Cluster Management

In this section, we discuss how you can add or remove nodes from your Citus cluster and how you can deal with node failures.


To make moving shards across nodes or re-replicating shards on failed nodes easier, Citus Enterprise comes with a shard rebalancer extension. We discuss briefly about the functions provided by the shard rebalancer as and when relevant in the sections below. You can learn more about these functions, their arguments and usage, in the Cluster Management And Repair Functions reference section.

Choosing Cluster Size

This section explores configuration settings for running a cluster in production.

Shard Count

The number of nodes in a cluster is easy to change (see Scaling the cluster), but the number of shards to distribute among those nodes is more difficult to change after cluster creation. Choosing the shard count for each distributed table is a balance between the flexibility of having more shards, and the overhead for query planning and execution across them.

Multi-Tenant SaaS Use-Case

The optimal choice varies depending on your access patterns for the data. For instance, in the Multi-Tenant Database use-case we recommend choosing between 32 - 128 shards. For smaller workloads say <100GB, you could start with 32 shards and for larger workloads you could choose 64 or 128. This means that you have the leeway to scale from 32 to 128 worker machines.

Real-Time Analytics Use-Case

In the Real-Time Analytics use-case, shard count should be related to the total number of cores on the workers. To ensure maximum parallelism, you should create enough shards on each node such that there is at least one shard per CPU core. We typically recommend creating a high number of initial shards, e.g. 2x or 4x the number of current CPU cores. This allows for future scaling if you add more workers and CPU cores.

However keep in mind that for each query Citus opens one database connection per shard, and these connections are limited. Be careful to keep the shard count small enough that distributed queries won’t often have to wait for a connection. Put another way, the connections needed, (max concurrent queries * shard count), should generally not exceed the total connections possible in the system, (number of workers * max_connections per worker).

Initial Hardware Size

The size of a cluster, in terms of number of nodes and their hardware capacity, is easy to change. (Scaling on Citus Cloud is especially easy.) However you still need to choose an initial size for a new cluster. Here are some tips for a reasonable initial cluster size.

Multi-Tenant SaaS Use-Case

For those migrating to Citus from an existing single-node database instance, we recommend choosing a cluster where the number of worker cores and RAM in total equals that of the original instance. In such scenarios we have seen 2-3x performance improvements because sharding improves resource utilization, allowing smaller indices etc.

The coordinator node needs less memory than workers, so you can choose a compute-optimized machine for running the coordinator. The number of cores required depends on your existing workload (write/read throughput). By default in Citus Cloud the workers use Amazon EC2 instance type R4S, and the coordinator uses C4S.

Real-Time Analytics Use-Case

Total cores: when working data fits in RAM, you can expect a linear performance improvement on Citus proportional to the number of worker cores. To determine the right number of cores for your needs, consider the current latency for queries in your single-node database and the required latency in Citus. Divide current latency by desired latency, and round the result.

Worker RAM: the best case would be providing enough memory that the majority of the working set fits in memory. The type of queries your application uses affect memory requirements. You can run EXPLAIN ANALYZE on a query to determine how much memory it requires.

Scaling the cluster

Citus’s logical sharding based architecture allows you to scale out your cluster without any down time. This section describes how you can add more nodes to your Citus cluster in order to improve query performance / scalability.

Add a worker

Citus stores all the data for distributed tables on the worker nodes. Hence, if you want to scale out your cluster by adding more computing power, you can do so by adding a worker.

To add a new node to the cluster, you first need to add the DNS name or IP address of that node and port (on which PostgreSQL is running) in the pg_dist_node catalog table. You can do so using the master_add_node UDF. Example:

SELECT * from master_add_node('node-name', 5432);

The new node is available for shards of new distributed tables. Existing shards will stay where they are unless redistributed, so adding a new worker may not help performance without further steps.

Rebalance Shards without Downtime

If you want to move existing shards to a newly added worker, Citus Enterprise and Citus Cloud provide a rebalance_table_shards function to make it easier. This function will move the shards of a given table to distribute them evenly among the workers.

SELECT rebalance_table_shards('github_events');

Many products, like multi-tenant SaaS applications, cannot tolerate downtime, and Citus rebalancing is able to honor this requirement on PostgreSQL 10 or above. This means reads and writes from the application can continue with minimal interruption while data is being moved.

How it Works

Citus’ shard rebalancing uses PostgreSQL logical replication to move data from the old shard (called the “publisher” in replication terms) to the new (the “subscriber.”) Logical replication allows application reads and writes to continue uninterrupted while copying shard data. Citus puts a brief write-lock on a shard only during the time it takes to update metadata to promote the subscriber shard as active.

