Compact data files with optimize on Delta Lake

See OPTIMIZE.

Delta Lake on Azure Databricks can improve the speed of read queries from a table. One way to improve this speed is to coalesce small files into larger ones.

Note

In Databricks Runtime 13.3 and above, Databricks recommends using clustering for Delta table layout. See Use liquid clustering for Delta tables.

Databricks recommends using predictive optimization to automatically run OPTIMIZE for Delta tables. See Predictive optimization for Delta Lake.

Syntax examples

You trigger compaction by running the OPTIMIZE command:

SQL

OPTIMIZE delta.`/data/events`

Python

from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/data/events")
deltaTable.optimize().executeCompaction()

Scala

import io.delta.tables._
val deltaTable = DeltaTable.forPath(spark, "/data/events")
deltaTable.optimize().executeCompaction()

or, alternately:

SQL

OPTIMIZE events

Python

from delta.tables import *
deltaTable = DeltaTable.forName(spark, "events")
deltaTable.optimize().executeCompaction()

Scala

import io.delta.tables._
val deltaTable = DeltaTable.forName(spark, "events")
deltaTable.optimize().executeCompaction()

If you have a large amount of data and only want to optimize a subset of it, you can specify an optional partition predicate using WHERE:

SQL

OPTIMIZE events WHERE date >= '2022-11-18'

Python

from delta.tables import *
deltaTable = DeltaTable.forName(spark, "events")
deltaTable.optimize().where("date='2021-11-18'").executeCompaction()

Scala

import io.delta.tables._
val deltaTable = DeltaTable.forName(spark, "events")
deltaTable.optimize().where("date='2021-11-18'").executeCompaction()

Note

  • Bin-packing optimization is idempotent, meaning that if it is run twice on the same dataset, the second run has no effect.
  • Bin-packing aims to produce evenly-balanced data files with respect to their size on disk, but not necessarily number of tuples per file. However, the two measures are most often correlated.
  • Python and Scala APIs for executing OPTIMIZE operation are available from Databricks Runtime 11.0 and above.

Readers of Delta tables use snapshot isolation, which means that they are not interrupted when OPTIMIZE removes unnecessary files from the transaction log. OPTIMIZE makes no data related changes to the table, so a read before and after an OPTIMIZE has the same results. Performing OPTIMIZE on a table that is a streaming source does not affect any current or future streams that treat this table as a source. OPTIMIZE returns the file statistics (min, max, total, and so on) for the files removed and the files added by the operation. Optimize stats also contains the Z-Ordering statistics, the number of batches, and partitions optimized.

You can also compact small files automatically using auto compaction. See Auto compaction for Delta Lake on Azure Databricks.

How often should I run OPTIMIZE?

When you choose how often to run OPTIMIZE, there is a trade-off between performance and cost. For better end-user query performance, run OPTIMIZE more often. This will incur a higher cost because of the increased resource usage. To optimize cost, run it less often.

Databricks recommends that you start by running OPTIMIZE on a daily basis, and then adjust the frequency to balance cost and performance trade-offs.

What’s the best instance type to run OPTIMIZE (bin-packing and Z-Ordering) on?

Both operations are CPU intensive operations doing large amounts of Parquet decoding and encoding.

Databricks recommends Compute optimized instance types. OPTIMIZE also benefits from attached SSDs.