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trait CDCReaderImpl extends DeltaLogging

Linear Supertypes
DeltaLogging, DatabricksLogging, DeltaProgressReporter, LoggingShims, Logging, AnyRef, Any
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CDCReader
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  1. CDCReaderImpl
  2. DeltaLogging
  3. DatabricksLogging
  4. DeltaProgressReporter
  5. LoggingShims
  6. Logging
  7. AnyRef
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Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    LoggingShims
  2. case class CDCVersionDiffInfo(fileChangeDf: DataFrame, numFiles: Long, numBytes: Long) extends Product with Serializable

    Represents the changes between some start and end version of a Delta table

    Represents the changes between some start and end version of a Delta table

    fileChangeDf

    contains all of the file changes (AddFile, RemoveFile, AddCDCFile)

    numFiles

    the number of AddFile + RemoveFile + AddCDCFiles that are in the df

    numBytes

    the total size of the AddFile + RemoveFile + AddCDCFiles that are in the df

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def cdcReadSchema(deltaSchema: StructType): StructType

    Append CDC metadata columns to the provided schema.

  6. def changesToBatchDF(deltaLog: DeltaLog, start: Long, end: Long, spark: SparkSession, readSchemaSnapshot: Option[Snapshot] = None, useCoarseGrainedCDC: Boolean = false, startVersionSnapshot: Option[SnapshotDescriptor] = None): DataFrame

    Get the block of change data from start to end Delta log versions (both sides inclusive).

    Get the block of change data from start to end Delta log versions (both sides inclusive). The returned DataFrame has isStreaming set to false.

    readSchemaSnapshot

    The snapshot with the desired schema that will be used to serve this CDF batch. It is usually passed upstream from e.g. DeltaTableV2 as an effort to stablize the schema used for the batch DF. We don't actually use its data. If not set, it will fallback to the legacy behavior of using whatever deltaLog.unsafeVolatileSnapshot is. This should be avoided in production.

  7. def changesToDF(readSchemaSnapshot: SnapshotDescriptor, start: Long, end: Long, changes: Iterator[(Long, Seq[Action])], spark: SparkSession, isStreaming: Boolean = false, useCoarseGrainedCDC: Boolean = false, startVersionSnapshot: Option[SnapshotDescriptor] = None): CDCVersionDiffInfo

    For a sequence of changes(AddFile, RemoveFile, AddCDCFile) create a DataFrame that represents that captured change data between start and end inclusive.

    For a sequence of changes(AddFile, RemoveFile, AddCDCFile) create a DataFrame that represents that captured change data between start and end inclusive.

    Builds the DataFrame using the following logic: Per each change of type (Long, Seq[Action]) in changes, iterates over the actions and handles two cases. - If there are any CDC actions, then we ignore the AddFile and RemoveFile actions in that version and create an AddCDCFile instead. - If there are no CDC actions, then we must infer the CDC data from the AddFile and RemoveFile actions, taking only those with dataChange = true.

    These buffers of AddFile, RemoveFile, and AddCDCFile actions are then used to create corresponding FileIndexes (e.g. TahoeChangeFileIndex), where each is suited to use the given action type to read CDC data. These FileIndexes are then unioned to produce the final DataFrame.

    readSchemaSnapshot

    - Snapshot for the table for which we are creating a CDF Dataframe, the schema of the snapshot is expected to be the change DF's schema. We have already adjusted this snapshot with the schema mode if there's any. We don't use its data actually.

    start

    - startingVersion of the changes

    end

    - endingVersion of the changes

    changes

    - changes is an iterator of all FileActions for a particular commit version. Note that for log files where InCommitTimestamps are enabled, the iterator must also contain the CommitInfo action.

    spark

    - SparkSession

    isStreaming

    - indicates whether the DataFrame returned is a streaming DataFrame

    useCoarseGrainedCDC

    - ignores checks related to CDC being disabled in any of the versions and computes CDC entirely from AddFiles/RemoveFiles (ignoring AddCDCFile actions)

    startVersionSnapshot

    - The snapshot of the starting version.

    returns

    CDCInfo which contains the DataFrame of the changes as well as the statistics related to the changes

  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  9. 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.

    Attributes
    protected
    Definition Classes
    DeltaLogging
  10. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  11. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  12. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  13. def getBatchSchemaModeForTable(spark: SparkSession, columnMappingEnabled: Boolean): DeltaBatchCDFSchemaMode

    Get the batch cdf schema mode for a table, considering whether it has column mapping enabled or not.

  14. def getCDCRelation(spark: SparkSession, snapshotToUse: Snapshot, isTimeTravelQuery: Boolean, conf: SQLConf, options: CaseInsensitiveStringMap): BaseRelation

    Get a Relation that represents change data between two snapshots of the table.

    Get a Relation that represents change data between two snapshots of the table.

    spark

    Spark session

    snapshotToUse

    Snapshot to use to provide read schema and version

    isTimeTravelQuery

    Whether this CDC scan is used in conjunction with time-travel args

    conf

    SQL conf

    options

    CDC specific options

  15. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  16. def getCommonTags(deltaLog: DeltaLog, tahoeId: String): Map[TagDefinition, String]
    Definition Classes
    DeltaLogging
  17. def getDeletedAndAddedRows(addFileSpecs: Seq[CDCDataSpec[AddFile]], removeFileSpecs: Seq[CDCDataSpec[RemoveFile]], deltaLog: DeltaLog, snapshot: SnapshotDescriptor, isStreaming: Boolean, spark: SparkSession): Seq[DataFrame]

    Generate CDC rows by looking at added and removed files, together with Deletion Vectors they may have.