As the PostgreSQL docs explain, the source needs a replica identity configured:

A published table must have a “replica identity” configured in order to be able to replicate UPDATE and DELETE operations, so that appropriate rows to update or delete can be identified on the subscriber side. By default, this is the primary key, if there is one. Another unique index (with certain additional requirements) can also be set to be the replica identity.

In other words, if your distributed table has a primary key defined then it’s ready for shard rebalancing with no extra work. However if it doesn’t have a primary key or an explicitly defined replica identity, then attempting to rebalance it will cause an error. For instance:

-- creating the following table without REPLICA IDENTITY or PRIMARY KEY
CREATE TABLE test_table (key int not null, value text not null);
SELECT create_distributed_table('test_table', 'key');

-- running shard rebalancer with default behavior
SELECT rebalance_table_shards('test_table');

NOTICE:  Moving shard 102040 from localhost:9701 to localhost:9700 ...
ERROR: cannot use logical replication to transfer shards of the
  relation test_table since it doesn't have a REPLICA IDENTITY or
DETAIL:  UPDATE and DELETE commands on the shard will error out during
  logical replication unless there is a REPLICA IDENTIY or PRIMARY KEY.
HINT:  If you wish to continue without a replica identity set the
  shard_transfer_mode to 'force_logical' or 'block_writes'.

Here’s how to fix this error.

First, does the table have a unique index?

If the table to be replicated already has a unique index which includes the distribution column, then choose that index as a replica identity:

-- supposing my_table has unique index my_table_idx
-- which includes distribution column

  USING INDEX my_table_idx;


While REPLICA IDENTITY USING INDEX is fine, we recommend against adding REPLICA IDENTITY FULL to a table. This setting would result in each update/delete doing a full-table-scan on the subscriber side to find the tuple with those rows. In our testing we’ve found this to result in worse performance than even solution four below.

Otherwise, can you add a primary key?

Add a primary key to the table. If the desired key happens to be the distribution column, then it’s quite easy, just add the constraint. Otherwise, a primary key with a non-distribution column must be composite and contain the distribution column too.

Unwilling to add primary key or unique index?

If the distributed table doesn’t have a primary key or replica identity, and adding one is unclear or undesirable, you can still force the use of logical replication on PostgreSQL 10 or above. It’s OK to do this on a table which receives only reads and inserts (no deletes or updates). Include the optional shard_transfer_mode argument of rebalance_table_shards:

SELECT rebalance_table_shards(
  shard_transfer_mode => 'force_logical'

In this situation if an application does attempt an update or delete during replication, then the request will merely return an error. Deletes and writes will become possible again after replication is complete.

What about PostgreSQL 9.x?

On PostgreSQL 9.x and lower, logical replication is not supported. In this case we must fall back to a less efficient solution: locking a shard for writes as we copy it to its new location. Unlike logical replication, this approach introduces downtime for write statements (although read queries continue unaffected).

To choose this replication mode, use the shard_transfer_mode parameter again. Here is how to block writes and use the COPY command for replication:

SELECT rebalance_table_shards(
  shard_transfer_mode => 'block_writes'

Adding a coordinator

The Citus coordinator only stores metadata about the table shards and does not store any data. This means that all the computation is pushed down to the workers and the coordinator does only final aggregations on the result of the workers. Therefore, it is not very likely that the coordinator becomes a bottleneck for read performance. Also, it is easy to boost up the coordinator by shifting to a more powerful machine.

However, in some write heavy use cases where the coordinator becomes a performance bottleneck, users can add another coordinator. As the metadata tables are small (typically a few MBs in size), it is possible to copy over the metadata onto another node and sync it regularly. Once this is done, users can send their queries to any coordinator and scale out performance. If your setup requires you to use multiple coordinators, please contact us.

Dealing With Node Failures

In this sub-section, we discuss how you can deal with node failures without incurring any downtime on your Citus cluster. We first discuss how Citus handles worker failures automatically by maintaining multiple replicas of the data. We also briefly describe how users can replicate their shards to bring them to the desired replication factor in case a node is down for a long time. Lastly, we discuss how you can setup redundancy and failure handling mechanisms for the coordinator.

