PartitionRange class defined somewhere
15-15: Document PartitionSpec configuration.Consider adding a comment explaining why these specific values ("yyyy-MM-dd" format and 1-day window) were chosen for the PartitionSpec configuration.
+ // Configure PartitionSpec with daily granularity for date-based partition testing implicit val partitionSpec: PartitionSpec = new PartitionSpec("yyyy-MM-dd", new Window(1, TimeUnit.DAYS).millis)
34-42: Improve test readability.Consider removing the extra blank line between the test setup and assertions to maintain consistent spacing throughout the test suite.
it should "handle a single partition" in { val partitions = List("2020-01-01") val expectedRanges = Seq(PartitionRange("2020-01-01", "2020-01-01")) - val result = collapseToRange(partitions) - result should have length 1 result should contain theSameElementsAs expectedRanges }
44-65: Consider adding more edge cases.The current test suite covers basic scenarios well. Consider adding tests for:
- Invalid date formats
- Dates spanning multiple years
- Very large date ranges
- Mixed date formats (if supported)
Would you like me to help generate additional test cases for these scenarios?
api/src/main/scala/ai/chronon/api/PartitionSpec.scala (1)
57-59: Clarify documentation about sequence size.The comment "result size will be count + 1" is slightly ambiguous. Consider making it more explicit about the total number of partitions returned and their relationship to the input parameters.
-// all partitions `count` ahead of `s` including `s` - result size will be count + 1 +// Returns a sequence of partitions starting with `s` and including the next `count` partitions. +// The total sequence length will be (count + 1) as it includes the starting partition.spark/src/test/scala/ai/chronon/spark/test/udafs/NullnessCountersAggregatorTest.scala (3)
2-3: Remove extra blank line after package declaration.Maintain consistent spacing with a single blank line after the package declaration.
package ai.chronon.spark.test.udafs - import org.apache.spark.sql.DataFrame
56-57: Remove debug print statements.Debug statements like
show()andprintSchema()should be removed from the test code as they add noise to the test output.- innerDf.show() val resultDf = spark.sql(""" - println("Result Schema:") - resultDf.show() - resultDf.printSchema()Also applies to: 73-75
78-79: Document magic numbers in assertions.Extract magic numbers into named constants to improve test maintainability and readability.
+ val ExpectedNullCount = 4L // 2 nulls in first array + 1 null in second + 1 null in fourth + val ExpectedTotalSize = 12L // 4 + 4 + 0 + 4 elements in arrays - result.getLong(0) shouldBe 4 // Total nulls - result.getLong(1) shouldBe 12 // Total size (including nulls) + result.getLong(0) shouldBe ExpectedNullCount + result.getLong(1) shouldBe ExpectedTotalSizespark/src/test/scala/ai/chronon/spark/test/udafs/ApproxDistinctTest.scala (1)
9-73: Consider adding tests for additional edge cases.While the current test suite covers basic scenarios, consider adding tests for:
- Very large cardinality (near HyperLogLog accuracy bounds)
- Complex nested structures (arrays of maps, maps of arrays)
- Columns with special characters or Unicode
- Boundary conditions for the underlying sketch algorithm
Would you like me to help implement these additional test cases?
spark/src/test/scala/ai/chronon/spark/test/stats/drift/DriftTest.scala (2)
61-62: Convert TODO comments into test cases.The comments indicate missing test coverage for:
- Data preparation with join operations
- Anomaly injection verification
Would you like me to help create these test cases or create GitHub issues to track them?
64-79: Document method parameters and extract constants.
- Add scaladoc for the method parameters
- Extract magic numbers into named constants
- Consider using table name prefix to prevent test interference
+ /** Generates a test DataFrame with various column types + * @param numRows number of rows to generate + * @param partitions number of partitions for the DataFrame + * @return generated DataFrame + */ def generateDataFrame(numRows: Int, partitions: Int)(implicit spark: SparkSession): DataFrame = { + private val MAX_STRING_VARIANTS = 100 + private val MAX_DOLLAR_AMOUNT = 100000 + private val MAX_LIST_SIZE = 1000 + private val MAX_MAP_SIZE = 100 val dollarTransactions = List( - Column("user", api.StringType, 100), - Column("user_name", api.StringType, 100), - Column("amount_dollars", api.LongType, 100000), - Column("item_prices", api.ListType(api.LongType), 1000), - Column("category_item_prices", api.MapType(api.StringType, api.IntType), 100), + Column("user", api.StringType, MAX_STRING_VARIANTS), + Column("user_name", api.StringType, MAX_STRING_VARIANTS), + Column("amount_dollars", api.LongType, MAX_DOLLAR_AMOUNT), + Column("item_prices", api.ListType(api.LongType), MAX_LIST_SIZE), + Column("category_item_prices", api.MapType(api.StringType, api.IntType), MAX_MAP_SIZE), )spark/src/main/scala/ai/chronon/spark/streaming/StreamingStats.scala (1)
32-32: Consider documenting KllFloatsSketch parametersThe switch to
newHeapInstance()factory method is correct and follows Apache DataSketches best practices. However, consider documenting the default parameters (k, m, accuracy) for better maintainability.Add a comment explaining the chosen parameters:
+ // Using default parameters: k=200 (accuracy vs space trade-off) private var latencyHistogram: KllFloatsSketch = KllFloatsSketch.newHeapInstance()aggregator/src/test/scala/ai/chronon/aggregator/test/ApproxPercentilesTest.scala (1)
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32-54: LGTM! Consider adding method documentation.The implementation is thorough and well-structured. The rename to
basicImplTestHelperbetter reflects its purpose as a test utility method.Consider adding ScalaDoc to document the parameters and their acceptable ranges:
+ /** + * Helper method to test approximate percentile calculations + * @param nums Total number of elements to test + * @param slide Window size for sliding operation + * @param k Compression parameter affecting accuracy vs memory trade-off + * @param percentiles Array of percentile values to compute (between 0.0 and 1.0) + * @param errorPercent Acceptable error margin as a percentage + */ def basicImplTestHelper(nums: Int, slide: Int, k: Int, percentiles: Array[Double], errorPercent: Float): Unit = {aggregator/src/test/scala/ai/chronon/aggregator/test/TwoStackLiteAggregatorTest.scala (2)
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68-120: Consider enhancing performance test assertions.The test includes performance timing comparisons in comments, but lacks automated performance assertions. Consider:
- Adding threshold-based performance assertions
- Implementing proper benchmarking using JMH
- Documenting performance expectations
Would you like me to provide an example of how to implement proper performance benchmarking with JMH?
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114-117: Improve test result comparison.The current test uses Gson JSON serialization to compare results. This approach:
- May hide subtle differences in numeric precision
- Makes it harder to debug when assertions fail
- Could be performance intensive for large datasets
Consider implementing a more specific comparison:
def assertAggregationResults(bankersResult: AggregationResult, sawtoothResult: AggregationResult): Unit = { assertEquals(bankersResult.value, sawtoothResult.value) assertEquals(bankersResult.timestamp, sawtoothResult.timestamp) // Add more specific field comparisons } // Use in test bankersIrs.zip(sawtoothIrs).foreach { case (bankers, sawtooth) => assertAggregationResults(bankers, sawtooth) }online/src/main/scala/ai/chronon/online/DataRange.scala (1)
133-170: Consider adding input validation and error handling.The companion object methods would benefit from additional error handling:
- Validate that partitionSpec is not null
- Add error handling for malformed partition strings
- Consider returning Option[Seq[PartitionRange]] instead of null
This would make the API more robust and easier to use safely.
docs/source/test_deploy_serve/Drift.md (4)
5-37: Fix grammar and enhance readability of the introduction.There are several minor issues that should be addressed:
- Line 6: "that" should be "than" in "more effort is required to build and configure such monitoring systems than the data systems themselves"
- Line 16: Add article "a" before "global metric"
- Consider using bullet points consistently for the complex types section to match the style of other lists
Apply these changes to improve readability:
-Typically, *more effort* is required to build and configure such monitoring systems that the data systems themselves. +Typically, *more effort* is required to build and configure such monitoring systems than the data systems themselves. -rate - is global metric - that represents the number of entries +rate - is a global metric - that represents the number of entries🧰 Tools
🪛 LanguageTool
[uncategorized] ~6-~6: The preposition “than” seems more likely in this position.
Context: ...d and configure such monitoring systems that the data systems themselves. We aim to...(AI_EN_LECTOR_REPLACEMENT_PREPOSITION)
[uncategorized] ~16-~16: You might be missing the article “a” here.
Context: ... of issues. - rate - is global metric - that represents the number of ...(AI_EN_LECTOR_MISSING_DETERMINER_A)
[uncategorized] ~27-~27: If this is a compound adjective that modifies the following noun, use a hyphen.
Context: ...gory - is a map of category string to a floating point value. - Vectors - are summaries of...(EN_COMPOUND_ADJECTIVE_INTERNAL)
[uncategorized] ~29-~29: You might be missing the article “a” here.
Context: ...vant items or to transfer learning from larger general purpose model to a smaller task...(AI_EN_LECTOR_MISSING_DETERMINER_A)
[uncategorized] ~29-~29: When ‘task-specific’ is used as a modifier, it is usually spelled with a hyphen.
Context: ...rger general purpose model to a smaller task specific model. Embedding natural da...(SPECIFIC_HYPHEN)
38-53: Improve clarity of core concepts section.The section needs some grammatical improvements:
- Line 41: Add comma after "Since we know / derive"
- Line 51: Remove redundant "While" or restructure the sentence
- Line 52: Consider rephrasing "financial or impact" for clarity
Apply these changes:
-While daily drift monitoring is a reasonable starting point. However, for models with potential for financial or impact -or abuse, it is essential that we surface and remedy issues in realtime. +Daily drift monitoring is a reasonable starting point. However, for models with potential for financial impact +or abuse, it is essential that we surface and remedy issues in realtime.🧰 Tools
🪛 LanguageTool
[uncategorized] ~41-~41: Possible missing comma found.
Context: ...dex is humongous and wasteful. Since we know / derive, the exact set of metrics, we ...(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~47-~47: Possible missing comma found.
Context: ...out requiring a lot of user clicks and scrolls is very hard. Most solutions are only u...(AI_HYDRA_LEO_MISSING_COMMA)
88-94: Fix list indentation in the summarization section.The list indentation is inconsistent. According to Markdown best practices:
- First level items should start at column 0
- Each nested level should be indented by 2 spaces
- Maintain consistent indentation within each level
Apply consistent indentation:
- - continuous values - - - - short, int -> cast to long, float -> cast to double - - widen: cast int, short to long or float to double - - approx_percentile[0, 0.05, 0.1, ..., 0.95, 1] - - array of long or double - - discrete values - map +- continuous values +- numeric types + - short, int -> cast to long, float -> cast to double + - widen: cast int, short to long or float to double + - approx_percentile[0, 0.05, 0.1, ..., 0.95, 1] + - array of long or double +- discrete values - map🧰 Tools
🪛 Markdownlint
94-94: Expected: 8; Actual: 6
Inconsistent indentation for list items at the same level(MD005, list-indent)
88-88: Expected: 0; Actual: 2
Unordered list indentation(MD007, ul-indent)
89-89: Expected: 0; Actual: 2
Unordered list indentation(MD007, ul-indent)
90-90: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
91-91: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
92-92: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
93-93: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
94-94: Expected: 4; Actual: 6
Unordered list indentation(MD007, ul-indent)
1-94: Consider restructuring the document for better flow and completeness.The document provides valuable information about drift monitoring but would benefit from:
- A clear table of contents at the beginning
- Consistent formatting throughout
- Complete examples with code snippets
- A conclusion section summarizing key points
- Links to related documentation or implementation details
Consider adding these sections to make the document more comprehensive:
- Configuration examples
- Integration guidelines
- Troubleshooting guide
- Performance considerations
- Best practices
🧰 Tools
🪛 LanguageTool
[uncategorized] ~6-~6: The preposition “than” seems more likely in this position.
Context: ...d and configure such monitoring systems that the data systems themselves. We aim to...(AI_EN_LECTOR_REPLACEMENT_PREPOSITION)
[uncategorized] ~16-~16: You might be missing the article “a” here.
