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Async Compaction Deployment Models

2 min read

We will look at different deployment models for executing compactions asynchronously.


For Merge-On-Read table, data is stored using a combination of columnar (e.g parquet) + row based (e.g avro) file formats. Updates are logged to delta files & later compacted to produce new versions of columnar files synchronously or asynchronously. One of th main motivations behind Merge-On-Read is to reduce data latency when ingesting records. Hence, it makes sense to run compaction asynchronously without blocking ingestion.

Async Compaction

Async Compaction is performed in 2 steps:

  1. Compaction Scheduling: This is done by the ingestion job. In this step, Hudi scans the partitions and selects file slices to be compacted. A compaction plan is finally written to Hudi timeline.
  2. Compaction Execution: A separate process reads the compaction plan and performs compaction of file slices.

Deployment Models

There are few ways by which we can execute compactions asynchronously.

Spark Structured Streaming

With 0.6.0, we now have support for running async compactions in Spark Structured Streaming jobs. Compactions are scheduled and executed asynchronously inside the streaming job. Async Compactions are enabled by default for structured streaming jobs on Merge-On-Read table.

Here is an example snippet in java

import org.apache.hudi.DataSourceWriteOptions;
import org.apache.hudi.HoodieDataSourceHelpers;
import org.apache.hudi.config.HoodieCompactionConfig;
import org.apache.hudi.config.HoodieWriteConfig;

import org.apache.spark.sql.streaming.OutputMode;
import org.apache.spark.sql.streaming.ProcessingTime;

DataStreamWriter<Row> writer = streamingInput.writeStream().format("org.apache.hudi")
.option(DataSourceWriteOptions.OPERATION_OPT_KEY(), operationType)
.option(DataSourceWriteOptions.TABLE_TYPE_OPT_KEY(), tableType)
.option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY(), "_row_key")
.option(DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY(), "partition")
.option(DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY(), "timestamp")
.option(HoodieCompactionConfig.INLINE_COMPACT_NUM_DELTA_COMMITS_PROP, "10")
.option(DataSourceWriteOptions.ASYNC_COMPACT_ENABLE_OPT_KEY(), "true")
.option(HoodieWriteConfig.TABLE_NAME, tableName).option("checkpointLocation", checkpointLocation)
writer.trigger(new ProcessingTime(30000)).start(tablePath);

DeltaStreamer Continuous Mode

Hudi DeltaStreamer provides continuous ingestion mode where a single long running spark application
ingests data to Hudi table continuously from upstream sources. In this mode, Hudi supports managing asynchronous compactions. Here is an example snippet for running in continuous mode with async compactions

spark-submit --packages org.apache.hudi:hudi-utilities-bundle_2.11:0.6.0 \
--class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer \
--table-type MERGE_ON_READ \
--target-base-path <hudi_base_path> \
--target-table <hudi_table> \
--source-class org.apache.hudi.utilities.sources.JsonDFSSource \
--source-ordering-field ts \
--schemaprovider-class org.apache.hudi.utilities.schema.FilebasedSchemaProvider \
--props /path/to/ \

Hudi CLI

Hudi CLI is yet another way to execute specific compactions asynchronously. Here is an example

hudi:trips->compaction run --tableName <table_name> --parallelism <parallelism> --compactionInstant <InstantTime>

Hudi Compactor Script

Hudi provides a standalone tool to also execute specific compactions asynchronously. Here is an example

spark-submit --packages org.apache.hudi:hudi-utilities-bundle_2.11:0.6.0 \
--class org.apache.hudi.utilities.HoodieCompactor \
--base-path <base_path> \
--table-name <table_name> \
--instant-time <compaction_instant> \
--schema-file <schema_file>