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Use liquid clustering for Delta tables

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. Liquid clustering applies to both Streaming Tables and Materialized Views.

important

Databricks recommends using Databricks Runtime 15.2 and above for all tables with liquid clustering enabled. Public preview support with limitations is available in Databricks Runtime 13.3 LTS and above.

note

Tables with liquid clustering enabled support row-level concurrency in Databricks Runtime 13.3 LTS and above. Row-level concurrency is generally available in Databricks Runtime 14.2 and above for all tables with deletion vectors enabled. See Isolation levels and write conflicts on Databricks.

What is liquid clustering used for?​

Databricks recommends liquid clustering for all new Delta tables, which includes both Streaming Tables (STs) and Materialized Views (MVs). 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 can enable liquid clustering on an existing table or during table creation. Clustering is not compatible with partitioning or ZORDER, and requires that you use Databricks to manage all layout and optimization operations for data in your table. After liquid clustering is enabled, run OPTIMIZE jobs as usual 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 Databricks Runtime 14.2 and above, you can use DataFrame APIs and DeltaTable API in Python or Scala to enable liquid clustering.

SQL
-- Create an empty table
CREATE TABLE table1(col0 int, col1 string) 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;

In Databricks Runtime 16.0 and above, you can create tables with liquid clustering enabled using Structured Streaming writes, as in the following examples:

Python
(spark.readStream.table("source_table")
  .writeStream
  .clusterBy("column_name")
  .option("checkpointLocation", checkpointPath)
  .toTable("target_table")
)
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. You can override the enablement of some of these features. See Override default feature enablement (optional).

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 Delta Lake feature compatibility and protocols.

You can enable liquid clustering on an existing unpartitioned Delta table using the following syntax:

SQL
ALTER TABLE <table_name>
CLUSTER BY (<clustering_columns>)
important

Default behavior does not apply clustering to previously written data. To force reclustering for all records, you must use OPTIMIZE FULL. See Force reclustering for all records.

To remove clustering keys, use the following syntax:

SQL
ALTER TABLE table_name CLUSTER BY NONE;

Automatic liquid clustering​

Preview

Automatic liquid clustering is in Public Preview.

In Databricks Runtime 15.4 LTS and above, you can enable automatic liquid clustering for Unity Catalog managed tables. With automatic liquid clustering enabled, Databricks intelligently chooses clustering keys to optimize query performance. You enable automatic liquid clustering using the CLUSTER BY AUTO clause.

When enabled, automatic key selection and clustering operations run asynchronously as a maintenance operation and require that predictive optimization is enabled for the table. See Predictive optimization for Unity Catalog managed tables.

To identify clustering keys, Databricks analyzes the table's historical query workload and identifies the best candidate columns. Clustering keys are changed when the predicted cost savings from data skipping improvements outweigh the data clustering cost.

If the way you query your data changes over time or query performance indicates changes in your data distributions, automatic liquid clustering selects new keys to optimize performance.

If a key was not selected by automatic liquid clustering, the reason can be:

  • The table is too small to benefit from liquid clustering.
  • The table already has a good clustering scheme. For example, it has good keys that were applied once before, or the insertion order already performs well for the given query pattern, such as data inserted in chronological order and queried on timestamp.
  • The table does not have frequent queries.
  • You are not using Databricks Runtime 15.4 LTS or above.

You can apply automatic liquid clustering for all Unity Catalog managed tables, regardless of data and query characteristics. These features provide intelligent optimization of data layout based on your data usage patterns, and the heuristics will decide whether it's cost-beneficial to select clustering keys.

note

You can read or write tables with automatic clustering enabled from all Databricks Runtime versions that support liquid clustering. However, intelligent key selection relies on metadata introduced in Databricks Runtime 15.4 LTS. Use Databricks Runtime 15.4 LTS or above to ensure that automatically selected keys benefit all of your workloads and that these workloads are considered when selecting new keys.

Enable or disable automatic clustering​

To enable or disable automatic liquid clustering on a new or existing table, use the following syntax:

SQL
-- Create an empty table.
CREATE OR REPLACE TABLE table1(column01 int, column02 string) CLUSTER BY AUTO;

-- Enable automatic liquid clustering on an existing table,
-- including tables that previously had manually specified keys.
ALTER TABLE table1 CLUSTER BY AUTO;

-- Disable automatic liquid clustering on an existing table.
ALTER TABLE table1 CLUSTER BY NONE;

-- Disable automatic liquid clustering by setting the clustering keys
-- to chosen clustering columns or new columns.
ALTER TABLE table1 CLUSTER BY (column01, column02);
note

If you run CREATE OR REPLACE table_name without specifying CLUSTER BY AUTO and the table already exists and has automatic liquid clustering enabled, the AUTO setting and the clustering columns for the table (if applied) are preserved when the table is replaced. Predictive optimization also maintains the historical query workload for this table to identify the best clustering keys.

Check if automatic clustering is enabled​

To check if a table has automatic liquid clustering enabled, use DESCRIBE TABLE or SHOW TBLPROPERTIES.

If automatic liquid clustering is enabled, the clusterByAuto property is set to true. The clusteringColumns property shows the current clustering columns that were automatically or manually selected.

