Monitoring Structured Streaming queries on Azure Databricks

  • Article

Azure Databricks provides built-in monitoring for Structured Streaming applications through the Spark UI under the Streaming tab.

Distinguish Structured Streaming queries in the Spark UI

Provide your streams a unique query name by adding .queryName(<query-name>) to your writeStream code to easily distinguish which metrics belong to which stream in the Spark UI.

Push Structured Streaming metrics to external services

Streaming metrics can be pushed to external services for alerting or dashboarding use cases by using Apache Spark’s Streaming Query Listener interface. In Databricks Runtime 11.3 LTS and above, the Streaming Query Listener is available in Python and Scala.

Important

Credentials and objects managed by Unity Catalog cannot be used in StreamingQueryListener logic.

Note

Processing latency with listeners can significantly affect query processing speeds. It’s advised to limit processing logic in these listeners and opt for writing to fast-response systems like Kafka for efficiency.

The following code provides basic examples of the syntax for implementing a listener:

Scala

import org.apache.spark.sql.streaming.StreamingQueryListener
import org.apache.spark.sql.streaming.StreamingQueryListener._

val myListener = new StreamingQueryListener {

  /**
    * Called when a query is started.
    * @note This is called synchronously with
    *       [[org.apache.spark.sql.streaming.DataStreamWriter `DataStreamWriter.start()`]].
    *       `onQueryStart` calls on all listeners before
    *       `DataStreamWriter.start()` returns the corresponding [[StreamingQuery]].
    *        Do not block this method, as it blocks your query.
    */
  def onQueryStarted(event: QueryStartedEvent): Unit = {}

  /**
    * Called when there is some status update (ingestion rate updated, etc.)
    *
    * @note This method is asynchronous. The status in [[StreamingQuery]] returns the
    *       latest status, regardless of when this method is called. The status of [[StreamingQuery]]
    *       may change before or when you process the event. For example, you may find [[StreamingQuery]]
    *       terminates when processing `QueryProgressEvent`.
    */
  def onQueryProgress(event: QueryProgressEvent): Unit = {}

  /**
    * Called when the query is idle and waiting for new data to process.
    */
  def onQueryIdle(event: QueryProgressEvent): Unit = {}

  /**
    * Called when a query is stopped, with or without error.
    */
  def onQueryTerminated(event: QueryTerminatedEvent): Unit = {}
}

Python

class MyListener(StreamingQueryListener):
    def onQueryStarted(self, event):
        """
        Called when a query is started.

        Parameters
        ----------
        event: :class:`pyspark.sql.streaming.listener.QueryStartedEvent`
            The properties are available as the same as Scala API.

        Notes
        -----
        This is called synchronously with
        meth:`pyspark.sql.streaming.DataStreamWriter.start`,
        that is, ``onQueryStart`` will be called on all listeners before
        ``DataStreamWriter.start()`` returns the corresponding
        :class:`pyspark.sql.streaming.StreamingQuery`.
        Do not block in this method as it will block your query.
        """
        pass

    def onQueryProgress(self, event):
        """
        Called when there is some status update (ingestion rate updated, etc.)

        Parameters
        ----------
        event: :class:`pyspark.sql.streaming.listener.QueryProgressEvent`
            The properties are available as the same as Scala API.

        Notes
        -----
        This method is asynchronous. The status in
        :class:`pyspark.sql.streaming.StreamingQuery` returns the
        most recent status, regardless of when this method is called. The status
        of :class:`pyspark.sql.streaming.StreamingQuery`.
        may change before or when you process the event.
        For example, you may find :class:`StreamingQuery`
        terminates when processing `QueryProgressEvent`.
        """
        pass

    def onQueryIdle(self, event):
        """
        Called when the query is idle and waiting for new data to process.
        """
        pass

    def onQueryTerminated(self, event):
        """
        Called when a query is stopped, with or without error.

        Parameters
        ----------
        event: :class:`pyspark.sql.streaming.listener.QueryTerminatedEvent`
            The properties are available as the same as Scala API.
        """
        pass

my_listener = MyListener()

Defining observable metrics in Structured Streaming

Observable metrics are named arbitrary aggregate functions that can be defined on a query (DataFrame). As soon as the execution of a DataFrame reaches a completion point (that is, finishes a batch query or reaches a streaming epoch), a named event is emitted that contains the metrics for the data processed since the last completion point.