Context: ... of issues. - rate - is global metric - that represents the number of ...(AI_EN_LECTOR_MISSING_DETERMINER_A)
[uncategorized] ~27-~27: If this is a compound adjective that modifies the following noun, use a hyphen.
Context: ...gory - is a map of category string to a floating point value. - Vectors - are summaries of...(EN_COMPOUND_ADJECTIVE_INTERNAL)
[uncategorized] ~29-~29: You might be missing the article “a” here.
Context: ...vant items or to transfer learning from larger general purpose model to a smaller task...(AI_EN_LECTOR_MISSING_DETERMINER_A)
[uncategorized] ~29-~29: When ‘task-specific’ is used as a modifier, it is usually spelled with a hyphen.
Context: ...rger general purpose model to a smaller task specific model. Embedding natural da...(SPECIFIC_HYPHEN)
[uncategorized] ~41-~41: Possible missing comma found.
Context: ...dex is humongous and wasteful. Since we know / derive, the exact set of metrics, we ...(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~47-~47: Possible missing comma found.
Context: ...out requiring a lot of user clicks and scrolls is very hard. Most solutions are only u...(AI_HYDRA_LEO_MISSING_COMMA)
[style] ~77-~77: In American English, abbreviations like “etc.” require a period.
Context: ...re 20% of values between 1&4, 4&7, 7&11 etc assuming the values are uniformly distr...(ETC_PERIOD)
🪛 Markdownlint
94-94: Expected: 8; Actual: 6
Inconsistent indentation for list items at the same level(MD005, list-indent)
67-67: Expected: 0; Actual: 2
Unordered list indentation(MD007, ul-indent)
88-88: Expected: 0; Actual: 2
Unordered list indentation(MD007, ul-indent)
89-89: Expected: 0; Actual: 2
Unordered list indentation(MD007, ul-indent)
90-90: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
91-91: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
92-92: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
93-93: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
94-94: Expected: 4; Actual: 6
Unordered list indentation(MD007, ul-indent)
aggregator/src/test/scala/ai/chronon/aggregator/test/SawtoothOnlineAggregatorTest.scala (1)
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33-144: Consider enhancing test coverage for edge cases.While the test provides comprehensive coverage for happy path scenarios, consider adding tests for:
- Edge cases:
- Empty event streams
- Boundary conditions for timestamps
- Maximum window sizes
- Error scenarios:
- Invalid window configurations
- Null or malformed input data
This would help ensure robustness of the aggregator implementation.
Would you like me to help generate additional test cases for these scenarios?
aggregator/src/main/scala/ai/chronon/aggregator/row/StatsGenerator.scala (1)
134-139: Add documentation and consider parameter validation.The L-infinity distance calculation lacks documentation explaining its purpose and the impact of the bins parameter. Additionally, the reduction in default bins from 128 to 20 might affect accuracy.
Consider adding documentation and parameter validation:
+ /** + * Calculates the L-infinity distance between two KLL sketches. + * + * @param sketch1 First KLL sketch as serialized bytes + * @param sketch2 Second KLL sketch as serialized bytes + * @param bins Number of bins for quantile calculations (default: 20) + * @return The L-infinity distance between the two distributions + */ def lInfKllSketch(sketch1: AnyRef, sketch2: AnyRef, bins: Int = 20): AnyRef = { + require(bins > 0, "Number of bins must be positive") if (sketch1 == null || sketch2 == null) return Nonespark/src/main/scala/ai/chronon/spark/ChrononKryoRegistrator.scala (2)
113-113: LGTM! Consider adding a comment for the data sketches section.The updated path for KllFloatsSketch registration is correct. Consider grouping and documenting the data sketches related registrations for better maintainability.
+ // Apache DataSketches related classes "org.apache.datasketches.kll.KllFloatsSketch",
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1-186: Consider documenting the DataSketches version compatibility.Since we're migrating from Yahoo to Apache DataSketches, it would be helpful to document the compatible DataSketches version in the project's dependency management files or documentation. This helps track the migration and ensures consistent version usage across the project.
build.sbt (1)
129-129: Consider implications of including Spark dependencies in runtime.The removal of "provided" scope means Spark dependencies will be included in the final artifact. This:
- Significantly increases artifact size
- May cause conflicts with Spark versions in runtime environments
- Appears to be a temporary solution based on the TODO comment above
Consider:
- Adding the planned service module with explicit Spark dependencies
- Documenting version compatibility requirements
- Evaluating if all Spark modules are necessary
spark/src/main/scala/ai/chronon/spark/Analyzer.scala (3)
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52-77: Consider improving the initialization pattern of ItemSketchSerializable.The current initialization pattern using
initmethod could be replaced with a more idiomatic Scala approach using a companion object.Consider this alternative implementation:
-class ItemSketchSerializable extends Serializable { - var sketch: ItemsSketch[String] = null - def init(mapSize: Int): ItemSketchSerializable = { - sketch = new ItemsSketch[String](mapSize) - this - } +object ItemSketchSerializable { + def apply(mapSize: Int): ItemSketchSerializable = { + new ItemSketchSerializable(new ItemsSketch[String](mapSize)) + } +} + +class ItemSketchSerializable private (private var sketch: ItemsSketch[String]) extends Serializable {This approach:
- Eliminates the null initialization
- Provides better encapsulation
- Follows Scala's object-oriented patterns
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127-134: Add validation for the sampleFraction parameter.The
sampleFractionparameter should be validated to ensure it's within a reasonable range (0 < sampleFraction ≤ 1).Add parameter validation:
def heavyHittersWithTsAndCount(df: DataFrame, keys: Array[String], frequentItemMapSize: Int = 1024, sampleFraction: Double = 0.1): Array[(String, Array[(String, Long)])] = { + require(sampleFraction > 0 && sampleFraction <= 1, "sampleFraction must be between 0 and 1") + require(frequentItemMapSize > 0, "frequentItemMapSize must be positive") val baseDf = df.withColumn("total_count", lit("rows")) val baseKeys = keys :+ "total_count"
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449-471: Enhance error reporting with structured logging.The current implementation uses print statements for error reporting. Consider using a proper logging framework with structured logging for better error tracking and monitoring.
Replace print statements with structured logging:
def runDataAvailabilityCheck(...): List[(String, String, String)] = { if (unfilledRanges.isEmpty) { - println("No unfilled ranges found.") + logger.info("Data availability check: no unfilled ranges found") List.empty } else { val firstUnfilledPartition = unfilledRanges.min.start // ... existing code ... if (minPartition.isEmpty || minPartition.get > expectedStart) { - println(s""" - |Join needs data older than what is available for GroupBy: ${groupBy.metaData.name} - |left-${leftDataModel.toString.low.yellow}, - |right-${groupBy.dataModel.toString.low.yellow}, - |accuracy-${groupBy.inferredAccuracy.toString.low.yellow} - |expected earliest available data partition: $expectedStart\n""".stripMargin.red) + logger.error("Data availability check failed", Map( + "groupBy" -> groupBy.metaData.name, + "leftModel" -> leftDataModel.toString, + "rightModel" -> groupBy.dataModel.toString, + "accuracy" -> groupBy.inferredAccuracy.toString, + "expectedStart" -> expectedStart + ))aggregator/src/main/scala/ai/chronon/aggregator/base/SimpleAggregators.scala (1)
339-345: LGTM: New CpcFriendly implicit conversions are well-implemented.The new implicit conversions for
Int,Float, andBigDecimalare correctly implemented, following the established pattern and properly handling type conversions.Consider adding scaladoc comments to document the conversion behavior, especially for
BigDecimalwheretoPlainStringis used instead of other available string representations.Also applies to: 354-357
spark/src/main/scala/ai/chronon/spark/TableUtils.scala (1)
878-883: Consider handling edge cases in partitionRange.While the implementation is functionally correct, it could benefit from additional error handling and documentation.
Consider these improvements:
+ /** + * Returns the partition range (min and max partitions) for the specified table. + * + * @param table The name of the table to get partition range for + * @return PartitionRange containing min and max partitions, null if table doesn't exist or has no partitions + */ def partitionRange(table: String): PartitionRange = { + if (!tableExists(table)) { + return PartitionRange(null, null)(partitionSpec) + } val parts = partitions(table) val minPartition = parts.reduceOption(Ordering[String].min).orNull val maxPartition = parts.reduceOption(Ordering[String].max).orNull PartitionRange(minPartition, maxPartition)(partitionSpec) }spark/src/main/scala/ai/chronon/spark/stats/drift/DistanceMetrics.scala (1)
58-68: Remove or revise themainmethod used for testing.Including a
mainmethod within the class for demonstration purposes is not a best practice for production code. It can be confusing and may unintentionally be included in the deployed code.Consider moving test code to a separate unit test suite or an example script. If keeping the
mainmethod is necessary for demonstration, ensure it's clearly marked and excluded from production builds.spark/src/main/scala/ai/chronon/spark/utils/PartitionRunner.scala (3)
83-96: Use a logging framework instead ofprintlnstatementsThe code uses
printlnstatements for logging, which can be less flexible and harder to control in production environments. Consider using a logging framework likeLog4jorSLF4Jfor better log management, level control, and output formatting.Also applies to: 109-113, 123-127
79-79: Address the TODO: Handle multiple input casesThere's a TODO comment indicating the need to handle multiple input cases. Implementing this functionality will enhance the utility of the
PartitionRunnerfor scenarios involving multiple input tables or sources.Would you like assistance in implementing support for multiple input cases?
101-102: Address the TODO: Integrate with control plane for parallel executionThe TODO comment suggests handing over the job to a control plane for parallel execution and merging concurrent jobs. Implementing this feature will improve performance and scalability.
Can I assist you in developing the integration with the control plane for parallel processing?
spark/src/main/scala/ai/chronon/spark/stats/drift/Expressions.scala (5)
170-170: Address the TODO: Handle map key cardinality.There's a TODO comment indicating the need to measure and handle map key cardinality. This is important for accurately summarizing map types and could impact the effectiveness of data drift detection.
Would you like assistance in implementing this feature or creating a GitHub issue to track this task?
218-219: Address the TODOs: Handle map keys and implement heavy hitters.The TODO comments suggest plans to:
- Deal with map keys for histograms, particularly in scenarios with high or low cardinality.
- Implement heavy hitters (top-k elements) using approximate histograms.
Including these features would enhance the summarization capabilities for complex data types.
Would you like assistance in implementing these features or creating GitHub issues to track these tasks?
156-157: Simplify thecehelper method inCardinalityExpression.The
cemethod currently wrapsselectwithOption, butselectcould already be anOption[String].Apply this diff to adjust the method signature and usage:
-private def ce(select: String, aggExpr: String): CardinalityExpression = - CardinalityExpression(Option(select), aggExpr) +private def ce(select: Option[String], aggExpr: String): CardinalityExpression = + CardinalityExpression(select, aggExpr)Ensure that calls to
ceare updated accordingly to passOption[String]forselect.
185-186: Clarify immutability in theSummaryExpressioncase class.The
rendermethod creates a newSummaryExpression, but it's not immediately clear if instances are immutable.Consider adding documentation to the
SummaryExpressionclass to clarify its immutability, which can aid in understanding and maintaining the code.+/** + * A case class representing a summary expression. + * Immutable after creation. + */ case class SummaryExpression(mapEx: Option[String], aggEx: String, name: String, column: String)
3-260: Add unit tests for theExpressionsobject and its components.Given the complexity and importance of the
Expressionsobject, comprehensive unit tests would ensure its reliability and facilitate future maintenance.Would you like assistance in generating unit tests for these components or creating a GitHub issue to prioritize this task?
spark/src/main/scala/ai/chronon/spark/stats/drift/Summarizer.scala (1)
35-35: Address the pending TODO commentThere is a TODO comment indicating an override for the metric is needed. This suggests that some functionality related to metric overriding is yet to be implemented.
