SQL Support and Workarounds¶
As Citus provides distributed functionality by extending PostgreSQL, it is compatible with PostgreSQL constructs. This means that users can use the tools and features that come with the rich and extensible PostgreSQL ecosystem for distributed tables created with Citus.
Citus has 100% SQL coverage for any queries it is able to execute on a single worker node. These kind of queries are common in Multi-tenant Applications when accessing information about a single tenant.
Even cross-node queries (used for parallel computations) support most SQL features. However some SQL features are not supported for queries which combine information from multiple nodes.
Limitations for Cross-Node SQL Queries:
- SELECT … FOR UPDATE work in single-shard queries only
- TABLESAMPLE work in single-shard queries only
- Correlated subqueries are supported only when the correlation is on the Distribution Column and the subqueries conform to subquery pushdown rules (e.g., grouping by the distribution column, with no LIMIT or LIMIT OFFSET clause).
- Recursive CTEs work in single-shard queries only
- Grouping sets work in single-shard queries only
To learn more about PostgreSQL and its features, you can visit the PostgreSQL documentation. For a detailed reference of the PostgreSQL SQL command dialect (which can be used as is by Citus users), you can see the SQL Command Reference.
Before attempting workarounds consider whether Citus is appropriate for your situation. Citus’ current version works well for real-time analytics and multi-tenant use cases.
Citus supports all SQL statements in the multi-tenant use-case. Even in the real-time analytics use-cases, with queries that span across nodes, Citus supports the majority of statements. The few types of unsupported queries are listed in Are there any PostgreSQL features not supported by Citus? Many of the unsupported features have workarounds; below are a number of the most useful.
JOIN a local and a distributed table¶
Attempting to execute a JOIN between a local table “local” and a distributed table “dist” causes an error:
SELECT * FROM local JOIN dist USING (id); /* ERROR: relation local is not distributed STATEMENT: SELECT * FROM local JOIN dist USING (id); ERROR: XX000: relation local is not distributed LOCATION: DistributedTableCacheEntry, metadata_cache.c:711 */
Although you can’t join such tables directly, by wrapping the local table in a subquery or CTE you can make Citus’ recursive query planner copy the local table data to worker nodes. By colocating the data this allows the query to proceed.
-- either SELECT * FROM (SELECT * FROM local) AS x JOIN dist USING (id); -- or WITH x AS (SELECT * FROM local) SELECT * FROM x JOIN dist USING (id);
Remember that the coordinator will send the results in the subquery or CTE to all workers which require it for processing. Thus it’s best to either add the most specific filters and limits to the inner query as possible, or else aggregate the table. That reduces the network overhead which such a query can cause. More about this in Subquery/CTE Network Overhead.
JOIN a local and a reference table¶
Attempting to execute a JOIN between a local table “local” and a reference table “ref” causes an error:
SELECT * FROM local JOIN ref USING (id);
ERROR: relation local is not distributed
Ordinarily a copy of every reference table exists on each worker node, but does not exist on the coordinator. Thus a reference table’s data is not placed for efficient joins with tables local to the coordinator. To allow these kind of joins we can request that Citus place a copy of every reference table on the coordinator as well:
SELECT master_add_node('localhost', 5432, groupid => 0);
This adds the coordinator to Worker node table with a group ID of 0. Joins between reference and local tables will then be possible.
If the reference tables are large there is a risk that they might exhaust the coordinator disk space. Use caution.
Temp Tables: the Workaround of Last Resort¶
In our real-time analytics tutorial we
created a table called
github_events, distributed by the column
user_id. Let’s query it and find the earliest events for a preselected
set of repos, grouped by combinations of event type and event publicity. A
convenient way to do this is with gouping sets. However, as mentioned, this
feature is not yet supported in distributed queries:
-- this won't work SELECT repo_id, event_type, event_public, grouping(event_type, event_public), min(created_at) FROM github_events WHERE repo_id IN (8514, 15435, 19438, 21692) GROUP BY repo_id, ROLLUP(event_type, event_public);
ERROR: could not run distributed query with GROUPING SETS, CUBE, or ROLLUP HINT: Consider using an equality filter on the distributed table's partition column.
There is a trick, though. We can pull the relevant information to the coordinator as a temporary table:
-- grab the data, minus the aggregate, into a local table CREATE TEMP TABLE results AS ( SELECT repo_id, event_type, event_public, created_at FROM github_events WHERE repo_id IN (8514, 15435, 19438, 21692) ); -- now run the aggregate locally SELECT repo_id, event_type, event_public, grouping(event_type, event_public), min(created_at) FROM results GROUP BY repo_id, ROLLUP(event_type, event_public);
. repo_id | event_type | event_public | grouping | min ---------+-------------------+--------------+----------+--------------------- 8514 | PullRequestEvent | t | 0 | 2016-12-01 05:32:54 8514 | IssueCommentEvent | t | 0 | 2016-12-01 05:32:57 19438 | IssueCommentEvent | t | 0 | 2016-12-01 05:48:56 21692 | WatchEvent | t | 0 | 2016-12-01 06:01:23 15435 | WatchEvent | t | 0 | 2016-12-01 05:40:24 21692 | WatchEvent | | 1 | 2016-12-01 06:01:23 15435 | WatchEvent | | 1 | 2016-12-01 05:40:24 8514 | PullRequestEvent | | 1 | 2016-12-01 05:32:54 8514 | IssueCommentEvent | | 1 | 2016-12-01 05:32:57 19438 | IssueCommentEvent | | 1 | 2016-12-01 05:48:56 15435 | | | 3 | 2016-12-01 05:40:24 21692 | | | 3 | 2016-12-01 06:01:23 19438 | | | 3 | 2016-12-01 05:48:56 8514 | | | 3 | 2016-12-01 05:32:54
Creating a temporary table on the coordinator is a last resort. It is limited by the disk size and CPU of the node.