You can observe these metrics by attaching a listener to the Spark session. The listener depends on the execution mode:

  • Batch mode: Use QueryExecutionListener.

    QueryExecutionListener is called when the query completes. Access the metrics using the QueryExecution.observedMetrics map.

  • Streaming, or microbatch: Use StreamingQueryListener.

    StreamingQueryListener is called when the streaming query completes an epoch. Access the metrics using the StreamingQueryProgress.observedMetrics map. Azure Databricks does not support continuous execution streaming.

For example:

Scala

// Observe row count (rc) and error row count (erc) in the streaming Dataset
val observed_ds = ds.observe("my_event", count(lit(1)).as("rc"), count($"error").as("erc"))
observed_ds.writeStream.format("...").start()

// Monitor the metrics using a listener
spark.streams.addListener(new StreamingQueryListener() {
  override def onQueryProgress(event: QueryProgressEvent): Unit = {
    event.progress.observedMetrics.get("my_event").foreach { row =>
      // Trigger if the number of errors exceeds 5 percent
      val num_rows = row.getAs[Long]("rc")
      val num_error_rows = row.getAs[Long]("erc")
      val ratio = num_error_rows.toDouble / num_rows
      if (ratio > 0.05) {
        // Trigger alert
      }
    }
  }
})

Python

# Observe metric
observed_df = df.observe("metric", count(lit(1)).as("cnt"), count(col("error")).as("malformed"))
observed_df.writeStream.format("...").start()

# Define my listener.
class MyListener(StreamingQueryListener):
    def onQueryStarted(self, event):
        print(f"'{event.name}' [{event.id}] got started!")
    def onQueryProgress(self, event):
        row = event.progress.observedMetrics.get("metric")
        if row is not None:
            if row.malformed / row.cnt > 0.5:
                print("ALERT! Ouch! there are too many malformed "
                      f"records {row.malformed} out of {row.cnt}!")
            else:
                print(f"{row.cnt} rows processed!")
    def onQueryTerminated(self, event):
        print(f"{event.id} got terminated!")

# Add my listener.
spark.streams.addListener(MyListener())

StreamingQueryListener object metrics

Metric Description
id A unique query ID that persists across restarts. See StreamingQuery.id().
runId A query id that is unique for every start/restart. See StreamingQuery.runId().
name The user-specified name of the query. Name is null if no name is specified.
timestamp The timestamp for the execution of the microbatch.
batchId A unique ID for the current batch of data being processed. In the case of retries after a failure, a given batch ID may be executed more than once. Similarly, when there is no data to be processed, the batch ID is not incremented.
numInputRows The aggregate (across all sources) number of records processed in a trigger.
inputRowsPerSecond The aggregate (across all sources) rate of arriving data.
processedRowsPerSecond The aggregate (across all sources) rate at which Spark is processing data.

durationMs object

Information about the time it takes to complete various stages of the microbatch execution process.

Metric Description
durationMs.addBatch The time taken to execute the microbatch. This excludes the time Spark takes to plan the microbatch.
durationMs.getBatch The time it takes to retrieve the metadata about the offsets from the source.
durationMs.latestOffset The latest offset consumed for the microbatch. This progress object refers to the time taken to retrieve the latest offset from sources.
durationMs.queryPlanning The time taken to generate the execution plan.
durationMs.triggerExecution The time it takes to plan and execute the microbatch.
durationMs.walCommit The time taken to commit the new available offsets.

eventTime object

Information about the event time value seen within the data being processed in the microbatch. This data is used by the watermark to figure out how to trim the state for processing stateful aggregations defined in the Structured Streaming job.

Metric Description
eventTime.avg The average event time seen in that trigger.
eventTime.max The maximum event time seen in that trigger.
eventTime.min The minimum event time seen in that trigger.
eventTime.watermark The value of the watermark used in that trigger.

stateOperators object

Information about the stateful operations that are defined in the Structured Streaming job and the aggregations that are produced from them.