Worker Node Failures

Citus supports two modes of replication, allowing it to tolerate worker-node failures. In the first model, we use PostgreSQL’s streaming replication to replicate the entire worker-node as-is. In the second model, Citus can replicate data modification statements, thus replicating shards across different worker nodes. They have different advantages depending on the workload and use-case as discussed below:

  1. PostgreSQL streaming replication. This option is best for heavy OLTP workloads. It replicates entire worker nodes by continuously streaming their WAL records to a standby. You can configure streaming replication on-premise yourself by consulting the PostgreSQL replication documentation or use Citus Cloud which is pre-configured for replication and high-availability.
  2. Citus shard replication. This option is best suited for an append-only workload. Citus replicates shards across different nodes by automatically replicating DML statements and managing consistency. If a node goes down, the coordinator node will continue to serve queries by routing the work to the replicas seamlessly. To enable shard replication simply set SET citus.shard_replication_factor = 2; (or higher) before distributing data to the cluster.

Coordinator Node Failures

The Citus coordinator maintains metadata tables to track all of the cluster nodes and the locations of the database shards on those nodes. The metadata tables are small (typically a few MBs in size) and do not change very often. This means that they can be replicated and quickly restored if the node ever experiences a failure. There are several options on how users can deal with coordinator failures.

1. Use PostgreSQL streaming replication: You can use PostgreSQL’s streaming replication feature to create a hot standby of the coordinator. Then, if the primary coordinator node fails, the standby can be promoted to the primary automatically to serve queries to your cluster. For details on setting this up, please refer to the PostgreSQL wiki.

2. Since the metadata tables are small, users can use EBS volumes, or PostgreSQL backup tools to backup the metadata. Then, they can easily copy over that metadata to new nodes to resume operation.

Tenant Isolation


Tenant isolation is a feature of Citus Enterprise Edition and Citus Cloud only.

Citus places table rows into worker shards based on the hashed value of the rows’ distribution column. Multiple distribution column values often fall into the same shard. In the Citus multi-tenant use case this means that tenants often share shards.

However sharing shards can cause resource contention when tenants differ drastically in size. This is a common situation for systems with a large number of tenants – we have observed that the size of tenant data tend to follow a Zipfian distribution as the number of tenants increases. This means there are a few very large tenants, and many smaller ones. To improve resource allocation and make guarantees of tenant QoS it is worthwhile to move large tenants to dedicated nodes.

Citus Enterprise Edition and Citus Cloud provide the tools to isolate a tenant on a specific node. This happens in two phases: 1) isolating the tenant’s data to a new dedicated shard, then 2) moving the shard to the desired node. To understand the process it helps to know precisely how rows of data are assigned to shards.

Every shard is marked in Citus metadata with the range of hashed values it contains (more info in the reference for pg_dist_shard). The Citus UDF isolate_tenant_to_new_shard(table_name, tenant_id) moves a tenant into a dedicated shard in three steps:

  1. Creates a new shard for table_name which (a) includes rows whose distribution column has value tenant_id and (b) excludes all other rows.
  2. Moves the relevant rows from their current shard to the new shard.
  3. Splits the old shard into two with hash ranges that abut the excision above and below.

Furthermore, the UDF takes a CASCADE option which isolates the tenant rows of not just table_name but of all tables co-located with it. Here is an example:

-- This query creates an isolated shard for the given tenant_id and
-- returns the new shard id.

-- General form:

SELECT isolate_tenant_to_new_shard('table_name', tenant_id);

-- Specific example:

SELECT isolate_tenant_to_new_shard('lineitem', 135);

-- If the given table has co-located tables, the query above errors out and
-- advises to use the CASCADE option

SELECT isolate_tenant_to_new_shard('lineitem', 135, 'CASCADE');


│ isolate_tenant_to_new_shard │
│                      102240 │

The new shard(s) are created on the same node as the shard(s) from which the tenant was removed. For true hardware isolation they can be moved to a separate node in the Citus cluster. As mentioned, the isolate_tenant_to_new_shard function returns the newly created shard id, and this id can be used to move the shard:

-- find the node currently holding the new shard
SELECT nodename, nodeport
  FROM pg_dist_placement AS placement,
       pg_dist_node AS node
 WHERE placement.groupid = node.groupid
   AND node.noderole = 'primary'
   AND shardid = 102240;

-- list the available worker nodes that could hold the shard
SELECT * FROM master_get_active_worker_nodes();

-- move the shard to your choice of worker
-- (it will also move any shards created with the CASCADE option)
SELECT master_move_shard_placement(
  'source_host', source_port,
  'dest_host', dest_port);

Note that master_move_shard_placement will also move any shards which are co-located with the specified one, to preserve their co-location.

