Use liquid clustering for Delta tables

Liquid clustering improves the existing partitioning and ZORDER techniques by simplifying data layout decisions in order to optimize query performance. Liquid clustering provides flexibility to redefine clustering columns without rewriting existing data, allowing data layout to evolve alongside analytic needs over time.

Note

This feature is available in Delta Lake 3.1.0 and above. See Limitations.

What is liquid clustering used for?

The following are examples of scenarios that benefit from clustering:

  • Tables often filtered by high cardinality columns.

  • Tables with significant skew in data distribution.

  • Tables that grow quickly and require maintenance and tuning effort.

  • Tables with access patterns that change over time.

  • Tables where a typical partition column could leave the table with too many or too few partitions.

Enable liquid clustering

You must enable liquid clustering when creating a table. Clustering is not compatible with partitioning or ZORDER. Once enabled, run OPTIMIZE jobs as normal to incrementally cluster data. See How to trigger clustering.

To enable liquid clustering, add the CLUSTER BY phrase to a table creation statement, as in the examples below:

Note

In Delta Lake 3.2 and above, you can use DeltaTable API in Python or Scala to enable liquid clustering.

-- Create an empty table
CREATE TABLE table1(col0 int, col1 string) USING DELTA CLUSTER BY (col0);

-- Using a CTAS statement
CREATE TABLE table2 CLUSTER BY (col0)  -- specify clustering after table name, not in subquery
AS SELECT * FROM table1;

# Create an empty table
DeltaTable.create()
  .tableName("table1")
  .addColumn("col0", dataType = "INT")
  .addColumn("col1", dataType = "STRING")
  .clusterBy("col0")
  .execute()

// Create an empty table
DeltaTable.create()
  .tableName("table1")
  .addColumn("col0", dataType = "INT")
  .addColumn("col1", dataType = "STRING")
  .clusterBy("col0")
  .execute()

Warning

Tables created with liquid clustering have Clustering and DomainMetadata table features enabled (both writer features) and use Delta writer version 7 and reader version 1. Table protocol versions cannot be downgraded. See How does Delta Lake manage feature compatibility?.

Choose clustering columns

Clustering columns can be defined in any order. If two columns are correlated, you only need to add one of them as a clustering column.

If you’re converting an existing table, consider the following recommendations:

Current data optimization technique

Recommendation for clustering columns

Hive-style partitioning

Use partition columns as clustering columns.

Z-order indexing

Use the ZORDER BY columns as clustering columns.

Hive-style partitioning and Z-order

Use both partition columns and ZORDER BY columns as clustering columns.

Generated columns to reduce cardinality (for example, date for a timestamp)

Use the original column as a clustering column, and don’t create a generated column.

Write data to a clustered table

You must use a Delta writer client that supports Clustering and DomainMetadata table features.

How to trigger clustering

Use the OPTIMIZE command on your table, as in the following example:

OPTIMIZE table_name;

Liquid clustering is incremental, meaning that data is only rewritten as necessary to accommodate data that needs to be clustered. Already clustered data files with different clustering columns are not rewritten.

Read data from a clustered table

You can read data in a clustered table using any Delta Lake client. For best query results, include clustering columns in your query filters, as in the following example:

SELECT * FROM table_name WHERE clustering_column_name = "some_value";

Change clustering columns

You can change clustering columns for a table at any time by running an ALTER TABLE command, as in the following example:

ALTER TABLE table_name CLUSTER BY (new_column1, new_column2);

When you change clustering columns, subsequent OPTIMIZE and write operations use the new clustering approach, but existing data is not rewritten.

You can also turn off clustering by setting the columns to NONE, as in the following example:

ALTER TABLE table_name CLUSTER BY NONE;

Setting cluster columns to NONE does not rewrite data that has already been clustered, but prevents future OPTIMIZE operations from using clustering columns.

See how table is clustered

You can use DESCRIBE DETAIL commands to see the clustering columns for a table, as in the following examples:

DESCRIBE DETAIL table_name;

Limitations

The following limitations exist:

  • You can only specify columns with statistics collected for clustering columns. By default, the first 32 columns in a Delta table have statistics collected.

  • You can specify up to 4 clustering columns.

Important

In Delta Lake 3.1, users needs to enable the feature flag spark.databricks.delta.clusteredTable.enableClusteringTablePreview to use liquid clustering. The following features are not supported in this preview:

  • ZCube based incremental clustering

  • ALTER TABLE ... CLUSTER BY to change clustering columns

  • DESCRIBE DETAIL to inspect the current clustering columns

In Delta Lake 3.2, the preview flag is removed and the above features are supported.