Metric Description
stateOperators.operatorName The name of the stateful operator to which the metrics relate, such as symmetricHashJoin, dedupe, stateStoreSave.
stateOperators.numRowsTotal The total number of rows in state as a result of a stateful operator or aggregation.
stateOperators.numRowsUpdated The total number of rows updated in state as a result of a stateful operator or aggregation.
stateOperators.allUpdatesTimeMs This metric is currently not measurable by Spark and is planned to be removed in future updates.
stateOperators.numRowsRemoved The total number of rows removed from state as a result of a stateful operator or aggregation.
stateOperators.allRemovalsTimeMs This metric is currently not measurable by Spark and is planned to be removed in future updates.
stateOperators.commitTimeMs The time taken to commit all updates (puts and removes) and return a new version.
stateOperators.memoryUsedBytes Memory used by the state store.
stateOperators.numRowsDroppedByWatermark The number of rows that are considered too late to be included in a stateful aggregation. Streaming aggregations only: The number of rows dropped post-aggregation (not raw input rows). This number is not precise, but provides an indication that there is late data being dropped.
stateOperators.numShufflePartitions The number of shuffle partitions for this stateful operator.
stateOperators.numStateStoreInstances The actual state store instance that the operator has initialized and maintained. For many stateful operators, this is the same as the number of partitions. However, stream-stream joins initialize four state store instances per partition.

stateOperators.customMetrics object

Information collected from RocksDB capturing metrics about its performance and operations with respect to the stateful values it maintains for the Structured Streaming job. For more information, see Configure RocksDB state store on Azure Databricks.

Metric Description
customMetrics.rocksdbBytesCopied The number of bytes copied as tracked by the RocksDB File Manager.
customMetrics.rocksdbCommitCheckpointLatency The time in milliseconds taking a snapshot of native RocksDB and write it to a local directory.
customMetrics.rocksdbCompactLatency The time in milliseconds compacting (optional) during the checkpoint commit.
customMetrics.rocksdbCommitFileSyncLatencyMs The time in milliseconds syncing the native RocksDB snapshot to external storage (the checkpoint location).
customMetrics.rocksdbCommitFlushLatency The time in milliseconds flushing the RocksDB in-memory changes to the local disk.
customMetrics.rocksdbCommitPauseLatency The time in milliseconds stopping the background worker threads as part of the checkpoint commit, such as for compaction.
customMetrics.rocksdbCommitWriteBatchLatency The time in milliseconds applying the staged writes in in-memory structure (WriteBatch) to native RocksDB.
customMetrics.rocksdbFilesCopied The number of files copied as tracked by the RocksDB File Manager.
customMetrics.rocksdbFilesReused The number of files reused as tracked by the RocksDB File Manager.
customMetrics.rocksdbGetCount The number of get calls to the DB (does not include gets from WriteBatch - in-memory batch used for staging writes).
customMetrics.rocksdbGetLatency The average time in nanoseconds for the underlying native RocksDB::Get call.
customMetrics.rocksdbReadBlockCacheHitCount The count of cache hits from the block cache in RocksDB that are useful in avoiding local disk reads.
customMetrics.rocksdbReadBlockCacheMissCount The count of the block cache in RocksDB is not useful in avoiding local disk reads.
customMetrics.rocksdbSstFileSize The size of all Static Sorted Table (SST) file - the tabular structure RocksDB uses to store data.
customMetrics.rocksdbTotalBytesRead The number of uncompressed bytes read by get operations.
customMetrics.rocksdbTotalBytesReadByCompaction The number of bytes that the compaction process reads from the disk.
customMetrics.rocksdbTotalBytesReadThroughIterator The total number of bytes of uncompressed data read using an iterator. Some stateful operations (for example, timeout processing in FlatMapGroupsWithState and watermarking) require reading data in DB through an iterator.
customMetrics.rocksdbTotalBytesWritten The total number of uncompressed bytes written by put operations.
customMetrics.rocksdbTotalBytesWrittenByCompaction The total number of bytes the compaction process writes to the disk.
customMetrics.rocksdbTotalCompactionLatencyMs The time in milliseconds for RocksDB compactions, including background compactions and the optional compaction initiated during the commit.
customMetrics.rocksdbTotalFlushLatencyMs The total flush time, including background flushing. Flush operations are processes by which the MemTable is flushed to storage once it’s full. MemTables are the first level where data is stored in RocksDB.
customMetrics.rocksdbZipFileBytesUncompressed The size in bytes of the uncompressed zip files as reported by the File Manager. The File Manager manages the physical SST file disk space utilization and deletion.

sources object (Kafka)