Viewing Query Statistics


The citus_stat_statements view is a feature of Citus Enterprise Edition and Citus Cloud only.

When administering a Citus cluster it’s useful to know what queries users are running, which nodes are involved, and which execution method Citus is using for each query. Citus records query statistics in a metadata view called citus_stat_statements, named analogously to Postgres’ pg_stat_statments. Whereas pg_stat_statements stores info about query duration and I/O, citus_stat_statements stores info about Citus execution methods and shard partition keys (when applicable).

Citus requires the pg_stat_statements extension to be installed in order to track query statistics. On Citus Cloud this extension will be pre-activated, but on a self-hosted Postgres instance you must load the extension in postgresql.conf via shared_preload_libraries, then create the extension in SQL:

CREATE EXTENSION pg_stat_statements;

Let’s see how this works. Assume we have a table called foo that is hash-distributed by its id column.

-- create and populate distributed table
create table foo ( id int );
select create_distributed_table('foo', 'id');

insert into foo select generate_series(1,100);

We’ll run two more queries, and citus_stat_statements will show how Citus chooses to execute them.

-- counting all rows executes on all nodes, and sums
-- the results on the coordinator
SELECT count(*) FROM foo;

-- specifying a row by the distribution column routes
-- execution to an individual node
SELECT * FROM foo WHERE id = 42;

To find how these queries were executed, ask the stats table:

SELECT * FROM citus_stat_statements;


│  queryid   │ userid │ dbid  │                     query                     │   executor    │ partition_key │ calls │
│ 1496051219 │  16384 │ 16385 │ select count(*) from foo;                     │ real-time     │ NULL          │     1 │
│ 2530480378 │  16384 │ 16385 │ select * from foo where id = $1               │ router        │ 42            │     1 │
│ 3233520930 │  16384 │ 16385 │ insert into foo select generate_series($1,$2) │ insert-select │ NULL          │     1 │

We can see that Citus used the real-time executor to run the count query. This executor fragments the query into constituent queries to run on each node and combines the results on the coordinator node. In the case of the second query (filtering by the distribution column id = $1), Citus determined that it needed the data from just one node. Thus Citus chose the router executor to send the whole query to that node for execution. Lastly, we can see that the insert into foo select… statement ran with the insert-select executor which provides flexibility to run these kind of queries. The insert-select executor must sometimes pull results to the coordinator and push them back down to workers.

So far the information in this view doesn’t give us anything we couldn’t already learn by running the EXPLAIN command for a given query. However in addition to getting information about individual queries, the citus_stat_statements view allows us to answer questions such as “what percentage of queries in the cluster are run by which executors?”

SELECT executor, sum(calls)
FROM citus_stat_statements
GROUP BY executor;
│   executor    │ sum │
│ insert-select │   1 │
│ real-time     │   1 │
│ router        │   1 │

In a multi-tenant database, for instance, we would expect to see “router” for the vast majority of queries, because the queries should route to individual tenants. Seeing too many real-time queries may indicate that queries do not have the proper filters to match a tenant, and are using unnecessary resources.

We can also find which partition_ids are the most frequent targets of router execution. In a multi-tenant application these would be the busiest tenants.

SELECT partition_key, sum(calls) as total_queries
FROM citus_stat_statements
WHERE coalesce(partition_key, '') <> ''
GROUP BY partition_key
ORDER BY total_queries desc
│ partition_key │ total_queries │
│ 42            │             1 │

Finally, by joining citus_stat_statements with pg_stat_statements we can gain a better view into not only how many queries use different executor types, but how much time each executor spends running queries.

SELECT executor, sum(css.calls), sum(pss.total_time)
FROM citus_stat_statements css
JOIN pg_stat_statements pss USING (queryid)
GROUP BY executor;
│   executor    │ sum │    sum    │
│ insert-select │   1 │ 18.393312 │
│ real-time     │   1 │  3.537155 │
│ router        │   1 │  0.392590 │

Resource Conservation

Limiting Long-Running Queries

Long running queries can hold locks, queue up WAL, or just consume a lot of system resources, so in a production environment it’s good to prevent them from running too long. You can set the statement_timeout parameter on the coordinator and workers to cancel queries that run too long.

-- limit queries to five minutes
  SET statement_timeout TO 300000;
SELECT run_command_on_workers($cmd$
    SET statement_timeout TO 300000;

The timeout is specified in milliseconds.