Would you like assistance in implementing the metric override or creating a placeholder implementation?
spark/src/test/scala/ai/chronon/spark/test/stats/drift/PrepareData.scala (1)
361-383: Replace magic numbers with named constantsSeveral hard-coded values, such as
Random.nextInt(551) + 300andRandom.nextInt(3650) + 1, are used throughout the data generation process. These magic numbers can make the code less readable.Define these numbers as constants with descriptive names (e.g.,
MIN_CREDIT_SCORE,MAX_CREDIT_SCORE) to improve code clarity and maintainability.api/thrift/api.thrift (2)
170-170: Consider reorderingTimeUnitenum valuesFor consistency, consider ordering the
TimeUnitenum values from the smallest to the largest unit, i.e.,MINUTES,HOURS,DAYS.
289-294: Consider orderingDriftMetricenum values consistentlyFor better readability, consider ordering the
DriftMetricenum values alphabetically or by relevance.
📜 Review details
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Review profile: CHILL
📒 Files selected for processing (42)
- aggregator/src/main/scala/ai/chronon/aggregator/base/SimpleAggregators.scala (5 hunks)
- aggregator/src/main/scala/ai/chronon/aggregator/row/StatsGenerator.scala (3 hunks)
- aggregator/src/test/scala/ai/chronon/aggregator/test/ApproxPercentilesTest.scala (3 hunks)
- aggregator/src/test/scala/ai/chronon/aggregator/test/DataGen.scala (10 hunks)
- aggregator/src/test/scala/ai/chronon/aggregator/test/SawtoothAggregatorTest.scala (2 hunks)
- aggregator/src/test/scala/ai/chronon/aggregator/test/SawtoothOnlineAggregatorTest.scala (1 hunks)
- aggregator/src/test/scala/ai/chronon/aggregator/test/TwoStackLiteAggregatorTest.scala (1 hunks)
- api/py/python-api-build.sh (1 hunks)
- api/py/setup.py (1 hunks)
- api/src/main/scala/ai/chronon/api/Builders.scala (1 hunks)
- api/src/main/scala/ai/chronon/api/Constants.scala (1 hunks)
- api/src/main/scala/ai/chronon/api/Extensions.scala (2 hunks)
- api/src/main/scala/ai/chronon/api/PartitionSpec.scala (1 hunks)
- api/thrift/api.thrift (3 hunks)
- build.sbt (3 hunks)
- build.sh (1 hunks)
- docs/source/conf.py (1 hunks)
- docs/source/test_deploy_serve/Drift.md (1 hunks)
- online/src/main/java/ai/chronon/online/scripts/AvroDecodingBenchmark.scala (1 hunks)
- online/src/main/scala/ai/chronon/online/DataRange.scala (2 hunks)
- online/src/test/scala/ai/chronon/online/test/DataRangeTest.scala (1 hunks)
- spark/src/main/scala/ai/chronon/spark/Analyzer.scala (1 hunks)
- spark/src/main/scala/ai/chronon/spark/ChrononKryoRegistrator.scala (2 hunks)
- spark/src/main/scala/ai/chronon/spark/Extensions.scala (2 hunks)
- spark/src/main/scala/ai/chronon/spark/TableUtils.scala (2 hunks)
- spark/src/main/scala/ai/chronon/spark/stats/StatsCompute.scala (1 hunks)
- spark/src/main/scala/ai/chronon/spark/stats/drift/DistanceMetrics.scala (1 hunks)
- spark/src/main/scala/ai/chronon/spark/stats/drift/Expressions.scala (1 hunks)
- spark/src/main/scala/ai/chronon/spark/stats/drift/Summarizer.scala (1 hunks)
- spark/src/main/scala/ai/chronon/spark/stats/drift/SummaryUploader.scala (1 hunks)
- spark/src/main/scala/ai/chronon/spark/streaming/StreamingStats.scala (3 hunks)
- spark/src/main/scala/ai/chronon/spark/udafs/ApproxDistinct.scala (1 hunks)
- spark/src/main/scala/ai/chronon/spark/udafs/Histogram.scala (1 hunks)
- spark/src/main/scala/ai/chronon/spark/utils/PartitionRunner.scala (1 hunks)
- spark/src/test/scala/ai/chronon/spark/test/FetcherTest.scala (1 hunks)
- spark/src/test/scala/ai/chronon/spark/test/MockKVStore.scala (1 hunks)
- spark/src/test/scala/ai/chronon/spark/test/MutationsTest.scala (1 hunks)
- spark/src/test/scala/ai/chronon/spark/test/stats/drift/DriftTest.scala (1 hunks)
- spark/src/test/scala/ai/chronon/spark/test/stats/drift/PrepareData.scala (1 hunks)
- spark/src/test/scala/ai/chronon/spark/test/udafs/ApproxDistinctTest.scala (1 hunks)
- spark/src/test/scala/ai/chronon/spark/test/udafs/HistogramTest.scala (1 hunks)
- spark/src/test/scala/ai/chronon/spark/test/udafs/NullnessCountersAggregatorTest.scala (1 hunks)
🧰 Additional context used
🪛 Shellcheck
build.sh
[warning] 45-45: Don't use ls | grep. Use a glob or a for loop with a condition to allow non-alphanumeric filenames.
(SC2010)
🪛 LanguageTool
docs/source/test_deploy_serve/Drift.md
[uncategorized] ~6-~6: The preposition “than” seems more likely in this position.
Context: ...d and configure such monitoring systems that the data systems themselves. We aim to...(AI_EN_LECTOR_REPLACEMENT_PREPOSITION)
[uncategorized] ~16-~16: You might be missing the article “a” here.
Context: ... of issues. - rate - is global metric - that represents the number of ...(AI_EN_LECTOR_MISSING_DETERMINER_A)
[uncategorized] ~27-~27: If this is a compound adjective that modifies the following noun, use a hyphen.
Context: ...gory - is a map of category string to a floating point value. - Vectors - are summaries of...(EN_COMPOUND_ADJECTIVE_INTERNAL)
[uncategorized] ~29-~29: You might be missing the article “a” here.
Context: ...vant items or to transfer learning from larger general purpose model to a smaller task...(AI_EN_LECTOR_MISSING_DETERMINER_A)
[uncategorized] ~29-~29: When ‘task-specific’ is used as a modifier, it is usually spelled with a hyphen.
Context: ...rger general purpose model to a smaller task specific model. Embedding natural da...(SPECIFIC_HYPHEN)
[uncategorized] ~41-~41: Possible missing comma found.
Context: ...dex is humongous and wasteful. Since we know / derive, the exact set of metrics, we ...(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~47-~47: Possible missing comma found.
Context: ...out requiring a lot of user clicks and scrolls is very hard. Most solutions are only u...(AI_HYDRA_LEO_MISSING_COMMA)
[style] ~77-~77: In American English, abbreviations like “etc.” require a period.
Context: ...re 20% of values between 1&4, 4&7, 7&11 etc assuming the values are uniformly distr...(ETC_PERIOD)
🪛 Markdownlint
docs/source/test_deploy_serve/Drift.md
94-94: Expected: 8; Actual: 6
Inconsistent indentation for list items at the same level(MD005, list-indent)
67-67: Expected: 0; Actual: 2
Unordered list indentation(MD007, ul-indent)
88-88: Expected: 0; Actual: 2
Unordered list indentation(MD007, ul-indent)
89-89: Expected: 0; Actual: 2
Unordered list indentation(MD007, ul-indent)
90-90: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
91-91: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
92-92: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
93-93: Expected: 4; Actual: 8
Unordered list indentation(MD007, ul-indent)
94-94: Expected: 4; Actual: 6
Unordered list indentation(MD007, ul-indent)
🔇 Additional comments (65)
spark/src/main/scala/ai/chronon/spark/stats/drift/SummaryUploader.scala (1)
1-6: LGTM! Imports are clean and well-organized.spark/src/test/scala/ai/chronon/spark/test/MockKVStore.scala (1)
1-27: Add unit tests for MockKVStoreSince this is a test utility class, it should have its own unit tests to ensure it behaves correctly when used in other tests.
Would you like me to help create unit tests for this mock implementation?
build.sh (2)
36-36: LGTM! Package naming follows Python conventionsThe change from hyphen to underscore in the package name follows PEP 8 conventions for Python package naming.
44-44: Verify the impact of simplified SBT assembly commandThe change from
spark_uber/assemblytospark/assemblymight indicate significant changes in how the jar is assembled. Please ensure this doesn't affect the jar's contents or downstream dependencies.online/src/test/scala/ai/chronon/online/test/DataRangeTest.scala (2)
1-11: LGTM! Well-organized imports.The imports are clean, relevant, and properly organized.
1-66: Verify test coverage for PartitionRange functionality.While this test suite provides good coverage for the
collapseToRangefunction, let's verify coverage for other PartitionRange functionality.✅ Verification successful
Let me try a different search strategy to locate the PartitionRange implementation and related tests.
Based on the search results, I can now generate the final response. The codebase shows extensive usage of PartitionRange and reveals that the test coverage is well distributed:
Test coverage for PartitionRange is comprehensive across multiple test files
The test coverage for PartitionRange functionality is well-distributed across several test files:
online/src/test/scala/ai/chronon/online/test/DataRangeTest.scala: Tests core PartitionRange functionality includingcollapseToRangespark/src/test/scala/ai/chronon/spark/test/TableUtilsTest.scala: Tests PartitionRange in table operationsspark/src/test/scala/ai/chronon/spark/test/JoinUtilsTest.scala: Tests PartitionRange in join operationsspark/src/test/scala/ai/chronon/spark/test/GroupByTest.scala: Tests PartitionRange in group-by operationsThe test coverage spans both the core functionality and its integration with various components of the system.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for other test files related to PartitionRange echo "Searching for other PartitionRange test files..." rg -t scala "class.*Test.*PartitionRange" --type scala # Search for untested public methods in PartitionRange echo "Searching for potentially untested public methods..." ast-grep --pattern 'def $methodName($$$): $returnType = $$$' online/src/main/scala/ai/chronon/online/PartitionRange.scalaLength of output: 510
Script:
#!/bin/bash # Search for PartitionRange in all scala files echo "Searching for PartitionRange implementation..." fd -e scala | xargs rg "PartitionRange" -l # Get content of files containing PartitionRange echo -e "\nContent of files containing PartitionRange:" fd -e scala | xargs rg "PartitionRange" -A 5Length of output: 84958
api/src/main/scala/ai/chronon/api/PartitionSpec.scala (1)
50-64: Verify integration with DataRange usage.The new methods appear to support enhanced partition handling in DataRange. Let's verify their usage:
spark/src/test/scala/ai/chronon/spark/test/udafs/NullnessCountersAggregatorTest.scala (3)
28-33: LGTM! Good test data coverage.The test data effectively covers various null scenarios:
- Row 1: Multiple nulls within array
- Row 2: Single null within array
- Row 3: Entire array is null
- Row 4: Single null within array
69-71: Verify percentile calculation results.The query calculates percentiles but the results aren't being verified in the test assertions.
Add assertions for the percentile results:
val result = resultDf.collect().head result.getLong(0) shouldBe 4 // Total nulls result.getLong(1) shouldBe 12 // Total size (including nulls) + val percentiles = result.getAs[Seq[Double]](2) + percentiles.length shouldBe 2 + percentiles(0) shouldBe 4.0 // median (50th percentile) + percentiles(1) shouldBe 4.0 // 95th percentile
82-87: LGTM! Proper cleanup implementation.The cleanup code correctly handles the SparkSession shutdown with appropriate null checking and super call.
docs/source/conf.py (1)
83-83: LGTM: Article header configuration is properly structured.The theme configuration for article header elements includes both the navbar icon links and theme switcher, which is the correct setup for the sphinx-book-theme.
spark/src/test/scala/ai/chronon/spark/test/udafs/ApproxDistinctTest.scala (1)
1-9: LGTM! Well-structured test class setup.The test class is properly structured with appropriate imports and test framework traits.
api/src/main/scala/ai/chronon/api/Constants.scala (2)
51-51: LGTM - Constant addition is well-placed.The new constant follows the established patterns:
- Consistent naming convention with other column constants
- Logically grouped with other column definitions
- Immutable value definition (using
val)
51-51: Add documentation for the TileColumn constant.While the constant follows the naming convention of other columns, please add a comment explaining its purpose and usage in the context of data summarization.
Let's verify how this constant is being used across the codebase:
✅ Verification successful
Let me gather more context about the usage of TileColumn to verify if it's being used consistently and if there's existing documentation.