Metric Description
sources.description A detailed description of the Kafka source, specifying the exact Kafka topic being read from. For example: “KafkaV2[Subscribe[KAFKA_TOPIC_NAME_INPUT_A]]”.
sources.startOffset object The starting offset number within the Kafka topic at which the streaming job started.
sources.endOffset object The last offset processed by the microbatch. This could be equal to latestOffset for an ongoing microbatch execution.
sources.latestOffset object The latest offset figured by the microbatch. The microbatching process might not process all offsets when there is throttling, which results in endOffset and latestOffset differiong.
sources.numInputRows The number of input rows processed from this source.
sources.inputRowsPerSecond The rate at which data is arriving for processing from this source.
sources.processedRowsPerSecond The rate at which Spark is processing data from this source.

sources.metrics object (Kafka)

Metric Description
sources.metrics.avgOffsetsBehindLatest The average number of offsets that the streaming query is behind the latest available offset among all the subscribed topics.
sources.metrics.estimatedTotalBytesBehindLatest The estimated number of bytes that the query process has not consumed from the subscribed topics.
sources.metrics.maxOffsetsBehindLatest The maximum number of offsets that the streaming query is behind the latest available offset among all the subscribed topics.
sources.metrics.minOffsetsBehindLatest The minimum number of offsets that the streaming query is behind the latest available offset among all the subscribed topics.

sink object (Kafka)

Metric Description
sink.description The description of the Kafka sink to which the streaming query is writing, detailing the specific Kafka sink implementation being used. For example: “org.apache.spark.sql.kafka010.KafkaSourceProvider$KafkaTable@e04b100”.
sink.numOutputRows The number of rows that were written to the output table or sink as part of the microbatch. For some situations, this value can be “-1” and generally can be interpreted as “unknown”.

sources object (Delta Lake)

Metric Description
sources.description The description of the source from which the streaming query is reading from. For example: “DeltaSource[table]”.
sources.[startOffset/endOffset].sourceVersion The version of serialization with which this offset is encoded.
sources.[startOffset/endOffset].reservoirId The ID of the table being read. This is used to detect misconfiguration when restarting a query.
sources.[startOffset/endOffset].reservoirVersion The version of the table that is currently processing.
sources.[startOffset/endOffset].index The index in the sequence of AddFiles in this version. This is used to break large commits into multiple batches. This index is created by sorting on modificationTimestamp and path.
sources.[startOffset/endOffset].isStartingVersion Identifies whether current offset marks the start of a new streaming query rather than the processing of changes that occurred after the initial data was processed. When starting a new query, all data present in the table at the start is processed first, and then any new data that arrives.
sources.latestOffset The latest offset processed by the microbatch query.
sources.numInputRows The number of input rows processed from this source.
sources.inputRowsPerSecond The rate at which data is arriving for processing from this source.
sources.processedRowsPerSecond The rate at which Spark is processing data from this source.
sources.metrics.numBytesOutstanding The combined size of the outstanding files (files tracked by RocksDB). This is the backlog metric for Delta and Auto Loader as the streaming source.
sources.metrics.numFilesOutstanding The number of outstanding files to be processed. This is the backlog metric for Delta and Auto Loader as the streaming source.

sink object (Delta Lake)

Metric Description
sink.description The description of the Delta sink, detailing the specific Delta sink implementation being used. For example: “DeltaSink[table]”.
sink.numOutputRows The number of rows is always “-1” because Spark can’t infer output rows for DSv1 sinks, which is the classification for the Delta Lake sink.

