trait StatisticsCollection extends DeltaLogging
A helper trait that constructs expressions that can be used to collect global and column level statistics for a collection of data, given its schema.
Global statistics (such as the number of records) are stored as top level columns. Per-column statistics (such as min/max) are stored in a struct that mirrors the schema of the data.
To illustrate, here is an example of a data schema along with the schema of the statistics that would be collected.
Data Schema:
|-- a: struct (nullable = true) | |-- b: struct (nullable = true) | | |-- c: long (nullable = true)
Collected Statistics:
|-- stats: struct (nullable = true) | |-- numRecords: long (nullable = false) | |-- minValues: struct (nullable = false) | | |-- a: struct (nullable = false) | | | |-- b: struct (nullable = false) | | | | |-- c: long (nullable = true) | |-- maxValues: struct (nullable = false) | | |-- a: struct (nullable = false) | | | |-- b: struct (nullable = false) | | | | |-- c: long (nullable = true) | |-- nullCount: struct (nullable = false) | | |-- a: struct (nullable = false) | | | |-- b: struct (nullable = false) | | | | |-- c: long (nullable = true)
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Abstract Value Members
- abstract def columnMappingMode: DeltaColumnMappingMode
The column mapping mode of the target delta table.
- abstract def outputAttributeSchema: StructType
The schema of the output attributes of the write queries that needs to collect statistics.
The schema of the output attributes of the write queries that needs to collect statistics. The partition columns' definitions are not included in this schema.
- abstract def outputTableStatsSchema: StructType
The output attributes (
outputAttributeSchema) that are replaced with table schema with the physical mapping information.The output attributes (
outputAttributeSchema) that are replaced with table schema with the physical mapping information. NOTE: The partition columns' definitions are not included in this schema. - abstract def protocol: Protocol
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- abstract def spark: SparkSession
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- abstract val statsColumnSpec: DeltaStatsColumnSpec
The statistic indexed column specification of the target delta table.
- abstract def tableSchema: StructType
The schema of the target table of this statistics collection.
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- def applyFuncToStatisticsColumn(statisticsSchema: StructType, statisticsColumn: Column)(function: PartialFunction[(Column, StructField), Option[Column]]): Seq[Column]
Traverses the statisticsSchema for the provided statisticsColumn and applies function to leaves.
Traverses the statisticsSchema for the provided statisticsColumn and applies function to leaves.
Note, for values that are outside the domain of the partial function we keep the original column. If the caller wants to drop the column needs to explicitly return None.
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- def deltaAssert(check: => Boolean, name: String, msg: String, deltaLog: DeltaLog = null, data: AnyRef = null, path: Option[Path] = None): Unit
Helper method to check invariants in Delta code.
Helper method to check invariants in Delta code. Fails when running in tests, records a delta assertion event and logs a warning otherwise.
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- def recordDeltaEvent(deltaLog: DeltaLog, opType: String, tags: Map[TagDefinition, String] = Map.empty, data: AnyRef = null, path: Option[Path] = None): Unit
Used to record the occurrence of a single event or report detailed, operation specific statistics.
Used to record the occurrence of a single event or report detailed, operation specific statistics.
- path
Used to log the path of the delta table when
deltaLogis null.
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- def recordDeltaOperation[A](deltaLog: DeltaLog, opType: String, tags: Map[TagDefinition, String] = Map.empty)(thunk: => A): A
Used to report the duration as well as the success or failure of an operation on a
deltaLog.Used to report the duration as well as the success or failure of an operation on a
deltaLog.- Attributes
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- def recordDeltaOperationForTablePath[A](tablePath: String, opType: String, tags: Map[TagDefinition, String] = Map.empty)(thunk: => A): A
Used to report the duration as well as the success or failure of an operation on a
tahoePath.Used to report the duration as well as the success or failure of an operation on a
tahoePath.- Attributes
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- def recordEvent(metric: MetricDefinition, additionalTags: Map[TagDefinition, String] = Map.empty, blob: String = null, trimBlob: Boolean = true): Unit
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- def recordFrameProfile[T](group: String, name: String)(thunk: => T): T
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- def recordOperation[S](opType: OpType, opTarget: String = null, extraTags: Map[TagDefinition, String], isSynchronous: Boolean = true, alwaysRecordStats: Boolean = false, allowAuthTags: Boolean = false, killJvmIfStuck: Boolean = false, outputMetric: MetricDefinition = METRIC_OPERATION_DURATION, silent: Boolean = true)(thunk: => S): S
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- def recordProductEvent(metric: MetricDefinition with CentralizableMetric, additionalTags: Map[TagDefinition, String] = Map.empty, blob: String = null, trimBlob: Boolean = true): Unit
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- def recordProductUsage(metric: MetricDefinition with CentralizableMetric, quantity: Double, additionalTags: Map[TagDefinition, String] = Map.empty, blob: String = null, forceSample: Boolean = false, trimBlob: Boolean = true, silent: Boolean = false): Unit
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- def recordUsage(metric: MetricDefinition, quantity: Double, additionalTags: Map[TagDefinition, String] = Map.empty, blob: String = null, forceSample: Boolean = false, trimBlob: Boolean = true, silent: Boolean = false): Unit
- Definition Classes
- DatabricksLogging
- lazy val statCollectionLogicalSchema: StructType
statCollectionLogicalSchema is the logical schema that is composed of all the columns that have the stats collected with our current table configuration.
- lazy val statCollectionPhysicalSchema: StructType
statCollectionPhysicalSchema is the schema that is composed of all the columns that have the stats collected with our current table configuration.
- lazy val statsCollector: Column
Returns a struct column that can be used to collect statistics for the current schema of the table.
Returns a struct column that can be used to collect statistics for the current schema of the table. The types we keep stats on must be consistent with DataSkippingReader.SkippingEligibleLiteral. If a column is missing from dataSchema (which will be filled with nulls), we will only collect the NULL_COUNT stats for it as the number of rows.
- lazy val statsSchema: StructType
Returns schema of the statistics collected.
- final def synchronized[T0](arg0: => T0): T0
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- def updateStatsToWideBounds(withStats: DataFrame, statsColName: String): DataFrame
Sets the TIGHT_BOUNDS column to false and converts the logical nullCount to a tri-state nullCount.
Sets the TIGHT_BOUNDS column to false and converts the logical nullCount to a tri-state nullCount. The nullCount states are the following: 1) For "all-nulls" columns we set the physical nullCount which is equal to the physical numRecords. 2) "no-nulls" columns remain unchanged, i.e. zero nullCount is the same for both physical and logical representations. 3) For "some-nulls" columns, we leave the existing value. In files with wide bounds, the nullCount in SOME_NULLs columns is considered unknown.
The file's state can transition back to tight when statistics are recomputed. In that case, TIGHT_BOUNDS is set back to true and nullCount back to the logical value.
Note, this function gets as input parsed statistics and returns a json document similarly to allFiles. To further match the behavior of allFiles we always return a column named
statsinstead of statsColName.- withStats
A dataFrame of actions with parsed statistics.
- statsColName
The name of the parsed statistics column.
- final def wait(): Unit
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- final def wait(arg0: Long, arg1: Int): Unit
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- def withStatusCode[T](statusCode: String, defaultMessage: String, data: Map[String, Any] = Map.empty)(body: => T): T
Report a log to indicate some command is running.
Report a log to indicate some command is running.
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- DeltaProgressReporter