Based on the gathered context, I can now provide a final response regarding the TileColumn constant and its documentation:
Documentation for TileColumn is appropriate in its current form
The
TileColumnconstant with value "_tile" is correctly used as part of Chronon's tiled architecture implementation, where:
- It's used consistently across the codebase for identifying tile-based aggregations in drift analysis and summarization
- The prefix underscore is consistent with internal column naming conventions
- The documentation in
Tiled_Architecture.mdandwindow_tiling.mdthoroughly explains the tiling concept and its implementationThe constant is properly used in:
Expressions.scalafor tile-based summary schema indexingSummarizer.scalafor tile column injection and validationNo additional documentation is needed directly in the Constants file as:
- The constant's purpose is well-documented in the architecture docs
- Its usage follows established naming patterns
- The implementation details are appropriately covered in the technical documentation
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Search for usages of TileColumn constant and "_tile" string # to ensure consistent usage through the constant rather than hardcoded strings echo "Searching for TileColumn constant usage:" rg -l "TileColumn" echo -e "\nSearching for potential hardcoded '_tile' strings:" rg -l "_tile" echo -e "\nSearching for related documentation or comments:" rg -l -g "*.md" -g "*.scala" "tile"Length of output: 3459
Script:
#!/bin/bash # Check the actual usage context of TileColumn in the identified files echo "Checking TileColumn usage in Expressions.scala:" rg -A 5 -B 5 "TileColumn" "spark/src/main/scala/ai/chronon/spark/stats/drift/Expressions.scala" echo -e "\nChecking TileColumn usage in Summarizer.scala:" rg -A 5 -B 5 "TileColumn" "spark/src/main/scala/ai/chronon/spark/stats/drift/Summarizer.scala" echo -e "\nChecking documentation in Tiled_Architecture.md:" cat "docs/source/Tiled_Architecture.md" echo -e "\nChecking documentation in window_tiling.md:" cat "docs/source/window_tiling.md"Length of output: 31213
online/src/main/java/ai/chronon/online/scripts/AvroDecodingBenchmark.scala (2)
1-16: LGTM! Imports and object declaration are well-organized.The necessary imports are present and properly organized by package. The documentation clearly states the purpose of this benchmark utility.
1-93: Verify memory requirements and add documentation.Please verify that this benchmark can run within reasonable memory constraints. Consider adding documentation about:
- Memory requirements
- Expected runtime
- Recommended JVM settings
✅ Verification successful
Let me verify if there are any existing memory-related documentation or JVM settings in the codebase.
Based on the analysis of the codebase and memory calculations, I'll generate the final response.
Add memory requirements documentation and JVM settings
The benchmark requires significant memory resources:
- Total data size: ~1.9GB for 1M records
- Recommended heap size: ~4GB minimum
- Expected runtime: Varies by system but outputs detailed metrics
Add the following documentation above the
mainmethod:/** * Avro decoding benchmark that generates and processes 1M records. * * Requirements: * - Minimum heap size: 4GB (-Xmx4g) * - Disk space: ~2GB for the generated Avro file * - Expected runtime: 1-5 minutes depending on hardware * * Example usage: * java -Xmx4g -XX:+UseG1GC AvroDecodingBenchmark */This aligns with other memory configurations found in the codebase (e.g., Spark executor memory of 2-8GB in docker-compose.yml and Kaggle documentation).
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Calculate approximate memory requirements echo "Calculating approximate memory requirements..." # Each record has 250 longs (8 bytes each) plus overhead RECORD_SIZE=2000 # bytes (approximate) NUM_RECORDS=1000000 TOTAL_SIZE=$((RECORD_SIZE * NUM_RECORDS)) TOTAL_SIZE_MB=$((TOTAL_SIZE / 1024 / 1024)) echo "Estimated memory requirements:" echo "Single record size: ~${RECORD_SIZE} bytes" echo "Total data size: ~${TOTAL_SIZE_MB} MB" echo "Recommended heap size: ~$((TOTAL_SIZE_MB * 2)) MB (2x data size)"Length of output: 496
Script:
#!/bin/bash # Check for memory-related documentation or JVM settings echo "Searching for memory-related documentation..." rg -i "memory|heap|jvm" --type md echo -e "\nChecking for build/run configuration files..." fd -e properties -e conf -e yml -e yaml -e xml -e gradle -e sbt | xargs rg -i "memory|heap|jvm|xmx|xms" echo -e "\nChecking for similar benchmark files..." fd -e scala -e java | grep -i "benchmark" | xargs catLength of output: 9057
spark/src/test/scala/ai/chronon/spark/test/stats/drift/DriftTest.scala (1)
1-19: LGTM! Imports are well-organized and necessary.The imports are properly structured, separating test utilities from production code.
spark/src/main/scala/ai/chronon/spark/streaming/StreamingStats.scala (2)
Line range hint
32-61: Ensure regression tests cover sketch behaviorThe migration to Apache DataSketches, while maintaining the same logical behavior, might have subtle differences in accuracy or memory usage. Consider adding regression tests comparing the quantile calculations between the old and new implementations.
Let's check for existing tests:
#!/bin/bash # Look for related test files fd -e scala -e java "Test.*Stats|.*StatsTest" # Check for quantile-related test assertions rg "getQuantile|assert.*quantile" -t scala -t java
21-21: LGTM! Verify complete migration from Yahoo to Apache DataSketchesThe import change from Yahoo to Apache DataSketches is correct, as Apache DataSketches is the successor project.
Let's verify the migration is complete across the codebase:
✅ Verification successful
Let me gather more information to verify the migration completeness.
Migration from Yahoo to Apache DataSketches is complete and consistent
Based on the comprehensive search results:
- No instances of
com.yahoo.sketcheswere found in the codebase- All
KllFloatsSketchusages consistently use the Apache DataSketches import (org.apache.datasketches.kll.KllFloatsSketch)- The migration is properly reflected across all relevant files including:
- Main application code in
spark/andaggregator/modules- Test files
- Build configuration (
build.sbt)🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check for any remaining Yahoo DataSketches imports rg "com\.yahoo\.sketches" # Verify consistent usage of Apache DataSketches rg "org\.apache\.datasketches"Length of output: 1355
Script:
#!/bin/bash # Search for any remaining Yahoo DataSketches imports or usage rg -l "com\.yahoo\.sketches" # Search for Apache DataSketches imports to verify consistent migration rg -l "org\.apache\.datasketches" # Search for KllFloatsSketch usage to ensure all instances are using Apache's version rg "KllFloatsSketch" -A 2Length of output: 9081
aggregator/src/test/scala/ai/chronon/aggregator/test/ApproxPercentilesTest.scala (2)
68-69: LGTM! Verify consistent KllFloatsSketch instantiation patternThe change to use
newHeapInstancefactory method is correct and follows the Apache DataSketches API pattern.✅ Verification successful
Based on the shell script results, I can now generate the final response:
✅ KllFloatsSketch instantiation pattern is consistent across the codebase
The verification confirms that:
- No direct constructor usage (
new KllFloatsSketch) exists in the codebase- All instances are created using the
newHeapInstancefactory method consistently across all files:
aggregator/src/main/scala/ai/chronon/aggregator/base/SimpleAggregators.scalaaggregator/src/test/scala/ai/chronon/aggregator/test/ApproxPercentilesTest.scalaspark/src/main/scala/ai/chronon/spark/streaming/StreamingStats.scala🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check for any remaining direct KllFloatsSketch constructor usage ast-grep --pattern 'new KllFloatsSketch' # Verify consistent usage of factory method rg "KllFloatsSketch\.newHeapInstance" -t scalaLength of output: 902
22-22: Verify Apache DataSketches migration compatibilityThe change from Yahoo to Apache DataSketches package is part of a larger migration. While the import change is correct, we should ensure all related dependencies are properly updated.
✅ Verification successful
Let me gather more information to verify the migration status comprehensively.
The previous results showed KllFloatsSketch usage but we need to verify the complete migration status by checking build configurations and ensuring no old imports remain.
Based on the comprehensive results, I can now provide the final response:
Migration to Apache DataSketches is complete and correct
The verification shows:
- No remaining
com.yahoodependencies found in the codebase- The build.sbt correctly includes Apache DataSketches dependency:
"org.apache.datasketches" % "datasketches-java" % "6.1.0"- All imports across the codebase consistently use
org.apache.datasketchesnamespace- The KllFloatsSketch usage is consistent with the Apache DataSketches API
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check for any remaining Yahoo DataSketches dependencies rg "com\.yahoo\.sketches" -t scala # Verify build configuration includes Apache DataSketches fd "build.sbt|pom.xml" --exec grep -l "datasketches"Length of output: 902
Script:
#!/bin/bash # Check build files for both old and new dependencies fd "build.sbt" --exec cat {} # Check for any remaining Yahoo imports across all file types rg "com\.yahoo" --type-list rg "com\.yahoo" # Check for Apache DataSketches imports rg "org\.apache\.datasketches" -t scalaLength of output: 14926
aggregator/src/test/scala/ai/chronon/aggregator/test/TwoStackLiteAggregatorTest.scala (1)
68-68: Verify type compatibility across aggregator implementations.The explicit casting to
Longsuggests potential type inference issues. While this change aligns the types correctly, we should ensure that:
- All aggregator implementations handle the Long type consistently
- The type casting doesn't mask any underlying type compatibility issues
Let's verify the type handling across the codebase:
✅ Verification successful
Let me generate additional verification scripts to better understand the type handling in the codebase:
This will help us understand:
- The return type of
genTimestampsmethod- How Window class handles timestamps
- If there are any other timestamp type conversions in the codebase
Based on the gathered evidence, I can now generate the final response:
Type casting is consistent with codebase patterns
The explicit casting of timestamps to
LonginTwoStackLiteAggregatorTestis consistent with the established patterns across the codebase:
- All test files using
CStream.genTimestamps()consistently cast toLong(SawtoothOnlineAggregatorTest, SawtoothAggregatorTest)- Timestamp handling throughout the codebase consistently uses
Longtype for epoch milliseconds- The type casting is part of the standard timestamp handling pattern in both test and production code
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for other timestamp handling patterns to ensure consistency ast-grep --pattern 'CStream.genTimestamps($$$).map($$$)' # Look for potential type-related issues in aggregator implementations rg -l "asInstanceOf\[Long\]" | xargs -I {} rg -A 5 "class.*Aggregator" {}Length of output: 4421
Script:
#!/bin/bash # Check CStream.genTimestamps implementation and return type ast-grep --pattern 'def genTimestamps($$$)' # Look for Window class usage and timestamp handling rg -A 3 "class Window" # Check for other timestamp-related type conversions rg "asInstanceOf\[(Long|Timestamp)\]"Length of output: 5439
spark/src/test/scala/ai/chronon/spark/test/udafs/HistogramTest.scala (3)
1-14: LGTM! Well-structured test class setup.The class is properly organized with appropriate test framework traits and necessary imports.
72-77: LGTM! Proper resource cleanup.The teardown logic correctly stops the Spark session and calls super.
79-94: 🛠️ Refactor suggestionConsider enhancing test coverage and readability.
While the tests cover basic functionality, consider these improvements:
- Add separate test cases for:
- Empty groups
- Large maps/arrays
- Special characters in strings
- Make SQL queries more readable using multiline strings with stripMargin
- Add property-based tests for more thorough validation
Example of improved SQL query formatting:
val result = spark.sql( """ |SELECT | group, | map_histogram(data) as histogram |FROM map_data |GROUP BY group |ORDER BY group """.stripMargin).collect()Let's verify if there are any existing property-based tests in the codebase:
Also applies to: 96-111, 113-128
spark/src/main/scala/ai/chronon/spark/stats/StatsCompute.scala (2)
27-28: Add tests and documentation for statistical operations.Given the critical nature of statistical computations and the library migration:
- Consider adding specific tests for the DataSketches migration, particularly around the
percentileFinalizerUdffunctionality- Update documentation to reflect the transition to Apache DataSketches and explain the statistical methodology
Let's check for existing tests and documentation:
#!/bin/bash # Description: Verify test coverage and documentation # Test 1: Look for related test files echo "Checking for related test files..." fd -e scala -e java "Test.*Stats.*|.*StatsTest.*|.*PercentileTest.*" # Test 2: Check for documentation files echo "Checking for documentation..." fd -e md -e rst "stats|percentile|sketch"Would you like help creating:
- Test cases for the DataSketches migration?