Examples

Example Kafka-to-Kafka StreamingQueryListener event

{
  "id" : "3574feba-646d-4735-83c4-66f657e52517",
  "runId" : "38a78903-9e55-4440-ad81-50b591e4746c",
  "name" : "STREAMING_QUERY_NAME_UNIQUE",
  "timestamp" : "2022-10-31T20:09:30.455Z",
  "batchId" : 1377,
  "numInputRows" : 687,
  "inputRowsPerSecond" : 32.13433743393049,
  "processedRowsPerSecond" : 34.067241892293964,
  "durationMs" : {
    "addBatch" : 18352,
    "getBatch" : 0,
    "latestOffset" : 31,
    "queryPlanning" : 977,
    "triggerExecution" : 20165,
    "walCommit" : 342
  },
  "eventTime" : {
    "avg" : "2022-10-31T20:09:18.070Z",
    "max" : "2022-10-31T20:09:30.125Z",
    "min" : "2022-10-31T20:09:09.793Z",
    "watermark" : "2022-10-31T20:08:46.355Z"
  },
  "stateOperators" : [ {
    "operatorName" : "stateStoreSave",
    "numRowsTotal" : 208,
    "numRowsUpdated" : 73,
    "allUpdatesTimeMs" : 434,
    "numRowsRemoved" : 76,
    "allRemovalsTimeMs" : 515,
    "commitTimeMs" : 0,
    "memoryUsedBytes" : 167069743,
    "numRowsDroppedByWatermark" : 0,
    "numShufflePartitions" : 20,
    "numStateStoreInstances" : 20,
    "customMetrics" : {
      "rocksdbBytesCopied" : 0,
      "rocksdbCommitCheckpointLatency" : 0,
      "rocksdbCommitCompactLatency" : 0,
      "rocksdbCommitFileSyncLatencyMs" : 0,
      "rocksdbCommitFlushLatency" : 0,
      "rocksdbCommitPauseLatency" : 0,
      "rocksdbCommitWriteBatchLatency" : 0,
      "rocksdbFilesCopied" : 0,
      "rocksdbFilesReused" : 0,
      "rocksdbGetCount" : 222,
      "rocksdbGetLatency" : 0,
      "rocksdbPutCount" : 0,
      "rocksdbPutLatency" : 0,
      "rocksdbReadBlockCacheHitCount" : 165,
      "rocksdbReadBlockCacheMissCount" : 41,
      "rocksdbSstFileSize" : 232729,
      "rocksdbTotalBytesRead" : 12844,
      "rocksdbTotalBytesReadByCompaction" : 0,
      "rocksdbTotalBytesReadThroughIterator" : 161238,
      "rocksdbTotalBytesWritten" : 0,
      "rocksdbTotalBytesWrittenByCompaction" : 0,
      "rocksdbTotalCompactionLatencyMs" : 0,
      "rocksdbTotalFlushLatencyMs" : 0,
      "rocksdbWriterStallLatencyMs" : 0,
      "rocksdbZipFileBytesUncompressed" : 0
    }
  }, {
    "operatorName" : "dedupe",
    "numRowsTotal" : 2454744,
    "numRowsUpdated" : 73,
    "allUpdatesTimeMs" : 4155,
    "numRowsRemoved" : 0,
    "allRemovalsTimeMs" : 0,
    "commitTimeMs" : 0,
    "memoryUsedBytes" : 137765341,
    "numRowsDroppedByWatermark" : 34,
    "numShufflePartitions" : 20,
    "numStateStoreInstances" : 20,
    "customMetrics" : {
      "numDroppedDuplicateRows" : 193,
      "rocksdbBytesCopied" : 0,
      "rocksdbCommitCheckpointLatency" : 0,
      "rocksdbCommitCompactLatency" : 0,
      "rocksdbCommitFileSyncLatencyMs" : 0,
      "rocksdbCommitFlushLatency" : 0,
      "rocksdbCommitPauseLatency" : 0,
      "rocksdbCommitWriteBatchLatency" : 0,
      "rocksdbFilesCopied" : 0,
      "rocksdbFilesReused" : 0,
      "rocksdbGetCount" : 146,
      "rocksdbGetLatency" : 0,
      "rocksdbPutCount" : 0,
      "rocksdbPutLatency" : 0,
      "rocksdbReadBlockCacheHitCount" : 3,
      "rocksdbReadBlockCacheMissCount" : 3,
      "rocksdbSstFileSize" : 78959140,
      "rocksdbTotalBytesRead" : 0,
      "rocksdbTotalBytesReadByCompaction" : 0,
      "rocksdbTotalBytesReadThroughIterator" : 0,
