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A streaming query is created by reading a stream from a source table using the read function.
False
A streaming DataFrame is similar to a standard Spark DataFrame and can be used with all operations like count() or sort().
False
The withColumn and current_timestamp() functions are used to add a Timestamp Column to the streaming DataFrame.
True
The select function is used to select all columns from the streaming DataFrame.
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The writeStream function is used to write the streaming DataFrame to an output file.
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The checkpoint file is used to store the output of the streaming query.
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The query progress log (QPL) provides execution details on the entire streaming query.
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The readChangeFeed option is used to read a CDF stream and capture changes made to the source table, excluding inserts.
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The stream unique id in the query log is used to map back to the source table.
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The batchld in the query log represents the micro-batch ID and increments by one for every processed row.
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The numlnputRows field in the query log represents the total number of rows in the stream.
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The durationMs field in the query log represents the total execution time of the query.
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The query progress log displays graphically the key metrics from the QPL.
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The Raw Data tab contains the dashboard, where some of the key metrics from the QPL are displayed graphically.
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The timestamp in the query log represents the start time of the query execution.
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The triggerExecution field in the durationMs object represents the time taken to execute the trigger.
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Spark Structured Streaming was introduced in Apache Spark 1.0.
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DStreams is a higher-level API than Structured Streaming.
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Delta Lake overcomes limitations associated with batch systems.
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The writeStream function is used to read a streaming query from a source table.
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The AvailableNow stream triggering mode is used to build incremental pipelines with state variables.
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A Change Data Feed (CDF) can be enabled on a non-Delta table.
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Spark Structured Streaming is used for building batch applications on Spark.
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Delta Lake is integrated with Spark Structured Streaming through its three major operators: readstream, writeStream, and updateStream.
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The ignoreChanges option will only emit inserted records to the stream.
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The print(streamQuery.status) command will always return {'message': 'Running', 'isDataAvailable': True, 'isTriggerActive': True}.
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The stream_df variable is a standard Spark DataFrame.
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The readstream function is used to read a stream from a source table.
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The ignoreChanges option can be used with the read function.
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The type(streamQuery) command will return 'StreamingQuery'.
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The streamQuery.status property will always return {'message': 'Running'}.
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The readstream function can be used to read a stream from a file.
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The Streaming query is designed to run indefinitely, looking for new records in the source table.
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The query will terminate once it has processed all the existing records in the source table.
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The batchld is reset to 0 after processing the current batch of records.
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The streaming query is designed to process one record at a time from the source table.
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The checkpoint file is used to store the intermediate state of the streaming query.
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The streaming query is only triggered when new records are inserted into the source table.
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The cost of running the streaming query can be economical in a real-world application.
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The streaming query will stop running when the source table is no longer updated.
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The recentProgress property does not print out the same output as the raw data section from the streaming output in the notebook.
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The awaitTermination() method is used to wait until the stream terminates.
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The checkpoint file is used to store the output of the streaming query.
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Deleting the checkpoint file will not affect the streaming query.
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The numlnputRows field in the query log represents the total number of rows processed in the current batch.
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The reservoirVersion in the endoffset represents the version of the source table.
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Study Notes
Streaming Queries
- A streaming query is created by reading a stream from a source table using
readstreaminstead ofread. -
readstreamreturns a streaming DataFrame, which is similar to a standard Spark DataFrame but is unbounded and cannot be used with certain operations likecount()orsort().
Adding a Timestamp Column
- A
RecordStreamTimecolumn can be added to the streaming DataFrame usingwithColumnandcurrent_timestamp(). - This column captures the timestamp when each record is read from the source table.
Selecting Columns
- The
selectfunction is used to select specific columns from the streaming DataFrame. - The
select_columnslist specifies the columns to be selected.
Writing to an Output Table
- The streaming DataFrame is written to an output table using
writeStream. - A target location and checkpoint location are specified.
- The checkpoint file maintains metadata and state of the streaming query, ensuring fault tolerance and enabling query recovery in case of failure.
Query Progress Log
- When the streaming query is started, a query progress log (QPL) is displayed.
- The QPL provides execution details on each micro-batch and is used to display a streaming dashboard in the notebook cell.
- The dashboard provides metrics, statistics, and insights about the stream application's performance, throughput, and latency.
Trigger Options
- Trigger options can be used to control the rate at which data is processed in each micro-batch.
- Options include
maxBytesPerTrigger,ignoreDeletes, andignoreChanges. - These options can be used to control rate limits and avoid overloading processing resources.
Reading a CDF Stream
- A CDF (Change Data Feed) stream can be read using
readstreamwith thereadChangeFeedoption. - This allows for capturing changes made to the source table, such as inserts, updates, and deletes.
- Rate limit options and ignore deletes can be specified to control the processing of the stream.
Spark Structured Streaming
- Spark Structured Streaming was introduced in Apache Spark 2.0 to build near-real-time streaming applications.
- It replaced the older DStreams API, which was based on the Spark RDD model.
- Structured Streaming has added many optimizations and connectors, including integration with Delta Lake.
Query Progress Log
- The query progress log displays key metrics, including the stream unique ID, batch ID, number of input rows, and processed rows per second.
- The stream unique ID uniquely identifies the stream and maps back to the checkpoint directory.
- The batch ID is the micro-batch ID, which starts with zero and increments by one for every processed micro-batch.
- The numlnputRows field represents the number of rows ingested in the current micro-batch.
Delta Lake Streaming
- Delta Lake is integrated with Spark Structured Streaming through its two major operators: readstream and writeStream.
- Delta tables can be used as both streaming sources and streaming sinks.
- Delta Lake overcomes limitations associated with streaming systems, including coalescing small files, maintaining "exactly-once" processing, and leveraging the Delta transaction log for efficient discovery of new files.
Delta Lake Streaming Example
- The example includes a streaming query that reads from a Delta table and writes to another Delta table.
- The query is triggered by inserting new records into the source table.
- The batch ID is set to 1 after the first batch is processed, and the number of input rows is updated accordingly.
Streaming Query
- The StreamingQuery class is used to execute a streaming query.
- The query can be monitored using the status property, which shows whether the query is running or stopped, and whether there is data available.
- The recentProgress property displays the latest progress of the streaming query, including the number of input rows.
- The awaitTermination() method can be used to wait until the stream terminates.
Checkpoint File
- The checkpoint file is used to store the state of the streaming query.
- Deleting the checkpoint file and re-running the streaming query will start from the beginning of the source table and re-process all records.
- The checkpoint file can be used to re-process all or part of the source records.
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Description
Learn about creating streaming queries in Spark, adding timestamp columns, and working with unbounded DataFrames.
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