- Documentation for the statistical operations?
Also applies to: 89-95
27-28: LGTM! Migration to Apache DataSketches.The transition from Yahoo to Apache DataSketches is a positive change as it uses the officially maintained package.
Let's verify all DataSketches usage in the codebase to ensure consistent migration:
✅ Verification successful
Let me generate more focused shell scripts to verify the migration properly.
Migration to Apache DataSketches is complete and consistent
The verification results show:
- No remaining Yahoo DataSketches imports or usage found in the codebase
- All DataSketches imports consistently use the
org.apache.datasketchespackageKllFloatsSketchusage is properly migrated across all files:
- Spark components (ChrononKryoRegistrator, StreamingStats, StatsCompute)
- Aggregator components (StatsGenerator, SimpleAggregators)
- Test files (ApproxPercentilesTest)
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining Yahoo DataSketches imports or mixed usage # Test 1: Check for any remaining Yahoo DataSketches imports echo "Checking for remaining Yahoo DataSketches imports..." rg "com\.yahoo\.sketches" # Test 2: Verify consistent Apache DataSketches usage echo "Verifying Apache DataSketches usage..." rg "org\.apache\.datasketches"Length of output: 421
Script:
#!/bin/bash # Let's do a more thorough check with proper error handling # Check for any Yahoo DataSketches imports echo "=== Checking for Yahoo DataSketches imports ===" rg --type scala --type java "import.*com\.yahoo\.sketches" # Check for Apache DataSketches imports echo -e "\n=== Checking for Apache DataSketches imports ===" rg --type scala --type java "import.*org\.apache\.datasketches" # Check for any remaining Yahoo DataSketches usage in code echo -e "\n=== Checking for Yahoo DataSketches usage in code ===" rg --type scala --type java "com\.yahoo\.sketches" -g '!*.{sbt,properties,xml}' # Look for KllFloatsSketch usage to ensure consistent migration echo -e "\n=== Checking KllFloatsSketch usage ===" rg --type scala --type java "KllFloatsSketch"Length of output: 7928
online/src/main/scala/ai/chronon/online/DataRange.scala (3)
87-87: LGTM! Clear and helpful comment.The comment effectively clarifies that using equal values for size and step in sliding creates a tumbling window behavior.
134-137: LGTM! Clear string representation.The method provides a clean and readable string representation of partition ranges.
166-169: LGTM! Simple and effective wrapper method.The method provides a convenient way to get a string representation of collapsed ranges.
aggregator/src/test/scala/ai/chronon/aggregator/test/SawtoothOnlineAggregatorTest.scala (1)
40-40: Consider addressing the root cause of type inference issues.While the explicit casting to
Longmaintains consistency with other test files, it might indicate underlying type inference issues withCStream.genTimestamps. Consider:
- Adding type annotations to
CStream.genTimestampsto ensure it returns the correct type directly- Documenting why the explicit casting is necessary if it's intentional
Let's check if similar casting is used consistently across the codebase:
aggregator/src/main/scala/ai/chronon/aggregator/row/StatsGenerator.scala (2)
21-22: LGTM: Clean transition to Apache DataSketches.The import changes correctly implement the transition from Yahoo to Apache DataSketches library.
Line range hint
134-173: Verify the impact of reducing default bins from 128 to 20.The reduction in default bins from 128 to 20 in both
lInfKllSketchandPSIKllSketchmethods is a significant change that could affect the accuracy of the statistical calculations. Please ensure this change has been thoroughly tested with real-world data distributions.Let's check for any existing tests or benchmarks that validate this change:
aggregator/src/test/scala/ai/chronon/aggregator/test/SawtoothAggregatorTest.scala (2)
124-124: Same type casting issue as in testTailAccuracy.This reinforces that the type casting issue is systematic rather than isolated.
The same recommendations apply here regarding improving type safety in
CStream.genTimestamps.
Line range hint
53-124: Verify test coverage after type system changes.While the tests are comprehensive in comparing naive vs optimized implementations, please ensure:
- The type casting changes don't mask any edge cases
- All window and aggregation combinations are still effectively tested
- Error cases (e.g., invalid timestamps) are handled appropriately
spark/src/main/scala/ai/chronon/spark/ChrononKryoRegistrator.scala (1)
29-32: Verify consistent package migration across the codebase.The import changes from
com.yahootoorg.apache.datasketchesare correct. However, we should ensure all related imports are consistently updated throughout the codebase.✅ Verification successful
Based on the results from the shell scripts, I can now generate the final response:
Package migration from Yahoo to Apache DataSketches is complete and consistent
The verification shows:
- No remaining
com.yahoo.sketchesimports were found in the codebase- All DataSketches imports consistently use the
org.apache.datasketchespackage across all relevant files- The imports are properly organized across different modules (spark, aggregator) and their respective components
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining com.yahoo.sketches imports that need migration echo "Checking for any remaining Yahoo DataSketches imports..." rg "import com\.yahoo\.sketches" # Description: Verify consistent usage of org.apache.datasketches echo "Verifying Apache DataSketches imports..." rg "import org\.apache\.datasketches\.(common|cpc|frequencies|memory|kll)"Length of output: 3443
build.sbt (2)
171-177: Document overwatch project's purpose and review dependency scope.The new
overwatchproject includes both Spark and Flink dependencies without "provided" scope. Please:
- Document the project's purpose and responsibilities
- Consider using "provided" scope for framework dependencies if they'll be available in the runtime environment
- Evaluate if both Spark and Flink are necessary in the same artifact
Let's check for documentation:
#!/bin/bash # Description: Look for overwatch project documentation # Check for README or documentation files echo "Checking for project documentation..." fd -t f "README|\.md$" overwatch/ # Check for main entry points or key classes echo "Checking for main classes..." fd -t f "\.scala$" overwatch/src/main/
110-110: Verify the impact of major datasketches version upgrade.The upgrade from
com.yahoo.datasketches0.13.4 toorg.apache.datasketches6.1.0 represents a significant version jump and package change. This could introduce breaking API changes.Let's verify the impact:
api/src/main/scala/ai/chronon/api/Builders.scala (1)
278-280:⚠️ Potential issueAdd null check for name parameter and consider validating team names.
The new team assignment logic has potential edge cases:
- If both
teamandnameare null, this could lead to a NullPointerException- There's no validation of the derived team name format
Add null check and validation:
- val effectiveTeam = Option(team).getOrElse(name.split("\\.").headOption.getOrElse("chronon")) + val effectiveTeam = Option(team).getOrElse { + Option(name).flatMap(n => n.split("\\.").headOption).getOrElse("chronon") + } + require(effectiveTeam.matches("[a-zA-Z0-9-_]+"), s"Invalid team name format: $effectiveTeam")Consider adding a comment explaining the team name derivation logic:
// Team name is derived from: // 1. Explicitly provided team parameter // 2. First segment of the name parameter (split by dot) // 3. Default value "chronon"Let's verify the impact of this change on existing team assignments:
spark/src/main/scala/ai/chronon/spark/Extensions.scala (2)
42-42: LGTM: Import follows Scala conventions.The added import for
scala.collection.mutableis appropriately placed and necessary for the new functionality.
300-303: 🛠️ Refactor suggestionAdd documentation and consider performance implications.
The method computes approximate distinct counts for all columns which could be expensive for wide DataFrames.
- Add scaladoc to document the behavior and approximation error:
+ /** + * Computes approximate distinct count for each column in the DataFrame. + * Uses HyperLogLog with default relative standard deviation (RSD) of 0.05. + * + * @return Map of column names to their approximate distinct counts + * @note For wide DataFrames, consider computing distinct counts only for specific columns + */ def computeUniqueCountsMap: Map[String, Long] = {Let's verify the column count to assess potential memory impact:
spark/src/main/scala/ai/chronon/spark/Analyzer.scala (1)
34-37: Verify the impact of switching to Apache DataSketches.The transition from
com.yahootoorg.apache.datasketchesis a significant change that could affect the behavior and performance of sketching operations. While Apache DataSketches is well-maintained, we should ensure compatibility and performance characteristics remain acceptable.✅ Verification successful
Let me gather more information about the usage of DataSketches to better understand the impact of this change.
Based on the search results, I can now provide a conclusive response about the package transition:
The transition to
org.apache.datasketchesis properly implemented and safeThe codebase shows a complete and consistent transition to Apache DataSketches with no remaining
com.yahooimports. The implementation:
- Has proper serialization support via
ItemsSketchKryoSerializer- Maintains the same functionality with
ItemsSketch,ArrayOfStringsSerDe, andErrorType- Includes test coverage (
FrequentItemsTest.scala)- Shows consistent usage across multiple components (Analyzer, SimpleAggregators)
The change is a straightforward package rename without behavioral modifications, as evidenced by:
- Same class names and method signatures being used
- Identical error type handling (
ErrorType.NO_FALSE_POSITIVES)- Consistent serialization mechanisms
- No performance-related TODOs or FIXMEs introduced
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check for any remaining com.yahoo imports that might have been missed rg "com\.yahoo" --type scala # Look for any performance-related TODO/FIXME comments rg -i "TODO.*perform|FIXME.*perform" --type scalaLength of output: 82
Script:
#!/bin/bash # Check for any usage of ItemsSketch and related classes rg "ItemsSketch|ArrayOfStringsSerDe|ErrorType" -A 3 --type scala # Look for any test files that might validate the sketching behavior fd "test.*scala$" --exec grep -l "ItemsSketch|ArrayOfStringsSerDe|ErrorType" {} # Check if there are any configuration parameters related to sketching rg "sketch|frequency" --type scala --type conf --type propertiesLength of output: 16518
aggregator/src/main/scala/ai/chronon/aggregator/base/SimpleAggregators.scala (2)
21-30: LGTM: Import changes align with library transition.The transition from
com.yahootoorg.apache.datasketchesis implemented correctly with all necessary imports.
680-680: LGTM: Proper use of factory method for KllFloatsSketch instantiation.The change to use
KllFloatsSketch.newHeapInstance(k)instead of constructor instantiation follows the recommended pattern for Apache DataSketches library.Also applies to: 696-696
spark/src/main/scala/ai/chronon/spark/TableUtils.scala (2)
384-384: LGTM: Enhanced SQL query visibility in logs.The addition of yellow color formatting improves the visibility of SQL queries in the logs.
878-883: Verify usage of the new partitionRange method.Let's verify if this new method is being used consistently across the codebase.
✅ Verification successful
Based on the search results, I can now generate the final response:
The
partitionRangemethod is being used appropriately and consistently.The search results show that the
partitionRangemethod is being used in a consistent manner across the codebase:
- Primary usage in
PartitionRunner.scalafor input/output table partition range determination- Used in
GroupBy.scalafor range intersection and data profile computation- Used in
Join.scalafor bootstrap set computation and partition range validation- Used in various test classes with proper validation
The method is well integrated with the codebase's partition handling logic and follows the expected pattern of using
PartitionRangeobjects for partition boundary management.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Find all usages of partitionRange method to ensure proper integration # Test: Search for method calls. Expect: Calls to partitionRange with proper error handling # Search for direct method calls rg -A 5 'partitionRange\(' # Search for PartitionRange construction to compare with new method rg -A 5 'PartitionRange\('Length of output: 36588
spark/src/test/scala/ai/chronon/spark/test/MutationsTest.scala (1)
283-286: LGTM! Robust null handling in SQL aggregations.The addition of COALESCE functions improves the robustness of the aggregations by explicitly handling null values:
COALESCE(SUM(IF($includeCondition, mutations.rating, 0)), 0) COALESCE(COUNT(IF($includeCondition, mutations.rating, NULL)), 0)Let's verify the null handling with the following test:
✅ Verification successful
Let me gather more context about the test cases to verify the null handling implementation.
This will help us understand:
- How the test data is set up with ratings including null values
- The assertions that verify the calculations work correctly with null values
- The complete context around the rating aggregations
Based on the test cases and code analysis, I can now provide a final response regarding the null handling in the SQL aggregations.