      "rocksdbTotalBytesWritten" : 0,
      "rocksdbTotalBytesWrittenByCompaction" : 0,
      "rocksdbTotalCompactionLatencyMs" : 0,
      "rocksdbTotalFlushLatencyMs" : 0,
      "rocksdbWriterStallLatencyMs" : 0,
      "rocksdbZipFileBytesUncompressed" : 0
    }
  }, {
    "operatorName" : "symmetricHashJoin",
    "numRowsTotal" : 2583,
    "numRowsUpdated" : 682,
    "allUpdatesTimeMs" : 9645,
    "numRowsRemoved" : 508,
    "allRemovalsTimeMs" : 46,
    "commitTimeMs" : 21,
    "memoryUsedBytes" : 668544484,
    "numRowsDroppedByWatermark" : 0,
    "numShufflePartitions" : 20,
    "numStateStoreInstances" : 80,
    "customMetrics" : {
      "rocksdbBytesCopied" : 0,
      "rocksdbCommitCheckpointLatency" : 0,
      "rocksdbCommitCompactLatency" : 0,
      "rocksdbCommitFileSyncLatencyMs" : 0,
      "rocksdbCommitFlushLatency" : 0,
      "rocksdbCommitPauseLatency" : 0,
      "rocksdbCommitWriteBatchLatency" : 0,
      "rocksdbFilesCopied" : 0,
      "rocksdbFilesReused" : 0,
      "rocksdbGetCount" : 4218,
      "rocksdbGetLatency" : 3,
      "rocksdbPutCount" : 0,
      "rocksdbPutLatency" : 0,
      "rocksdbReadBlockCacheHitCount" : 3425,
      "rocksdbReadBlockCacheMissCount" : 149,
      "rocksdbSstFileSize" : 742827,
      "rocksdbTotalBytesRead" : 866864,
      "rocksdbTotalBytesReadByCompaction" : 0,
      "rocksdbTotalBytesReadThroughIterator" : 0,
      "rocksdbTotalBytesWritten" : 0,
      "rocksdbTotalBytesWrittenByCompaction" : 0,
      "rocksdbTotalCompactionLatencyMs" : 0,
      "rocksdbTotalFlushLatencyMs" : 0,
      "rocksdbWriterStallLatencyMs" : 0,
      "rocksdbZipFileBytesUncompressed" : 0
    }
  } ],
  "sources" : [ {
    "description" : "KafkaV2[Subscribe[KAFKA_TOPIC_NAME_INPUT_A]]",
    "startOffset" : {
      "KAFKA_TOPIC_NAME_INPUT_A" : {
        "0" : 349706380
      }
    },
    "endOffset" : {
      "KAFKA_TOPIC_NAME_INPUT_A" : {
        "0" : 349706672
      }
    },
    "latestOffset" : {
      "KAFKA_TOPIC_NAME_INPUT_A" : {
        "0" : 349706672
      }
    },
    "numInputRows" : 292,
    "inputRowsPerSecond" : 13.65826278123392,
    "processedRowsPerSecond" : 14.479817514628582,
    "metrics" : {
      "avgOffsetsBehindLatest" : "0.0",
      "estimatedTotalBytesBehindLatest" : "0.0",
      "maxOffsetsBehindLatest" : "0",
      "minOffsetsBehindLatest" : "0"
    }
  }, {
    "description" : "KafkaV2[Subscribe[KAFKA_TOPIC_NAME_INPUT_B]]",
    "startOffset" : {
      KAFKA_TOPIC_NAME_INPUT_B" : {
        "2" : 143147812,
        "1" : 129288266,
        "0" : 138102966
      }
    },
    "endOffset" : {
      "KAFKA_TOPIC_NAME_INPUT_B" : {
        "2" : 143147812,
        "1" : 129288266,
        "0" : 138102966
      }
    },
    "latestOffset" : {
      "KAFKA_TOPIC_NAME_INPUT_B" : {
        "2" : 143147812,
        "1" : 129288266,
        "0" : 138102966
      }
    },
    "numInputRows" : 0,
    "inputRowsPerSecond" : 0.0,
    "processedRowsPerSecond" : 0.0,
    "metrics" : {
      "avgOffsetsBehindLatest" : "0.0",
      "maxOffsetsBehindLatest" : "0",
      "minOffsetsBehindLatest" : "0"
    }
  } ],
  "sink" : {
    "description" : "org.apache.spark.sql.kafka010.KafkaSourceProvider$KafkaTable@e04b100",
    "numOutputRows" : 76
  }
}

