A unique id for the current batch of data being processed.
A unique id for the current batch of data being processed. Note that in the case of retries after a failure a given batchId my be executed more than once. Similarly, when there is no data to be processed, the batchId will not be incremented.
The amount of time taken to perform various operations in milliseconds.
Statistics of event time seen in this batch.
Statistics of event time seen in this batch. It may contain the following keys:
"max" -> "2016-12-05T20:54:20.827Z" // maximum event time seen in this trigger "min" -> "2016-12-05T20:54:20.827Z" // minimum event time seen in this trigger "avg" -> "2016-12-05T20:54:20.827Z" // average event time seen in this trigger "watermark" -> "2016-12-05T20:54:20.827Z" // watermark used in this trigger
All timestamps are in ISO8601 format, i.e. UTC timestamps.
An unique query id that persists across restarts.
An unique query id that persists across restarts. See StreamingQuery.id()
.
The aggregate (across all sources) rate of data arriving.
The compact JSON representation of this progress.
User-specified name of the query, null if not specified.
The aggregate (across all sources) number of records processed in a trigger.
The pretty (i.e.
The pretty (i.e. indented) JSON representation of this progress.
The aggregate (across all sources) rate at which Spark is processing data.
A query id that is unique for every start/restart.
A query id that is unique for every start/restart. See StreamingQuery.runId()
.
detailed statistics on data being read from each of the streaming sources.
Information about operators in the query that store state.
Beginning time of the trigger in ISO8601 format, i.e.
Beginning time of the trigger in ISO8601 format, i.e. UTC timestamps.
Information about progress made in the execution of a StreamingQuery during a trigger. Each event relates to processing done for a single trigger of the streaming query. Events are emitted even when no new data is available to be processed.
2.1.0