Null handling in aggregations is correctly implemented and thoroughly tested
The test cases demonstrate that the COALESCE wrapping of SUM and COUNT aggregations is working as intended:
testNoSnapshotJustMutationexplicitly tests null scenarios:
- Verifies null handling when no data exists before mutations
- Contains assertions for
Row(2, millis("2021-04-10 00:07:00"), 1, null, "2021-04-10")The implementation correctly handles three null scenarios:
- When no ratings exist (returns null)
- When aggregating with IF conditions (converts nulls to 0 for sums)
- When counting with IF conditions (uses NULL in COUNT for proper counting)
The SQL implementation:
COALESCE(SUM(IF($includeCondition, mutations.rating, 0)), 0) AS rating_add_sum, COALESCE(COUNT(IF($includeCondition, mutations.rating, NULL)), 0) AS rating_add_cntProperly distinguishes between:
- Using 0 for SUM to avoid affecting sums
- Using NULL for COUNT to avoid counting non-matching rows
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check if there are any test cases specifically testing null scenarios # Expect: Test cases that validate null value handling rg -A 5 "null" spark/src/test/scala/ai/chronon/spark/test/MutationsTest.scalaLength of output: 1322
Script:
#!/bin/bash # Check the test data setup and assertions around the rating calculations rg -B 5 -A 10 "rating" spark/src/test/scala/ai/chronon/spark/test/MutationsTest.scalaLength of output: 30479
api/src/main/scala/ai/chronon/api/Extensions.scala (2)
46-48: LGTM: TimeUnit MINUTES support added correctly.The implementation for MINUTES case is consistent with other time units and uses correct conversion factors.
Also applies to: 53-55
113-115: LGTM: Table naming methods follow consistent pattern.The new table naming methods follow the established pattern and maintain consistency with the existing codebase.
Let's verify the usage of these new table names:
✅ Verification successful
Let me gather more context about the usage of these table names by examining the actual code references.
The previous results show that these table naming patterns are used across different modules, but let's examine the actual implementation context to ensure the new methods align with existing usage patterns.
Based on the gathered evidence, I can now generate the final response:
The new table naming methods are correctly implemented and align with existing patterns
The verification shows that:
- The new methods (
summaryTable,packedSummaryTable,driftTable) follow the same naming convention as other existing table methods inExtensions.scala- The
_driftsuffix is consistently used in the codebase, particularly in the drift analysis module- The implementation aligns with other table naming patterns like
_logged,_labels,_labeled, etc.- The summary table pattern is already established in the codebase with similar usages like
_consistency_summary🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for references to the new table types # Test: Search for summary, packed summary, and drift table references echo "Checking summary table references:" rg -l "summary_table|summary_packed|_drift" --type scala # Test: Search for specific table name patterns echo "Checking table name patterns:" rg "_summary$|_summary_packed$|_drift$" --type scalaLength of output: 583
Script:
#!/bin/bash # Check the actual usage context of these table names echo "Checking Extensions.scala implementation:" rg -A 5 -B 5 "summaryTable|packedSummaryTable|driftTable" api/src/main/scala/ai/chronon/api/Extensions.scala echo "\nChecking drift-related usage:" rg -A 5 -B 5 "_drift" spark/src/main/scala/ai/chronon/spark/stats/drift/Expressions.scala echo "\nChecking summary table usage in API:" rg -A 5 -B 5 "_consistency_summary" online/src/main/scala/ai/chronon/online/Api.scala cloud_gcp/src/main/scala/ai/chronon/integrations/cloud_gcp/GcpApiImpl.scalaLength of output: 3955
spark/src/main/scala/ai/chronon/spark/stats/drift/DistanceMetrics.scala (2)
3-3: Verify the import ofai.chronon.spark.Extensions.ArrayOps.Ensure that the
ArrayOpsextension methods are used within this file. If not, the import may be unnecessary.Run the following script to check for usages of
ArrayOpsmethods:✅ Verification successful
Let me gather more information to verify if the
ArrayOpsextension methods are actually being used in the file.
Based on the examination of both files, I can now provide the final response:
The
ArrayOpsimport is necessary and used in the codebaseThe import is used within the
JensenShannonDivergenceCalculatorobject, specifically in thecomputePdfsmethod where it calls theuniqSortextension method on an array:val breaks = (a ++ b).uniqSort(Ordering.Double)This
uniqSortmethod is defined inExtensions.ArrayOpsas shown in the search results, and it provides functionality to sort and deduplicate array elements using a TreeSet.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for usages of ArrayOps extension methods in the file. # Test: Search for methods from ArrayOps. Expect: Usages if extension methods are utilized. rg --context 2 'ArrayOps' spark/src/main/scala/ai/chronon/spark/stats/drift/DistanceMetrics.scalaLength of output: 194
Script:
#!/bin/bash # First, let's check the content of the Extensions.ArrayOps to understand what methods it provides rg -l "object Extensions" --type scala rg -A 10 "ArrayOps" --type scala # Then, let's examine the full content of DistanceMetrics.scala to look for usage patterns cat spark/src/main/scala/ai/chronon/spark/stats/drift/DistanceMetrics.scalaLength of output: 7492
109-136:⚠️ Potential issueSimplify and correct the
computePDFfunction logic.The current implementation of
computePDFis complex and may not handle all edge cases correctly. Specifically, the use of mutable variables and intricate control flow can lead to errors.Consider simplifying the function using a more functional approach:
def computePDF(percentiles: Array[Double], breaks: Array[Double]): Array[Double] = { - // Existing complex logic + val counts = breaks.sliding(2).map { + case Array(lower, upper) => + percentiles.count(value => value > lower && value <= upper) + }.toArray + val total = counts.sum.toDouble + counts.map(_ / total) }This approach counts the number of percentile values within each interval defined by
breaksand then normalizes the counts to obtain the PDF.Likely invalid or redundant comment.
spark/src/main/scala/ai/chronon/spark/utils/PartitionRunner.scala (2)
40-40: EnsurepartitionSpec.minushandlesOption[Window]parameters correctlyIn lines 40, 54, and 55, the
partitionSpec.minusmethod is called with anOption[Window]parameter (e.g.,lag,inputLookBack). Verify that theminusmethod is designed to acceptOption[Window]and handleNonevalues appropriately. If not, you may need to handleNonecases explicitly before calling the method.To confirm, you can check if
partitionSpec.minushas the following signature:def minus(partition: String, windowOpt: Option[Window]): StringIf it does not support
Option[Window], consider modifying the calls:val holeMinusLag = lag.map(partitionSpec.minus(hole, _)).getOrElse(hole)Also applies to: 54-55
77-77:⚠️ Potential issueReview logic for computing
outputPartitionsToComputeThe calculation of
outputPartitionsToComputeusesinputPartitions.toSet, which may not accurately represent the set of output partitions to compute. It's worth verifying whether this logic aligns with the intended behavior. ShouldoutputPartitionsToComputebe derived from potential output partitions rather than input partitions?aggregator/src/test/scala/ai/chronon/aggregator/test/DataGen.scala (7)
38-42: LGTM: AddedrollFloatmethod is correctly implementedThe
rollFloatmethod correctly generates randomFloatvalues within the specified range, with nulls interspersed according to thenullRate. This aligns with the existing pattern used inrollDouble.
148-152: LGTM: AddedFloatStreamclass is correctly implementedThe
FloatStreamclass correctly generates randomFloatvalues within the specified range, with nulls interspersed according to thenullRate. This addition extends the data generation capabilities appropriately.
171-171: UpdategenImplmethod to handlenullRateparameterThe updated signature of
genImplnow includesnullRate, allowing for more granular control over null value generation for different data types. This enhancement improves flexibility in data generation.
180-180: LGTM: Added support forFloatTypeingenImplThe addition of handling for
FloatTypeensures that float data can now be generated, enhancing the utility of the data generation framework.
187-191: Verify null rate handling inMapTypekey and value streamsIn the
MapTypehandling, thekeyStreamis generated with anullRateof0, meaning it will not producenullkeys, while thevalueStreamuses the providednullRate. Confirm that this behavior is intentional and aligns with the requirements, as some scenarios may require allowingnullkeys.
196-196: Updategenmethod to includenullRateparameterPassing the
nullRatetogenImplensures that null value generation is consistently applied across different data types during data generation.
28-28: Verify the impact of removing covariance fromCStream[T]Changing the class signature from
CStream[+T]toCStream[T]removes covariance. This may affect howCStreaminstances are used throughout the codebase, potentially leading to type incompatibility issues where covariance was relied upon. Ensure that this change does not introduce any type errors in places whereCStreamwith covariance was expected.Run the following script to identify usages of
CStreamwhere covariance might be affected:✅ Verification successful
Let me analyze the results. The search shows that all usages of
CStreamare within the same test fileDataGen.scala. Looking at the usage patterns:
- The base class is defined as
CStream[T: ClassTag]- All implementations (subclasses) use
CStream[Any]- The
zipChunkmethod takes anotherCStream[Other]but returnsCStream[Any]- The
ZippedStreamclass takes varargs ofCStream[Any]and returnsCStream[TestRow]Let me verify if there are any other files that might be using this class.
Let me check how
CStreamis used in these other files to ensure we have a complete picture.
Based on the comprehensive analysis of the codebase, I can now provide the final response:
Removing covariance from
CStream[T]is safe in this codebaseThe removal of covariance (
+T) fromCStream[T]does not impact the codebase because:
- All implementations of
CStreamuseCStream[Any]orCStream[TestRow]- The class is only used in test code, as evidenced by its location and usage patterns
- All usages are direct instantiations or method calls where type parameters are explicitly specified
- There are no cases where covariant type relationships are being leveraged in the inheritance hierarchy
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Find all usages of CStream with type parameters that may rely on covariance. # Search for CStream usages with type parameters. rg 'CStream\[[^\]]+\]' -t scalaLength of output: 2184
Script:
#!/bin/bash # Search for any imports or references to CStream in other files rg "CStream" -lLength of output: 461
Script:
#!/bin/bash # Get context around CStream usage in other files rg "CStream" -B 2 -A 2 --no-filename \ spark/src/test/scala/ai/chronon/spark/test/GroupByTest.scala \ spark/src/test/scala/ai/chronon/spark/test/DataFrameGen.scala \ aggregator/src/test/scala/ai/chronon/aggregator/test/TwoStackLiteAggregatorTest.scala \ aggregator/src/test/scala/ai/chronon/aggregator/test/SawtoothAggregatorTest.scala \ aggregator/src/test/scala/ai/chronon/aggregator/test/SawtoothOnlineAggregatorTest.scalaLength of output: 3052
spark/src/main/scala/ai/chronon/spark/stats/drift/Expressions.scala (1)
114-141: Ensure all metric types are properly handled inmetricPopulatorFunc.As new metrics are added, it's important that
metricPopulatorFuncis updated to include them to prevent runtime exceptions.Consider implementing unit tests that validate
metricPopulatorFunchandles all definedMetricNamevalues.Do you want me to help create these unit tests or open an issue to track this?
api/thrift/api.thrift (4)
224-227: NewCardinalityenum addition looks goodThe
Cardinalityenum is correctly defined with valuesLOWandHIGH.
296-301:TileKeystruct definition is correctThe
TileKeystruct is properly defined with appropriate field numbering and data types.
303-316:TileSummariesstruct definition looks goodThe
TileSummariesstruct is well-defined with clear documentation for each field.
318-322:DriftSpecstruct definition is appropriateThe
DriftSpecstruct is correctly defined with necessary fields for drift analysis.
spark/src/main/scala/ai/chronon/spark/stats/drift/SummaryUploader.scala
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (1)
spark/src/main/scala/ai/chronon/spark/stats/drift/SummaryUploader.scala (1)
18-46: Consider adding transaction-like semantics.The current implementation might leave the system in an inconsistent state if it fails after writing some completion markers but not others.
Consider:
- Writing completion markers only after successful upload to KVStore
- Implementing a rollback mechanism for partial failures
- Using atomic operations where possible
Would you like help implementing these improvements?