Example Delta Lake-to-Delta Lake StreamingQueryListener event

{
  "id" : "aeb6bc0f-3f7d-4928-a078-ba2b304e2eaf",
  "runId" : "35d751d9-2d7c-4338-b3de-6c6ae9ebcfc2",
  "name" : "silverTransformFromBronze",
  "timestamp" : "2022-11-01T18:21:29.500Z",
  "batchId" : 4,
  "numInputRows" : 0,
  "inputRowsPerSecond" : 0.0,
  "processedRowsPerSecond" : 0.0,
  "durationMs" : {
    "latestOffset" : 62,
    "triggerExecution" : 62
  },
  "stateOperators" : [ ],
  "sources" : [ {
    "description" : "DeltaSource[dbfs:/FileStore/max.fisher@databricks.com/ctc/stateful-trade-analysis-demo/table]",
    "startOffset" : {
      "sourceVersion" : 1,
      "reservoirId" : "84590dac-da51-4e0f-8eda-6620198651a9",
      "reservoirVersion" : 3216,
      "index" : 3214,
      "isStartingVersion" : true
    },
    "endOffset" : {
      "sourceVersion" : 1,
      "reservoirId" : "84590dac-da51-4e0f-8eda-6620198651a9",
      "reservoirVersion" : 3216,
      "index" : 3214,
      "isStartingVersion" : true
    },
    "latestOffset" : null,
    "numInputRows" : 0,
    "inputRowsPerSecond" : 0.0,
    "processedRowsPerSecond" : 0.0,
    "metrics" : {
      "numBytesOutstanding" : "0",
      "numFilesOutstanding" : "0"
    }
  } ],
  "sink" : {
    "description" : "DeltaSink[dbfs:/user/hive/warehouse/maxfisher.db/trade_history_silver_delta_demo2]",
    "numOutputRows" : -1
  }
}

Example Kinesis-to-Delta Lake StreamingQueryListener event

{
  "id" : "3ce9bd93-da16-4cb3-a3b6-e97a592783b5",
  "runId" : "fe4a6bda-dda2-4067-805d-51260d93260b",
  "name" : null,
  "timestamp" : "2024-05-14T02:09:20.846Z",
  "batchId" : 0,
  "batchDuration" : 59322,
  "numInputRows" : 20,
  "inputRowsPerSecond" : 0.0,
  "processedRowsPerSecond" : 0.33714304979602844,
  "durationMs" : {
    "addBatch" : 5397,
    "commitBatch" : 4429,
    "commitOffsets" : 211,
    "getBatch" : 5,
    "latestOffset" : 21998,
    "queryPlanning" : 12128,
    "triggerExecution" : 59313,
    "walCommit" : 220
  },
  "stateOperators" : [ ],
  "sources" : [ {
    "description" : "KinesisV2[KinesisTestUtils-7199466178786508570-at-1715652545256]",
    "startOffset" : null,
    "endOffset" : [ {
      "shard" : {
        "stream" : "KinesisTestUtils-7199466178786508570-at-1715652545256",
        "shardId" : "shardId-000000000000"
      },
      "firstSeqNum" : "49652022592149344892294981243280420130985816456924495874",
      "lastSeqNum" : "49652022592149344892294981243290091537542733559041622018",
      "closed" : false,
      "msBehindLatest" : "0",
      "lastRecordSeqNum" : "49652022592149344892294981243290091537542733559041622018"
    }, {
      "shard" : {
        "stream" : "KinesisTestUtils-7199466178786508570-at-1715652545256",
        "shardId" : "shardId-000000000001"
      },
      "firstSeqNum" : "49652022592171645637493511866421955849258464818430476306",
      "lastSeqNum" : "49652022592171645637493511866434045107454611178897014802",
      "closed" : false,
      "msBehindLatest" : "0",
      "lastRecordSeqNum" : "49652022592171645637493511866434045107454611178897014802"
    } ],
    "latestOffset" : null,
    "numInputRows" : 20,
    "inputRowsPerSecond" : 0.0,
    "processedRowsPerSecond" : 0.33714304979602844,
    "metrics" : {
      "avgMsBehindLatest" : "0.0",
      "maxMsBehindLatest" : "0",
      "minMsBehindLatest" : "0",
      "mode" : "efo",
      "numClosedShards" : "0",
      "numProcessedBytes" : "30",
      "numProcessedRecords" : "18",
      "numRegisteredConsumers" : "1",
      "numStreams" : "1",
      "numTotalShards" : "2",
      "totalPrefetchedBytes" : "0"
    }
  } ],
  "sink" : {
    "description" : "DeltaSink[dbfs:/streaming/test/KinesisToDeltaServerlessLiteSuite/2024-05-14-01-58-14-76eb7e51/56b9426c-3492-4ac5-8fe8-3d00efe20be5/deltaTable]",
    "numOutputRows" : -1
  }
}

