Databricks Delta Tables - Streaming Reads and Writes¶
Databricks Delta is deeply integrated with Spark Structured Streaming through
writeStream. It overcomes many of the limitations typically associated with streaming systems and files, including:
- Dealing with small files produced by low latency ingest using
- Maintaining “exactly-once” processing with more than one stream (or concurrent batch jobs).
- Efficiently discovering which files are new when using files as the source for a stream.
As a source¶
When you load a Databricks Delta table as a stream source and use it in a streaming query, the query processes all of the data present in the table as well as any new data that arrives after the stream is started. You can load both tables and paths as a stream.
You can also control the maximum size of any microbatch that Databricks Delta gives to streaming by setting the
maxFilesPerTrigger option. This specifies the maximum number of new files to be considered in every trigger. The default is 1000.
Ignoring updates and deletes¶
Structured Streaming does not handle input that is not an append, and throws an exception if any modifications occur on the table being used as a source. There are two main strategies for dealing with changes that cannot be pushed downstream automatically:
- Since Databricks Delta tables retain all history by default, in many cases you can simply delete the output and checkpoint and restart the stream from the beginning.
As a sink¶
You can also write data into a Databricks Delta table using Structured Streaming. By using the transaction log, Delta can guarantee exactly-once processing, even when there are other streams or batch queries running concurrently against the table.
By default, streams run in append mode, which simply adds new records to the table:
events.writeStream .format("delta") .outputMode("append") .option("checkpointLocation", "/delta/events/_checkpoints/etl-from-json") .start("/delta/events") // as a path
You can also use Structured Streaming to replace the entire table with every batch. One example use case is to compute a summary using aggregation:
spark.readStream .format("delta") .groupBy("customerId") .count() .writeStream .format("delta") .outputMode("complete") .option("checkpointLocation", "/delta/eventsByCustomer/_checkpoints/streaming-agg") .start("/delta/eventsByCustomer")
The preceding example continuously updates a table that contains the aggregate number of events by customer.
For applications with more lenient latency requirements, you can save computing resources with one-time triggers. Use these to update summary aggregation tables on a given schedule, processing only new data that has arrived since the last update.