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (1)
- spark/src/main/scala/ai/chronon/spark/stats/drift/SummaryUploader.scala (1 hunks)
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🔇 Additional comments (2)
spark/src/main/scala/ai/chronon/spark/stats/drift/SummaryUploader.scala (2)
1-10: LGTM! Imports are well-organized and necessary.The imports are logically grouped and all are utilized in the implementation.
11-16:⚠️ Potential issueValidate the input DataFrame schema.
While the completed_schema is well-defined, there's no validation of the input
summaryDFschema. The code assumes the presence of specific columns ("keyBytes", "valueBytes", and partition column) without validation.Let's verify the schema requirements:
Add schema validation at initialization:
class SummaryUploader(summaryDF: DataFrame, kvStore: KVStore)(implicit tu: TableUtils) { // Validate required columns private val requiredColumns = Seq("keyBytes", "valueBytes", tu.partitionColumn) require( requiredColumns.forall(summaryDF.columns.contains), s"Missing required columns: ${requiredColumns.filterNot(summaryDF.columns.contains).mkString(", ")}" ) val completed_schema: types.StructType = // ... rest of the code✅ Verification successful
Let me gather more information about the schema requirements by checking the Summarizer class and related tests since they seem most relevant to our context.
This will help us understand:
- How these columns are used in the Summarizer class that likely creates the summaryDF
- How these columns are used in tests, which often reveal the expected schema and usage patterns
Based on the findings from Summarizer.scala, I can now generate the final response since we have clear evidence of the required schema.
Schema validation suggestion is correct but needs refinement
The review comment's concern about schema validation is valid, but the suggested implementation needs refinement based on the actual schema requirements found in Summarizer.scala. The complete schema requires:
partitionColumn(StringType, non-nullable)timestamp(LongType, non-nullable)keyBytes(BinaryType, non-nullable)valueBytes(BinaryType, non-nullable)Here's the corrected schema validation:
class SummaryUploader(summaryDF: DataFrame, kvStore: KVStore)(implicit tu: TableUtils) { // Validate required columns and their types private val requiredSchema = types.StructType(Seq( types.StructField(tu.partitionColumn, types.StringType, nullable = false), types.StructField("timestamp", types.LongType, nullable = false), types.StructField("keyBytes", types.BinaryType, nullable = false), types.StructField("valueBytes", types.BinaryType, nullable = false) )) require( summaryDF.schema.fields.toSet == requiredSchema.fields.toSet, s"Schema mismatch. Expected: ${requiredSchema.treeString}, Got: ${summaryDF.schema.treeString}" ) val completed_schema: types.StructType = // ... rest of the code🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for references to these column names to confirm schema requirements rg -l "keyBytes|valueBytes" | grep -v "SummaryUploader.scala"Length of output: 1747
Script:
#!/bin/bash # Check Summarizer.scala for schema details echo "=== Summarizer.scala ===" rg -A 10 "keyBytes|valueBytes" "spark/src/main/scala/ai/chronon/spark/stats/drift/Summarizer.scala" echo -e "\n=== Tests ===" # Check test files for schema usage rg -A 10 "keyBytes|valueBytes" "spark/src/test/scala/ai/chronon/spark/test/"Length of output: 7067
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This pr contains a notebook with code to 1. Generate a schema for a training set 2. Generate dummy data against that schema 3. Inject null rate spike and data drift anomalies 4. implement cardinality estimation 5. implement summarization logic based on high vs. low cardinalities 6. compute drift on summaries This PR also contains some book keeping changes 1. change discord to slack as the primary communication channel for the OSS chronon channel 2. Edit build scripts to remove non 2.12 builds - 2.12 is the only version that is supported across modern spark and flink. 3. pull in unit test fixes - [x] Added Unit Tests - [ ] Covered by existing CI - [ ] Integration tested - [ ] Documentation update <!-- This is an auto-generated comment: release notes by coderabbit.ai --> - **New Features** - Added functionality for enhanced data drift analysis and statistical distance calculations. - Implemented methods for generating and handling partition ranges and summarization in Spark. - Expanded API with new enumerations and structures for drift metrics. - Introduced new methods for generating synthetic data for fraud detection models. - Enhanced capabilities for user-defined aggregate functions (UDAFs) in Spark. - Introduced a new utility class for managing and computing missing partitions in data workflows. - **Bug Fixes** - Corrected dependency versions and improved error handling in various components. - **Documentation** - Expanded documentation on monitoring systems for ML and AI data, detailing drift computation. - **Tests** - Added comprehensive test suites for UDAFs, data summarization, and drift analysis functionalities. - Enhanced test coverage for various scenarios, including error handling and logging improvements. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: dependabot[bot] <[email protected]> Co-authored-by: Piyush Narang <[email protected]> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: ken-zlai <[email protected]> Co-authored-by: Chewy Shaw <[email protected]> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: chewy-zlai <[email protected]> Co-authored-by: ezvz <[email protected]>
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Actionable comments posted: 6
🧹 Outside diff range and nitpick comments (6)
quickstart/mongo-online-impl/src/main/scala/ai/chronon/quickstart/online/MongoLoggingDumper.scala (2)
Line range hint 1-54: Consider enhancing error handling and performance configurations.
The code would benefit from:
- Error handling for MongoDB operations and data transformations
- Configuration options for performance tuning, especially for large datasets
Consider applying these improvements:
object MongoLoggingDumper {
def main(args: Array[String]): Unit = {
if (args.length != 2) {
println("Usage: MongoLoggingDumper <tablename> <uri>")
sys.exit(1)
}
val tableName = args(0)
val uri = args(1)
val spark = SparkSession.builder()
.appName(s"MongoLoggingDumper")
.config("spark.mongodb.read.connection.uri", uri)
+ // Performance tuning configs
+ .config("spark.mongodb.read.batchSize", "1000")
+ .config("spark.mongodb.input.partitioner", "MongoSamplePartitioner")
+ .config("spark.mongodb.input.partitionerOptions.partitionKey", "_id")
+ .config("spark.mongodb.input.partitionerOptions.partitionSizeMB", "64")
.getOrCreate()
+ try {
val df = spark.read
.format("mongodb")
.option("database", Constants.mongoDatabase)
.option("collection", Constants.mongoLoggingCollection)
.load()
df.createOrReplaceTempView("temp_view")
df.printSchema()
val transformedDF = spark.sql(
s"""
| SELECT
| schemaHash AS schema_hash,
| BASE64(keyBytes) AS key_base64,
| BASE64(valueBytes) AS value_base64,
| atMillis AS ts_millis,
| ts AS ts,
| joinName AS name,
| FROM_UNIXTIME(ts / 1000, 'yyyy-MM-dd') AS ds
| FROM temp_view
| """.stripMargin)
transformedDF.printSchema()
transformedDF.write
.partitionBy("ds", "name")
.format("parquet")
.mode("overwrite")
.saveAsTable(tableName)
+ } catch {
+ case e: Exception =>
+ println(s"Error during data processing: ${e.getMessage}")
+ throw e
+ } finally {
+ if (spark != null) {
spark.stop()
+ }
+ }
}
}MongoDB connection settings require additional configuration for production readiness
The code currently only specifies the basic MongoDB connection URI without any timeout, retry, or error handling configurations. To ensure production reliability:
-
Add the following MongoDB connection configurations:
spark.mongodb.read.maxTimeMSspark.mongodb.read.retryReadsspark.mongodb.read.serverSelectionTimeoutMS
-
Implement error handling around the MongoDB operations to handle connection failures gracefully.
-
The MongoDB credentials and connection settings should be managed through a secure configuration system rather than passing directly as command-line arguments.
🔗 Analysis chain
Line range hint 1-54: Verify MongoDB connection settings in production environment.
Since this code handles data transfer between MongoDB and Hive, ensure that:
- MongoDB connection settings (timeout, retry policy) are properly configured
- The MongoDB user has appropriate permissions
- Network connectivity between the Spark cluster and MongoDB is stable
Let's verify the MongoDB configuration:
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check for MongoDB connection configuration files
rg -A 5 "mongodb.*connection" --type yaml --type conf --type properties
# Look for any MongoDB-related environment variables in deployment files
rg -A 5 "MONGO.*URI|MONGODB.*URI" --type yaml --type properties
Length of output: 203
Script:
#!/bin/bash
# Search for MongoDB configuration patterns in all files
rg -A 5 "mongodb.*connection|mongo.*config"
# Look for any MongoDB-related settings in the codebase
rg -A 5 "MONGO.*URI|MONGODB.*URI"
# Search for any MongoDB connection-related code patterns
ast-grep --pattern 'config("spark.mongodb.$_", $_)'
Length of output: 1771
Script:
#!/bin/bash
# Check for any MongoDB configuration constants or settings
rg -A 3 "Constants\.mongo"
# Look for any timeout or retry configurations in the codebase
rg "spark\.mongodb\.(read|write)\.(maxTimeMS|retryWrites|retryReads|serverSelectionTimeoutMS)"
# Search for any MongoDB-related error handling or connection management
ast-grep --pattern 'try {
$$$
.format("mongodb")
$$$
} catch {
$$$
}'
Length of output: 5276
api/src/main/scala/ai/chronon/api/PartitionSpec.scala (1)
76-80: Consider adding ScalaDoc for the companion object.
While the predefined specifications are self-explanatory, adding documentation about their intended use cases and examples would improve maintainability.
Add documentation like:
+/**
+ * Companion object providing commonly used partition specifications.
+ *
+ * Example usage:
+ * {{{
+ * val spec = PartitionSpec.hourly
+ * val partition = spec.at(System.currentTimeMillis())
+ * }}}
+ */
object PartitionSpec {spark/src/test/scala/ai/chronon/spark/test/udafs/UDAFSQLUsageTest.scala (1)
17-17: Consider making the Spark session private.
The spark variable should be marked as private to encapsulate the test infrastructure.
- var spark: SparkSession = SparkSessionBuilder.build("udaf_sql", local = true)
+ private var spark: SparkSession = SparkSessionBuilder.build("udaf_sql", local = true)build.sbt (2)
Line range hint 175-186: Remove duplicate overwatch project definition.
The overwatch project is defined twice with identical configuration. Remove one of the duplicate definitions.
-lazy val overwatch = project
- .dependsOn(aggregator.%("compile->compile;test->test"), online)
- .settings(
- libraryDependencies ++= spark_all,
- libraryDependencies ++= flink_all
- )Line range hint 1-281: Consider implementing a more structured dependency management approach.
The build configuration could benefit from:
- Centralizing version management using a dedicated versions.sbt file
- Creating shared dependency sets for common configurations
- Implementing a consistent strategy for "provided" scopes across projects
- Adding explicit cross-version support declarations for critical dependencies
Consider creating a project/Dependencies.scala:
object Dependencies {
object Versions {
val spark = "3.5.1"
val flink = "1.17.0"
// ... other versions
}
val sparkDeps = Seq(
"org.apache.spark" %% "spark-sql",
"org.apache.spark" %% "spark-core"
).map(_ % Versions.spark)
// ... other dependency groups
}📜 Review details
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📒 Files selected for processing (10)
- aggregator/src/test/scala/ai/chronon/aggregator/test/DataGen.scala (9 hunks)
- aggregator/src/test/scala/ai/chronon/aggregator/test/TwoStackLiteAggregatorTest.scala (2 hunks)
- api/py/python-api-build.sh (2 hunks)
- api/src/main/scala/ai/chronon/api/PartitionSpec.scala (2 hunks)
- build.sbt (4 hunks)
- flink/src/test/scala/ai/chronon/flink/test/SparkExpressionEvalFnTest.scala (1 hunks)
- quickstart/mongo-online-impl/src/main/scala/ai/chronon/quickstart/online/MongoLoggingDumper.scala (1 hunks)
- quickstart/mongo-online-impl/src/main/scala/ai/chronon/quickstart/online/Spark2MongoLoader.scala (0 hunks)
- spark/src/main/scala/ai/chronon/spark/ChrononKryoRegistrator.scala (4 hunks)
- spark/src/test/scala/ai/chronon/spark/test/udafs/UDAFSQLUsageTest.scala (1 hunks)
💤 Files with no reviewable changes (1)
- quickstart/mongo-online-impl/src/main/scala/ai/chronon/quickstart/online/Spark2MongoLoader.scala
✅ Files skipped from review due to trivial changes (1)
- flink/src/test/scala/ai/chronon/flink/test/SparkExpressionEvalFnTest.scala
🚧 Files skipped from review as they are similar to previous changes (3)
- aggregator/src/test/scala/ai/chronon/aggregator/test/TwoStackLiteAggregatorTest.scala
- api/py/python-api-build.sh
- spark/src/main/scala/ai/chronon/spark/ChrononKryoRegistrator.scala
🔇 Additional comments (19)
quickstart/mongo-online-impl/src/main/scala/ai/chronon/quickstart/online/MongoLoggingDumper.scala (1)
52-52: LGTM: Good addition of Spark session cleanup!
