Citus Columnar offers a per-table option for columnar storage to reduce IO requirements though compression and projection pushdown.

Design Trade-Offs

Existing PostgreSQL row tables work well for OLTP:

  • Support UPDATE/DELETE efficiently
  • Efficient single-tuple lookups

The Citus Columnar tables work best for analytic or DW workloads:

  • Compression
  • Doesn't read unnecessary columns
  • Efficient VACUUM

Next generation of cstore_fdw

Citus Columnar is the next generation of cstore_fdw.

Benefits of Citus Columnar over cstore_fdw:

  • Citus Columnar is based on the Table Access Method API, which allows it to behave exactly like an ordinary heap (row) table for most operations.
  • Supports Write-Ahead Log (WAL).
  • Supports ROLLBACK.
  • Supports physical replication.
  • Supports recovery, including Point-In-Time Restore (PITR).
  • Supports pg_dump and pg_upgrade without the need for special options or extra steps.
  • Better user experience; simple USINGclause.
  • Supports more features that work on ordinary heap (row) tables.


  • Append-only (no UPDATE/DELETE support)
  • No space reclamation (e.g. rolled-back transactions may still consume disk space)
  • No bitmap index scans
  • No tidscans
  • No sample scans
  • No TOAST support (large values supported inline)
  • No support for ON CONFLICT statements (except DO NOTHING actions with no target specified).
  • No support for tuple locks (SELECT ... FOR SHARE, SELECT ... FOR UPDATE)
  • No support for serializable isolation level
  • Support for PostgreSQL server versions 12+ only
  • No support for foreign keys, unique constraints, or exclusion constraints
  • No support for logical decoding
  • No support for intra-node parallel scans
  • No support for AFTER ... FOR EACH ROW triggers
  • No UNLOGGED columnar tables

Future iterations will incrementally lift the limitations listed above.

User Experience

Create a Columnar table by specifying USING columnar when creating the table.

sql CREATE TABLE my_columnar_table ( id INT, i1 INT, i2 INT8, n NUMERIC, t TEXT ) USING columnar;

Insert data into the table and read from it like normal (subject to the limitations listed above).

To see internal statistics about the table, use VACUUM VERBOSE. Note that VACUUM (without FULL) is much faster on a columnar table, because it scans only the metadata, and not the actual data.


Set options using:

sql alter_columnar_table_set( relid REGCLASS, chunk_group_row_limit INT4 DEFAULT NULL, stripe_row_limit INT4 DEFAULT NULL, compression NAME DEFAULT NULL, compression_level INT4)

For example:

sql SELECT alter_columnar_table_set( 'my_columnar_table', compression => 'none', stripe_row_limit => 10000);

The following options are available:

  • compression: [none|pglz|zstd|lz4|lz4hc] - set the compression type for newly-inserted data. Existing data will not be recompressed/decompressed. The default value is zstd (if support has been compiled in).
  • compression_level: <integer> - Sets compression level. Valid settings are from 1 through 19. If the compression method does not support the level chosen, the closest level will be selected instead.
  • stripe_row_limit: <integer> - the maximum number of rows per stripe for newly-inserted data. Existing stripes of data will not be changed and may have more rows than this maximum value. The default value is 150000.
  • chunk_group_row_limit: <integer> - the maximum number of rows per chunk for newly-inserted data. Existing chunks of data will not be changed and may have more rows than this maximum value. The default value is 10000.

View options for all tables with:

sql SELECT * FROM columnar.options;

You can also adjust options with a SET command of one of the following GUCs:

  • columnar.compression
  • columnar.compression_level
  • columnar.stripe_row_limit
  • columnar.chunk_group_row_limit

GUCs only affect newly-created tables, not any newly-created stripes on an existing table.