Override default feature enablement (optional)​

You can override default behavior that enables Delta table features during liquid clustering enablement. This prevents the reader and writer protocols associated with those table features from being upgraded. You must have an existing table to complete the following steps:

  1. Use ALTER TABLE to set the table property that disables one or more features. For example, to disable deletion vectors run the following:

    SQL
    ALTER TABLE table_name SET TBLPROPERTIES ('delta.enableDeletionVectors' = false);

  2. Enable liquid clustering on the table by running the following:

    SQL
    ALTER TABLE <table_name>
    CLUSTER BY (<clustering_columns>)

The following table provides information on the Delta features you can override and how enablement impacts compatibility with Databricks Runtime versions.

Delta feature

Runtime compatibility

Property to override enablement

Impact of disablement on liquid clustering

Deletion vectors

Reads and writes require Databricks Runtime 12.2 LTS and above.

'delta.enableDeletionVectors' = false

Row-level concurrency is disabled, making transactions and clustering operations more likely to conflict. See Write conflicts with row-level concurrency.

DELETE, MERGE, and UPDATE commands might run slower.

Row tracking

Writes require Databricks Runtime 13.3 LTS and above. Can be read from any Databricks Runtime version.

'delta.enableRowTracking' = false

Row-level concurrency is disabled, making transactions and clustering operations more likely to conflict. See Write conflicts with row-level concurrency.

Checkpoints V2

Reads and writes require Databricks Runtime 13.3 LTS and above.

'delta.checkpointPolicy' = 'classic'

No impact on liquid clustering behavior.

Choose clustering keys​

Databricks recommends automatic liquid clustering for supported tables. See Automatic liquid clustering.

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

You can specify up to four clustering keys. For smaller tables (under 10 TB), using more clustering keys (for example, four) can degrade performance when filtering on a single column compared to using fewer clustering keys (for example, two). However, as table size increases, the performance difference with using more clustering keys for single-column queries becomes negligible.

You can only specify columns that have statistics collected as clustering keys. By default, the first 32 columns in a Delta table have statistics collected. See Specify Delta statistics columns.

Clustering supports the following data types for clustering keys:

  • Date
  • Timestamp
  • TimestampNTZ (requires Databricks Runtime 14.3 LTS or above)
  • String
  • Integer
  • Long
  • Short
  • Float
  • Double
  • Decimal
  • Byte

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.

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 13.3 LTS and above.

Operations that cluster on write include the following:

  • INSERT INTO operations
  • CTAS and RTAS statements
  • COPY INTO from Parquet format
  • spark.write.mode("append")

Structured Streaming writes never trigger clustering on write. Additional limitations apply. See Limitations.

Clustering on write only triggers when data in the transaction meets a size threshold. These thresholds vary by the number of clustering columns and are lower for Unity Catalog managed tables than other Delta tables.

Number of clustering columns

Threshold size for Unity Catalog managed tables

Threshold size for other Delta tables

1

64 MB

256 MB

2

256 MB

1 GB

3

512 MB

2 GB

4

1 GB

4 GB

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

How to trigger clustering​

Predictive optimization automatically runs OPTIMIZE commands for enabled tables. See Predictive optimization for Unity Catalog managed tables.

To trigger clustering, you must use Databricks Runtime 13.3 LTS or above. Use the OPTIMIZE command on your table, as in the following example:

SQL
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.

Force reclustering for all records​

In Databricks Runtime 16.0 and above, you can force reclustering of all records in a table with the following syntax:

SQL
OPTIMIZE table_name FULL;
important

Running OPTIMIZE FULL reclusters all existing data as necessary. For large tables that have not previously been clustered on the specified keys, this operation might take hours.

Run OPTIMIZE FULL when you enable clustering for the first time or change clustering keys. If you have previously run OPTIMIZE FULL and there has been no change to clustering keys, OPTIMIZE FULL runs the same as OPTIMIZE. In this scenario, OPTIMIZE uses an incremental approach and only rewrites files that haven't previously been compacted. Always use OPTIMIZE FULL to ensure that data layout reflects the current clustering keys.

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:

SQL
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:

SQL
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:

SQL
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:

SQL
DESCRIBE TABLE table_name;

DESCRIBE DETAIL table_name;

Compatibility for tables with liquid clustering​

Tables created with liquid clustering in Databricks Runtime 14.1 and above use v2 checkpoints by default. You can read and write tables with v2 checkpoints in Databricks Runtime 13.3 LTS and above.

You can disable v2 checkpoints and downgrade table protocols to read tables with liquid clustering in Databricks Runtime 12.2 LTS and above. See Drop a Delta Lake table feature and downgrade table protocol.

Limitations​

The following limitations exist:

  • In Databricks Runtime 15.1 and below, clustering on write does not support source queries that include filters, joins, or aggregations.
  • Structured Streaming workloads do not support clustering-on-write.
  • In Databricks Runtime 15.4 LTS and below, you cannot create a table with liquid clustering enabled using a Structured Streaming write. You can use Structured Streaming to write data to an existing table with liquid clustering enabled.
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