It’s not possible to make an exception to the timeout for individual queries. Citus does not yet propagate SET LOCAL to the workers, so the statement_timeout GUC cannot be adjusted within a session.


Connection Management

When Citus nodes communicate with one another they consult a GUC for connection parameters and, in the Enterprise Edition of Citus, a table with connection credentials. This gives the database administrator flexibility to adjust parameters for security and efficiency.

To set non-sensitive libpq connection parameters to be used for all node connections, update the citus.node_conninfo GUC:

-- key=value pairs separated by spaces.
-- For example, ssl options:

SET citus.node_conninfo =
  'sslrootcert=/path/to/citus.crt sslmode=verify-full';

There is a whitelist of parameters that the GUC accepts. See the node_conninfo reference for details.

Citus Enterprise Edition includes an extra table used to set sensitive connection credentials. This is fully configurable per host/user. It’s easier than managing .pgpass files through the cluster and additionally supports certificate authentication.

-- only superusers can access this table

INSERT INTO pg_dist_authinfo
  (nodeid, rolename, authinfo)
  (123, 'jdoe', 'password=abc123');

After this INSERT, any query needing to connect to node 123 as the user jdoe will use the supplied password. The documentation for pg_dist_authinfo has more info.

Increasing Worker Security

For your convenience getting started, our multi-node installation instructions direct you to set up the pg_hba.conf on the workers with its authentication method set to “trust” for local network connections. However you might desire more security.

To require that all connections supply a hashed password, update the PostgreSQL pg_hba.conf on every worker node with something like this:

# Require password access to nodes in the local network. The following ranges
# correspond to 24, 20, and 16-bit blocks in Private IPv4 address spaces.
host    all             all                 md5

# Require passwords when the host connects to itself as well
host    all             all               md5
host    all             all             ::1/128                 md5

The coordinator node needs to know roles’ passwords in order to communicate with the workers. In Citus Enterprise the pg_dist_authinfo table can provide that information, as discussed earlier. However in Citus Community Edition the authentication information has to be maintained in a .pgpass file. Edit .pgpass in the postgres user’s home directory, with a line for each combination of worker address and role:


Sometimes workers need to connect to one another, such as during repartition joins. Thus each worker node requires a copy of the .pgpass file as well.

Row-Level Security


Row-level security support is a part of Citus Enterprise. Please contact us to obtain this functionality.

PostgreSQL row-level security policies restrict, on a per-user basis, which rows can be returned by normal queries or inserted, updated, or deleted by data modification commands. This can be especially useful in a multi-tenant Citus cluster because it allows individual tenants to have full SQL access to the database while hiding each tenant’s information from other tenants.

We can implement the separation of tenant data by using a naming convention for database roles that ties into table row-level security policies. We’ll assign each tenant a database role in a numbered sequence: tenant_1, tenant_2, etc. Tenants will connect to Citus using these separate roles. Row-level security policies can compare the role name to values in the tenant_id distribution column to decide whether to allow access.

Here is how to apply the approach on a simplified events table distributed by tenant_id. First create the roles tenant_1 and tenant_2 (it’s easy on Citus Cloud, see Users and Permissions). Then run the following as an administrator:

  tenant_id int,
  id int,
  type text

SELECT create_distributed_table('events','tenant_id');

INSERT INTO events VALUES (1,1,'foo'), (2,2,'bar');

-- assumes that roles tenant_1 and tenant_2 exist
GRANT select, update, insert, delete
  ON events TO tenant_1, tenant_2;

As it stands, anyone with select permissions for this table can see both rows. Users from either tenant can see and update the row of the other tenant. We can solve this with row-level table security policies.

Each policy consists of two clauses: USING and WITH CHECK. When a user tries to read or write rows, the database evaluates each row against these clauses. Existing table rows are checked against the expression specified in USING, while new rows that would be created via INSERT or UPDATE are checked against the expression specified in WITH CHECK.

