Use liquid clustering for Delta tables

Important

Delta Lake liquid clustering is available in Public Preview in Databricks Runtime 13.2 and above.

Delta Lake liquid clustering replaces table partitioning and ZORDER to simplify data layout decisions and optimize query performance. Liquid clustering provides flexibility to redefine clustering keys without rewriting existing data, allowing data layout to evolve alongside analytic needs over time.

Databricks supports enhance concurrency for Delta tables with liquid clustering enabled. See Row-level concurrency on Databricks.

Warning

Databricks Runtime 13.2 and above is required to write or OPTIMIZE Delta tables with liquid clustering enabled.

What is liquid clustering used for?

Databricks recommends liquid clustering for all new Delta tables. 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 concurrent write requirements.

  • Tables with access patterns that change over time.

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

Enable liquid clustering

You must enable Delta Lake liquid clustering when first creating a table. Clustering is not compatible with partitioning or ZORDER, and requires that the Databricks client manages all layout and optimization operations for data in your table. 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:

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

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

-- Using a LIKE statement to copy configurations
CREATE TABLE table3 LIKE table1;

Warning

Tables created with liquid clustering enabled have numerous Delta table features enabled at creation and use Delta writer version 7 and reader version 3. Table protocol versions cannot be downgraded, and tables with clustering enabled are not readable by Delta Lake clients that do not support all enabled Delta reader protocol table features. See How does Databricks manage Delta Lake feature compatibility?.

Choose clustering keys

Databricks recommends choosing clustering keys based on commonly used query filters. Clustering keys can be defined in any order. If two columns are correlated, you only need to add one of them as a clustering key.

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

Current data optimization technique

Recommendation for clustering keys

Hive-style partitioning

Use partition columns as clustering keys.

Z-order indexing

Use the ZORDER BY columns as clustering keys.

Hive-style partitioning and Z-order

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

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

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

Row-level concurrency on Databricks

Databricks provides row-level concurrency for clustered tables that can reduce the number of conflicts between concurrent write operations, including OPTIMIZE, INSERT, MERGE, UPDATE, and DELETE operations.

To reduce the likelihood of row-level conflicts, you can filter concurrent queries, as in the following example:

INSERT INTO table_name REPLACE WHERE column_name = "some_value" SELECT * FROM staging_table;

See What is row-level concurrency?.

Write data to a clustered table

You must use a Delta writer client that supports all Delta write protocol table features used by liquid clustering. On Databricks, you must use Databricks Runtime 13.2 and above.

Most operations do not automatically cluster data on write. Operations that cluster on write include the following:

  • INSERT INTO operations

  • CTAS statements

  • COPY INTO from Parquet format

  • spark.write.format("delta").mode("append")

Note

Clustering on write is a best effort application, and is not applied in the following situations:

  • If a write operation exceeds 512GB of data.

  • If the SELECT subquery contains a transformation, filter, or join.

  • If projected columns are not the same as the source table.

Because not all operations apply liquid clustering, Databricks recommends frequently running OPTIMIZE to ensure that all data is efficiently clustered.

How to trigger clustering

To trigger clustering, you must use Databricks Runtime 13.2 or above. 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. Data files with clustering keys that do not match data to be clustered are not rewritten.

For best performance, Databricks recommends scheduling regular OPTIMIZE jobs to cluster data. For tables experiencing many updates or inserts, Databricks recommends scheduling an OPTIMIZE job every one or two hours. Because liquid clustering is incremental, most OPTIMIZE jobs for clustered tables run quickly.

Read data from a clustered table

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

SELECT * FROM table_name WHERE cluster_key_column_name = "some_value";

Change clustering keys

You can change clustering keys 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 keys, 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 keys to NONE, as in the following example:

ALTER TABLE table_name CLUSTER BY NONE;

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

See how table is clustered

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

DESCRIBE TABLE table_name;

DESCRIBE DETAIL table_name;

Limitations

The following limitations exist:

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

  • You can specify up to 4 columns as clustering keys.

  • Structured Streaming workloads do not support clustering-on-write.