Columnar tables can be used as partitions; and a partitioned table may be made up of any combination of row and columnar partitions.

```sql CREATE TABLE parent(ts timestamptz, i int, n numeric, s text) PARTITION BY RANGE (ts);

-- columnar partition CREATE TABLE p0 PARTITION OF parent FOR VALUES FROM ('2020-01-01') TO ('2020-02-01') USING COLUMNAR; -- columnar partition CREATE TABLE p1 PARTITION OF parent FOR VALUES FROM ('2020-02-01') TO ('2020-03-01') USING COLUMNAR; -- row partition CREATE TABLE p2 PARTITION OF parent FOR VALUES FROM ('2020-03-01') TO ('2020-04-01');

INSERT INTO parent VALUES ('2020-01-15', 10, 100, 'one thousand'); -- columnar INSERT INTO parent VALUES ('2020-02-15', 20, 200, 'two thousand'); -- columnar INSERT INTO parent VALUES ('2020-03-15', 30, 300, 'three thousand'); -- row ```

When performing operations on a partitioned table with a mix of row and columnar partitions, take note of the following behaviors for operations that are supported on row tables but not columnar (e.g. UPDATE, DELETE, tuple locks, etc.):

  • If the operation is targeted at a specific row partition (e.g. UPDATE p2 SET i = i + 1), it will succeed; if targeted at a specified columnar partition (e.g. UPDATE p1 SET i = i + 1), it will fail.
  • If the operation is targeted at the partitioned table and has a WHERE clause that excludes all columnar partitions (e.g. UPDATE parent SET i = i + 1 WHERE ts = '2020-03-15'), it will succeed.
  • If the operation is targeted at the partitioned table, but does not exclude all columnar partitions, it will fail; even if the actual data to be updated only affects row tables (e.g. UPDATE parent SET i = i + 1 WHERE n = 300).

Note that Citus Columnar supports btree and hashindexes (and the constraints requiring them) but does not support gist, gin, spgist and brin indexes. For this reason, if some partitions are columnar and if the index is not supported by Citus Columnar, then it's impossible to create indexes on the partitioned (parent) table directly. In that case, you need to create the index on the individual row partitions. Similarly for the constraints that require indexes, e.g.:

sql CREATE INDEX p2_ts_idx ON p2 (ts); CREATE UNIQUE INDEX p2_i_unique ON p2 (i); ALTER TABLE p2 ADD UNIQUE (n);

Converting Between Row and Columnar

Note: ensure that you understand any advanced features that may be used with the table before converting it (e.g. row-level security, storage options, constraints, inheritance, etc.), and ensure that they are reproduced in the new table or partition appropriately. LIKE, used below, is a shorthand that works only in simple cases.

sql CREATE TABLE my_table(i INT8 DEFAULT '7'); INSERT INTO my_table VALUES(1); -- convert to columnar SELECT alter_table_set_access_method('my_table', 'columnar'); -- back to row SELECT alter_table_set_access_method('my_table', 'heap');

Performance Microbenchmark

Important: This microbenchmark is not intended to represent any real workload. Compression ratios, and therefore performance, will depend heavily on the specific workload. This is only for the purpose of illustrating a "columnar friendly" contrived workload that showcases the benefits of columnar.


```sql CREATE TABLE perf_row( id INT8, ts TIMESTAMPTZ, customer_id INT8, vendor_id INT8, name TEXT, description TEXT, value NUMERIC, quantity INT4 );

CREATE TABLE perf_columnar(LIKE perf_row) USING COLUMNAR; ```


sql CREATE OR REPLACE FUNCTION random_words(n INT4) RETURNS TEXT LANGUAGE plpython2u AS $$ import random t = '' words = ['zero','one','two','three','four','five','six','seven','eight','nine','ten'] for i in xrange(0,n): if (i != 0): t += ' ' r = random.randint(0,len(words)-1) t += words[r] return t $$;

```sql INSERT INTO perf_row SELECT g, -- id '2020-01-01'::timestamptz + ('1 minute'::interval * g), -- ts (random() * 1000000)::INT4, -- customer_id (random() * 100)::INT4, -- vendor_id random_words(7), -- name random_words(100), -- description (random() * 100000)::INT4/100.0, -- value (random() * 100)::INT4 -- quantity FROM generate_series(1,75000000) g;

INSERT INTO perf_columnar SELECT * FROM perf_row; ```

Compression Ratio

``` => SELECT pg_total_relation_size('perf_row')::numeric/pg_total_relation_size('perf_columnar') AS compression_ratio;


5.3958044063457513 (1 row) ```

The overall compression ratio of columnar table, versus the same data stored with row storage, is 5.4X.

=> VACUUM VERBOSE perf_columnar; INFO: statistics for "perf_columnar": storage id: 10000000000 total file size: 8761368576, total data size: 8734266196 compression rate: 5.01x total row count: 75000000, stripe count: 500, average rows per stripe: 150000 chunk count: 60000, containing data for dropped columns: 0, zstd compressed: 60000

VACUUM VERBOSE reports a smaller compression ratio, because it only averages the compression ratio of the individual chunks, and does not account for the metadata savings of the columnar format.


  • Azure VM: Standard D2s v3 (2 vcpus, 8 GiB memory)
  • Linux (ubuntu 18.04)
  • Data Drive: Standard HDD (512GB, 500 IOPS Max, 60 MB/s Max)
  • PostgreSQL 13 (--with-llvm, --with-python)
  • shared_buffers = 128MB
  • max_parallel_workers_per_gather = 0
  • jit = on

Note: because this was run on a system with enough physical memory to hold a substantial fraction of the table, the IO benefits of columnar won't be entirely realized by the query runtime unless the data size is substantially increased.


```sql -- OFFSET 1000 so that no rows are returned, and we collect only timings

SELECT vendor_id, SUM(quantity) FROM perf_row GROUP BY vendor_id OFFSET 1000; SELECT vendor_id, SUM(quantity) FROM perf_row GROUP BY vendor_id OFFSET 1000; SELECT vendor_id, SUM(quantity) FROM perf_row GROUP BY vendor_id OFFSET 1000; SELECT vendor_id, SUM(quantity) FROM perf_columnar GROUP BY vendor_id OFFSET 1000; SELECT vendor_id, SUM(quantity) FROM perf_columnar GROUP BY vendor_id OFFSET 1000; SELECT vendor_id, SUM(quantity) FROM perf_columnar GROUP BY vendor_id OFFSET 1000; ```

Timing (median of three runs): * row: 436s * columnar: 16s * speedup: 27X