Example Kafka+Delta Lake-to-Delta Lake StreamingQueryListener event

{
 "id" : "210f4746-7caa-4a51-bd08-87cabb45bdbe",
 "runId" : "42a2f990-c463-4a9c-9aae-95d6990e63f4",
 "name" : null,
 "timestamp" : "2024-05-15T21:57:50.782Z",
 "batchId" : 0,
 "batchDuration" : 3601,
 "numInputRows" : 20,
 "inputRowsPerSecond" : 0.0,
 "processedRowsPerSecond" : 5.55401277422938,
 "durationMs" : {
  "addBatch" : 1544,
  "commitBatch" : 686,
  "commitOffsets" : 27,
  "getBatch" : 12,
  "latestOffset" : 577,
  "queryPlanning" : 105,
  "triggerExecution" : 3600,
  "walCommit" : 34
 },
 "stateOperators" : [ {
  "operatorName" : "symmetricHashJoin",
  "numRowsTotal" : 20,
  "numRowsUpdated" : 20,
  "allUpdatesTimeMs" : 473,
  "numRowsRemoved" : 0,
  "allRemovalsTimeMs" : 0,
  "commitTimeMs" : 277,
  "memoryUsedBytes" : 13120,
  "numRowsDroppedByWatermark" : 0,
  "numShufflePartitions" : 5,
  "numStateStoreInstances" : 20,
  "customMetrics" : {
   "loadedMapCacheHitCount" : 0,
   "loadedMapCacheMissCount" : 0,
   "stateOnCurrentVersionSizeBytes" : 5280
  }
 } ],
 "sources" : [ {
  "description" : "KafkaV2[Subscribe[topic-1]]",
  "startOffset" : null,
  "endOffset" : {
   "topic-1" : {
    "1" : 5,
    "0" : 5
   }
  },
  "latestOffset" : {
   "topic-1" : {
    "1" : 5,
    "0" : 5
   }
  },
  "numInputRows" : 10,
  "inputRowsPerSecond" : 0.0,
  "processedRowsPerSecond" : 2.77700638711469,
  "metrics" : {
   "avgOffsetsBehindLatest" : "0.0",
   "estimatedTotalBytesBehindLatest" : "0.0",
   "maxOffsetsBehindLatest" : "0",
   "minOffsetsBehindLatest" : "0"
  }
 }, {
  "description" : "DeltaSource[file:/tmp/spark-1b7cb042-bab8-4469-bb2f-733c15141081]",
  "startOffset" : null,
  "endOffset" : {
   "sourceVersion" : 1,
   "reservoirId" : "b207a1cd-0fbe-4652-9c8f-e5cc467ae84f",
   "reservoirVersion" : 1,
   "index" : -1,
   "isStartingVersion" : false
  },
  "latestOffset" : null,
  "numInputRows" : 10,
  "inputRowsPerSecond" : 0.0,
  "processedRowsPerSecond" : 2.77700638711469,
  "metrics" : {
   "numBytesOutstanding" : "0",
   "numFilesOutstanding" : "0"
  }
 } ],
 "sink" : {
  "description" : "DeltaSink[/tmp/spark-d445c92a-4640-4827-a9bd-47246a30bb04]",
  "numOutputRows" : -1
 }
}

Example rate source to Delta Lake StreamingQueryListener event

{
  "id" : "912ebdc1-edf2-48ec-b9fb-1a9b67dd2d9e",
  "runId" : "85de73a5-92cc-4b7f-9350-f8635b0cf66e",
  "name" : "dataGen",
  "timestamp" : "2022-11-01T18:28:20.332Z",
  "batchId" : 279,
  "numInputRows" : 300,
  "inputRowsPerSecond" : 114.15525114155251,
  "processedRowsPerSecond" : 158.9825119236884,
  "durationMs" : {
    "addBatch" : 1771,
    "commitOffsets" : 54,
    "getBatch" : 0,
    "latestOffset" : 0,
    "queryPlanning" : 4,
    "triggerExecution" : 1887,
    "walCommit" : 58
  },
  "stateOperators" : [ ],
  "sources" : [ {
    "description" : "RateStreamV2[rowsPerSecond=100, rampUpTimeSeconds=0, numPartitions=default",
    "startOffset" : 560,
    "endOffset" : 563,
    "latestOffset" : 563,
    "numInputRows" : 300,
    "inputRowsPerSecond" : 114.15525114155251,
    "processedRowsPerSecond" : 158.9825119236884
  } ],
  "sink" : {
    "description" : "DeltaSink[dbfs:/user/hive/warehouse/maxfisher.db/trade_history_bronze_delta_demo]",
    "numOutputRows" : -1
  }
}