    Generate CDC rows by looking at added and removed files, together with Deletion Vectors they may have.

    When DV is used, the same file can be removed then added in the same version, and the only difference is the assigned DVs. The base method does not consider DVs in this case, thus will produce CDC that *all* rows in file being removed then *some* re-added. The correct answer, however, is to compare two DVs and apply the diff to the file to get removed and re-added rows.

    Currently it is always the case that in the log "remove" comes first, followed by "add" -- which means that the file stays alive with a new DV. There's another possibility, though not make many senses, that a file is "added" to log then "removed" in the same version. If this becomes possible in future, we have to reconstruct the timeline considering the order of actions rather than simply matching files by path.

    Attributes
    protected
  18. def getErrorData(e: Throwable): Map[String, Any]
    Definition Classes
    DeltaLogging
  19. def getNonICTTimestampsByVersion(deltaLog: DeltaLog, start: Long, end: Long): Map[Long, Timestamp]

    Builds a map from commit versions to associated commit timestamps where the timestamp is the modification time of the commit file.

    Builds a map from commit versions to associated commit timestamps where the timestamp is the modification time of the commit file. Note that this function will not return InCommitTimestamps, it is up to the consumer of this function to decide whether the file modification time is the correct commit timestamp or whether they need to read the ICT.

    start

    start commit version

    end

    end commit version (inclusive)

  20. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  21. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  22. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  23. def isCDCEnabledOnTable(metadata: Metadata, spark: SparkSession): Boolean

    Determine if the metadata provided has cdc enabled or not.

  24. def isCDCRead(options: CaseInsensitiveStringMap): Boolean

    Based on the read options passed it indicates whether the read was a cdc read or not.

  25. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  26. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  27. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  28. def logConsole(line: String): Unit
    Definition Classes
    DatabricksLogging
  29. def logDebug(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    LoggingShims
  30. def logDebug(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    LoggingShims
  31. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  32. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  33. def logError(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    LoggingShims
  34. def logError(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    LoggingShims
  35. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  36. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  37. def logInfo(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    LoggingShims
  38. def logInfo(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    LoggingShims
  39. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  40. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  41. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  42. def logTrace(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    LoggingShims
  43. def logTrace(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    LoggingShims
  44. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  45. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  46. def logWarning(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    LoggingShims
  47. def logWarning(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    LoggingShims
  48. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  49. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  50. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  51. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  52. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  53. def processDeletionVectorActions(addFilesMap: Map[FilePathWithTableVersion, AddFile], removeFilesMap: Map[FilePathWithTableVersion, RemoveFile], versionToCommitInfo: Map[Long, CommitInfo], deltaLog: DeltaLog, snapshot: SnapshotDescriptor, isStreaming: Boolean, spark: SparkSession): Seq[DataFrame]
  54. 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 deltaLog is null.

    Attributes
    protected
    Definition Classes
    DeltaLogging
  55. 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
    protected
    Definition Classes
    DeltaLogging
  56. 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
    protected
    Definition Classes
    DeltaLogging
  57. def recordEvent(metric: MetricDefinition, additionalTags: Map[TagDefinition, String] = Map.empty, blob: String = null, trimBlob: Boolean = true): Unit
    Definition Classes
    DatabricksLogging
  58. def recordFrameProfile[T](group: String, name: String)(thunk: => T): T
    Attributes
    protected
    Definition Classes
    DeltaLogging
  59. 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
    Definition Classes
    DatabricksLogging
  60. def recordProductEvent(metric: MetricDefinition with CentralizableMetric, additionalTags: Map[TagDefinition, String] = Map.empty, blob: String = null, trimBlob: Boolean = true): Unit
    Definition Classes
    DatabricksLogging
  61. 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
    Definition Classes
    DatabricksLogging
  62. 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
  63. def scanIndex(spark: SparkSession, index: TahoeFileIndexWithSnapshotDescriptor, isStreaming: Boolean = false): DataFrame

    Build a dataframe from the specified file index.

    Build a dataframe from the specified file index. We can't use a DataFrame scan directly on the file names because that scan wouldn't include partition columns.

    It can optionally take a customReadSchema for the dataframe generated.

    Attributes
    protected
  64. def shouldSkipFileActionsInCommit(commitInfo: CommitInfo): Boolean

    Function to check if file actions should be skipped for no-op merges based on CommitInfo metrics.

    Function to check if file actions should be skipped for no-op merges based on CommitInfo metrics. MERGE will sometimes rewrite files in a way which *could* have changed data (so dataChange = true) but did not actually do so (so no CDC will be produced). In this case the correct CDC output is empty - we shouldn't serve it from those files. This should be handled within the command, but as a hotfix-safe fix, we check the metrics. If the command reported 0 rows inserted, updated, or deleted, then CDC shouldn't be produced.

  65. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  66. def toString(): String
    Definition Classes
    AnyRef → Any
  67. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  68. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  69. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  70. 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.

    Definition Classes
    DeltaProgressReporter

Inherited from DeltaLogging

Inherited from DatabricksLogging

Inherited from DeltaProgressReporter

Inherited from LoggingShims

Inherited from Logging

Inherited from AnyRef

Inherited from Any

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