-- first a policy for the system admin "citus" user
CREATE POLICY admin_all ON events
  TO citus           -- apply to this role
  USING (true)       -- read any existing row
  WITH CHECK (true); -- insert or update any row

-- next a policy which allows role "tenant_<n>" to
-- access rows where tenant_id = <n>
CREATE POLICY user_mod ON events
  USING (current_user = 'tenant_' || tenant_id::text);
  -- lack of CHECK means same condition as USING

-- enforce the policies

Now roles tenant_1 and tenant_2 get different results for their queries:

Connected as tenant_1:

SELECT * FROM events;
│ tenant_id │ id │ type │
│         1 │  1 │ foo  │

Connected as tenant_2:

SELECT * FROM events;
│ tenant_id │ id │ type │
│         2 │  2 │ bar  │
INSERT INTO events VALUES (3,3,'surprise');
ERROR:  42501: new row violates row-level security policy for table "events_102055"

PostgreSQL extensions

Citus provides distributed functionality by extending PostgreSQL using the hook and extension APIs. This allows users to benefit from the features that come with the rich PostgreSQL ecosystem. These features include, but aren’t limited to, support for a wide range of data types (including semi-structured data types like jsonb and hstore), operators and functions, full text search, and other extensions such as PostGIS and HyperLogLog. Further, proper use of the extension APIs enable compatibility with standard PostgreSQL tools such as pgAdmin and pg_upgrade.

As Citus is an extension which can be installed on any PostgreSQL instance, you can directly use other extensions such as hstore, hll, or PostGIS with Citus. However, there are two things to keep in mind. First, while including other extensions in shared_preload_libraries, you should make sure that Citus is the first extension. Secondly, you should create the extension on both the coordinator and the workers before starting to use it.


Sometimes, there might be a few features of the extension that may not be supported out of the box. For example, a few aggregates in an extension may need to be modified a bit to be parallelized across multiple nodes. Please contact us if some feature from your favourite extension does not work as expected with Citus.

In addition to our core Citus extension, we also maintain several others:

  • cstore_fdw - Columnar store for analytics. The columnar nature delivers performance by reading only relevant data from disk, and it may compress data 6x-10x to reduce space requirements for data archival.
  • pg_cron - Run periodic jobs directly from the database.
  • postgresql-topn - Returns the top values in a database according to some criteria. Uses an approximation algorithm to provide fast results with modest compute and memory resources.
  • postgresql-hll - HyperLogLog data structure as a native data type. It’s a fixed-size, set-like structure used for distinct value counting with tunable precision.

Creating a New Database

Each PostgreSQL server can hold multiple databases. However new databases do not inherit the extensions of any others; all desired extensions must be added afresh. To run Citus on a new database, you’ll need to create the database on the coordinator and workers, create the Citus extension within that database, and register the workers in the coordinator database.

On an existing database on the coordinator run:

-- create the new db on coordinator and workers
SELECT run_command_on_workers('CREATE DATABASE newbie;');

-- review the worker nodes registered in current db
SELECT * FROM master_get_active_worker_nodes();

-- switch to new db on coordinator
\c newbie

-- create citus extension in new db

-- register workers in new db
SELECT * from master_add_node('node-name', 5432);
SELECT * from master_add_node('node-name2', 5432);
-- ... for each of them

In the new db on every worker, manually run:


Now the new database will be operating as another Citus cluster.

Checks For Updates and Cluster Statistics

Unless you opt out, Citus checks if there is a newer version of itself during installation and every twenty-four hours thereafter. If a new version is available, Citus emits a notice to the database logs:

a new minor release of Citus (X.Y.Z) is available

During the check for updates, Citus also sends general anonymized information about the running cluster to Citus Data company servers. This helps us understand how Citus is commonly used and thereby improve the product. As explained below, the reporting is opt-out and does not contain personally identifying information about schemas, tables, queries, or data.

What we Collect

  1. Citus checks if there is a newer version of itself, and if so emits a notice to the database logs.
  2. Citus collects and sends these statistics about your cluster:
    • Randomly generated cluster identifier
    • Number of workers
    • OS version and hardware type (output of uname -psr command)
    • Number of tables, rounded to a power of two
    • Total size of shards, rounded to a power of two
    • Whether Citus is running in Docker or natively

Because Citus is an open-source PostgreSQL extension, the statistics reporting code is available for you to audit. See statistics_collection.c.

How to Opt Out

If you wish to disable our anonymized cluster statistics gathering, set the following GUC in postgresql.conf on your coordinator node:

citus.enable_statistics_collection = off

This disables all reporting and in fact all communication with Citus Data servers, including checks for whether a newer version of Citus is available.

If you have super-user SQL access you can also achieve this without needing to find and edit the configuration file. Just execute the following statement in psql:

ALTER SYSTEM SET citus.enable_statistics_collection = 'off';

Since Docker users won’t have the chance to edit this PostgreSQL variable before running the image, we added a Docker flag to disable reports.

# Docker flag prevents reports

docker run -e DISABLE_STATS_COLLECTION=true citusdata/citus:latest