Adding spark.stop() is a good practice to ensure proper cleanup of Spark resources.
api/src/main/scala/ai/chronon/api/PartitionSpec.scala (3)
50-53: Previous suggestion about Option[String] still applies.
The current implementation still uses explicit null handling, which could be improved using Scala's Option type.
61-64: Previous suggestion about window validation still applies.
The method should validate the window duration.
62-62:
Consider handling potential overflow in count calculation.
For very large windows, the calculation window.millis.toDouble / spanMillis could result in a number larger than Int.MaxValue, leading to unexpected behavior.
Let's verify the maximum possible window size:
Consider adding a guard:
-val count = math.ceil(window.millis.toDouble / spanMillis).toInt
+val count = math.min(
+ math.ceil(window.millis.toDouble / spanMillis).toInt,
+ Int.MaxValue - 1
+)spark/src/test/scala/ai/chronon/spark/test/udafs/UDAFSQLUsageTest.scala (3)
1-14: LGTM! Well-organized imports and package structure.
The imports are comprehensive and properly organized, covering all necessary dependencies for Spark SQL testing.
77-84: LGTM! Comprehensive assertions with proper null handling.
The test assertions thoroughly validate:
- Correct aggregation of counts
- Proper null handling in different contexts
- Expected output formats
Also applies to: 94-101, 111-118
69-84: 🛠️ Refactor suggestion
Consider adding edge cases and error condition tests.
While the current tests cover basic functionality and null handling well, consider adding:
- Edge cases:
- Empty groups
- Very large maps/arrays
- Special characters in strings
- Error conditions:
- Invalid data types
- Malformed input
Let's check if these UDAFs have error handling tests elsewhere:
Also applies to: 86-101, 103-118
build.sbt (2)
81-81: LGTM: Valid addition of flink-clients dependency.
The addition of flink-clients is consistent with the project's Flink version (1.17.0).
109-109: Verify compatibility with Apache DataSketches upgrade.
The migration from Yahoo DataSketches (0.13.4) to Apache DataSketches (6.1.0) represents a major version change with potential breaking changes in the API.
✅ Verification successful
Let me analyze the code usage to verify the compatibility.
Migration to Apache DataSketches appears compatible
The codebase has been properly migrated to use the Apache DataSketches package. Key observations:
- All imports use
org.apache.datasketcheswith no remaining references tocom.yahoo.datasketches - Core functionality usage (CpcSketch, KllFloatsSketch, ItemsSketch) follows the Apache DataSketches API
- Method calls like
getFrequentItems,getEstimate, andgetQuantilesalign with the current Apache DataSketches API
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for any remaining references to the old Yahoo DataSketches package
rg "com\.yahoo\.datasketches" --type scala
# Search for common DataSketches class usages to verify migration
rg "import org\.apache\.datasketches\." --type scala -A 3
Length of output: 5590
Script:
#!/bin/bash
# Search for actual usage patterns of DataSketches classes to verify API compatibility
ast-grep --pattern 'new KllFloatsSketch'
ast-grep --pattern 'new CpcSketch'
ast-grep --pattern 'new ItemsSketch'
# Look for any serialization/deserialization code that might be affected
rg "sketch\.(ser|des|get)" --type scala -A 2
Length of output: 1841
aggregator/src/test/scala/ai/chronon/aggregator/test/DataGen.scala (10)
54-56: Preserve type safety in chunk method's return type
Changing the return type of chunk from CStream[Seq[T]] to CStream[Any] reduces type safety and may lead to runtime type casting issues.
68-82: Preserve type information in zipChunk method
The zipChunk method returns CStream[Any], which obscures the actual data type. Updating it to CStream[Map[T, Other]] enhances type safety and usability.
108-108: Preserve type parameter in PartitionStream
Extending CStream[Any] instead of CStream[String] reduces type safety. Since this stream generates String values, extending CStream[String] maintains correct type information.
113-113: Preserve type parameter in StringStream
Changing to CStream[Any] reduces type safety. Extending CStream[String] maintains specific type information and compile-time checks.
124-124: Preserve type parameter in TimeStream
Extending CStream[Any] instead of CStream[Long] reduces type safety. Since timestamps are Long values, extending CStream[Long] maintains clarity and type safety.
134-134: Preserve type parameter in IntStream
Extending CStream[Any] instead of CStream[Integer] reduces type safety. Extending CStream[Integer] ensures correct type information.
139-139: Preserve type parameter in LongStream
Extending CStream[Any] instead of CStream[JLong] reduces type safety. Extending CStream[JLong] maintains correct type information.
144-144: Preserve type parameter in DoubleStream
Extending CStream[Any] instead of CStream[JDouble] reduces type safety. Extending CStream[JDouble] maintains correct type information.
Line range hint 172-192: Maintain type safety in genImpl method's return type
Returning CStream[Any] reduces type safety. Adjusting the method to return specific CStream[T] types enhances clarity and compile-time checks.
197-197: Maintain type safety in gen method's return type
Returning CStream[Any] reduces type safety. Adjusting the method to return specific CStream[T] types enhances clarity and compile-time checks.
spark/src/test/scala/ai/chronon/spark/test/udafs/UDAFSQLUsageTest.scala
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aggregator/src/test/scala/ai/chronon/aggregator/test/DataGen.scala
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (1)
aggregator/src/test/scala/ai/chronon/aggregator/test/DataGen.scala (1)
Line range hint 172-181: Maintain type safety in the genImpl method
Returning CStream[Any] in genImpl reduces type safety and may lead to runtime casting issues. Consider parameterizing the return type to preserve the specific data type being generated.
Apply this diff to enhance type safety:
-def genImpl(dtype: DataType, partitionColumn: String, partitionSpec: PartitionSpec, nullRate: Double): CStream[Any] =
+def genImpl[T](dtype: DataType, partitionColumn: String, partitionSpec: PartitionSpec, nullRate: Double): CStream[T] =Then, update each case to return a CStream[T] with the appropriate type parameter based on dtype.
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📒 Files selected for processing (2)
- aggregator/src/test/scala/ai/chronon/aggregator/test/DataGen.scala (6 hunks)
- aggregator/src/test/scala/ai/chronon/aggregator/test/TwoStackLiteAggregatorTest.scala (2 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- aggregator/src/test/scala/ai/chronon/aggregator/test/TwoStackLiteAggregatorTest.scala
🔇 Additional comments (3)
aggregator/src/test/scala/ai/chronon/aggregator/test/DataGen.scala (3)
54-56: Preserve type safety in the chunk method's return type
Changing the return type of chunk from CStream[Seq[T]] to CStream[Any] reduces type safety and may lead to runtime type casting issues. Preserving the specific type CStream[Seq[T]] ensures compile-time checks and maintains clarity about the data being generated.
68-82: Preserve type information in the zipChunk method
The zipChunk method currently returns CStream[Any], which obscures the actual data type being generated. Since this method produces a Map[T, Other], updating the return type to CStream[Map[T, Other]] enhances type safety and usability.
94-96: Use Ordering[Long] to avoid unnecessary casting
By specifying Ordering[Long], you can eliminate the need to cast x and y to Long, improving type safety and readability.
aggregator/src/test/scala/ai/chronon/aggregator/test/DataGen.scala
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fetch to be called from zipline run outside of Driver.scala so that spark is not required
#306
## Summary Creates a Summary Uploader which uploads summary data to a KVStore. ## Checklist - [ x ] Added Unit Tests - [ ] Covered by existing CI - [ ] Integration tested - [ ] Documentation update <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Introduced `SummaryUploader` for uploading summary statistics to a key-value store. - Added `MockKVStore` for testing key-value store operations. - Implemented `KVStoreSemaphore` for managing concurrent access to resources. - **Enhancements** - Increased data volume in tests to improve testing scenarios. - Integrated `SummaryUploader` in `DriftTest` for uploading summary data during tests. - Enhanced control over concurrent reads and writes to DynamoDB with updated `DynamoDBKVStoreImpl`. - Refined error handling and flow in `multiPut` operations for better robustness. - Updated Spark dependency from `3.5.0` to `3.5.1` for improved stability. - Added a new constant `DriftStatsTable` for drift statistics. - **Bug Fixes** - Improved error handling for upload failures in `SummaryUploader`. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: dependabot[bot] <[email protected]> Co-authored-by: nikhil-zlai <[email protected]> Co-authored-by: Chewy Shaw <[email protected]> Co-authored-by: Piyush Narang <[email protected]> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: ken-zlai <[email protected]> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: ezvz <[email protected]>
## Summary Creates a Summary Uploader which uploads summary data to a KVStore. ## Checklist - [ x ] Added Unit Tests - [ ] Covered by existing CI - [ ] Integration tested - [ ] Documentation update <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Introduced `SummaryUploader` for uploading summary statistics to a key-value store. - Added `MockKVStore` for testing key-value store operations. - Implemented `KVStoreSemaphore` for managing concurrent access to resources. - **Enhancements** - Increased data volume in tests to improve testing scenarios. - Integrated `SummaryUploader` in `DriftTest` for uploading summary data during tests. - Enhanced control over concurrent reads and writes to DynamoDB with updated `DynamoDBKVStoreImpl`. - Refined error handling and flow in `multiPut` operations for better robustness. - Updated Spark dependency from `3.5.0` to `3.5.1` for improved stability. - Added a new constant `DriftStatsTable` for drift statistics. - **Bug Fixes** - Improved error handling for upload failures in `SummaryUploader`. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: dependabot[bot] <[email protected]> Co-authored-by: nikhil-zlai <[email protected]> Co-authored-by: Chewy Shaw <[email protected]> Co-authored-by: Piyush Narang <[email protected]> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: ken-zlai <[email protected]> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: ezvz <[email protected]>
## Summary Creates a Summary Uploader which uploads summary data to a KVStore. ## Cheour clientslist - [ x ] Added Unit Tests - [ ] Covered by existing CI - [ ] Integration tested - [ ] Documentation update <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Introduced `SummaryUploader` for uploading summary statistics to a key-value store. - Added `Moour clientsKVStore` for testing key-value store operations. - Implemented `KVStoreSemaphore` for managing concurrent access to resources. - **Enhancements** - Increased data volume in tests to improve testing scenarios. - Integrated `SummaryUploader` in `DriftTest` for uploading summary data during tests. - Enhanced control over concurrent reads and writes to DynamoDB with updated `DynamoDBKVStoreImpl`. - Refined error handling and flow in `multiPut` operations for better robustness. - Updated Spark dependency from `3.5.0` to `3.5.1` for improved stability. - Added a new constant `DriftStatsTable` for drift statistics. - **Bug Fixes** - Improved error handling for upload failures in `SummaryUploader`. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: dependabot[bot] <[email protected]> Co-authored-by: nikhil-zlai <[email protected]> Co-authored-by: Chewy Shaw <[email protected]> Co-authored-by: Piyush Narang <[email protected]> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: ken-zlai <[email protected]> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: ezvz <[email protected]>
Summary
Creates a Summary Uploader which uploads summary data to a KVStore.
Checklist
Summary by CodeRabbit
New Features
SummaryUploaderfor uploading summary statistics to a key-value store.MockKVStorefor testing key-value store operations.KVStoreSemaphorefor managing concurrent access to resources.Enhancements
SummaryUploaderinDriftTestfor uploading summary data during tests.DynamoDBKVStoreImpl.multiPutoperations for better robustness.3.5.0to3.5.1for improved stability.DriftStatsTablefor drift statistics.Bug Fixes
SummaryUploader.