Iceberg uses Apache Spark's DataSourceV2 API for data source and catalog implementations. Spark DSv2 is an evolving API with different levels of support in Spark versions.
As of Spark 3.0, DataFrame reads and writes are supported.
| Feature support | Spark 3.0 | Spark 2.4 | Notes |
|---|---|---|---|
| DataFrame write | ✔ | ✔ |
Iceberg supports processing incremental data in spark structured streaming jobs which starts from a historical timestamp:
val df = spark.readStream .format("iceberg") .option("stream-from-timestamp", Long.toString(streamStartTimestamp)) .load("database.table_name")
{{< hint warning >}} Iceberg only supports reading data from append snapshots. Overwrite snapshots cannot be processed and will cause an exception by default. Overwrites may be ignored by setting streaming-skip-overwrite-snapshots=true. Similarly, delete snapshots will cause an exception by default, and deletes may be ignored by setting streaming-skip-delete-snapshots=true. {{</ hint >}}
To write values from streaming query to Iceberg table, use DataStreamWriter:
val tableIdentifier: String = ... data.writeStream .format("iceberg") .outputMode("append") .trigger(Trigger.ProcessingTime(1, TimeUnit.MINUTES)) .option("path", tableIdentifier) .option("checkpointLocation", checkpointPath) .start()
The tableIdentifier can be:
hdfs://nn:8020/path/to/tabledatabase.table_nameIceberg doesn‘t support “continuous processing”, as it doesn’t provide the interface to “commit” the output.
Iceberg supports append and complete output modes:
append: appends the rows of every micro-batch to the tablecomplete: replaces the table contents every micro-batchThe table should be created in prior to start the streaming query. Refer SQL create table on Spark page to see how to create the Iceberg table.
Iceberg requires the data to be sorted according to the partition spec per task (Spark partition) in prior to write against partitioned table. For batch queries you're encouraged to do explicit sort to fulfill the requirement (see here), but the approach would bring additional latency as repartition and sort are considered as heavy operations for streaming workload. To avoid additional latency, you can enable fanout writer to eliminate the requirement.
val tableIdentifier: String = ... data.writeStream .format("iceberg") .outputMode("append") .trigger(Trigger.ProcessingTime(1, TimeUnit.MINUTES)) .option("path", tableIdentifier) .option("fanout-enabled", "true") .option("checkpointLocation", checkpointPath) .start()
Fanout writer opens the files per partition value and doesn‘t close these files till write task is finished. This functionality is discouraged for batch query, as explicit sort against output rows isn’t expensive for batch workload.
Streaming queries can create new table versions quickly, which creates lots of table metadata to track those versions. Maintaining metadata by tuning the rate of commits, expiring old snapshots, and automatically cleaning up metadata files is highly recommended.
Having high rate of commits would produce lots of data files, manifests, and snapshots which leads the table hard to maintain. We encourage having trigger interval 1 minute at minimum, and increase the interval if needed.
The triggers section in Structured Streaming Programming Guide documents how to configure the interval.
Each micro-batch written to a table produces a new snapshot, which are tracked in table metadata until they are expired to remove the metadata and any data files that are no longer needed. Snapshots accumulate quickly with frequent commits, so it is highly recommended that tables written by streaming queries are regularly maintained.
The amount of data written in a micro batch is typically small, which can cause the table metadata to track lots of small files. Compacting small files into larger files reduces the metadata needed by the table, and increases query efficiency.
To optimize write latency on streaming workload, Iceberg may write the new snapshot with a “fast” append that does not automatically compact manifests. This could lead lots of small manifest files. Manifests can be rewritten to optimize queries and to compact.