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 Community edition 11.0+ supports fully online shard rebalancing. We discuss briefly the functions provided by the shard rebalancer 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.
Initial Hardware Size
The size of a cluster, in terms of number of nodes and their hardware capacity, is easy to change. (Scaling on our Managed Service 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).
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 downtime. 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 citus_add_node UDF. Example:
SELECT * from citus_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.
If your cluster has very large reference tables, they can slow down the addition of a node. In this case, consider the citus.replicate_reference_tables_on_activate (boolean) GUC.
Also, new nodes synchronize Citus’ metadata upon creation. By default, the sync happens inside a single transaction for consistency. However, in a big cluster with large amounts of metadata, the transaction can run out of memory and fail. If you encounter this situation, you can choose a non-transactional metadata sync mode with the citus.metadata_sync_mode (enum) GUC.
Rebalance Shards without Downtime
Starting in version 11.0, Citus Community edition now supports non-blocking reads and writes during rebalancing.
If you want to move existing shards to a newly added worker, Citus provides a citus_rebalance_start function to make it easier. This function will distribute shards evenly among the workers.
The function is configurable to rebalance shards according to a number of strategies, to best match your database workload. See the function reference to learn which strategy to choose. Here’s an example of rebalancing shards using the default strategy:
Many products, like multi-tenant SaaS applications, cannot tolerate downtime, and on our managed service, 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.
This operation carries out multiple shard moves in a sequential order by default. There are some cases where you may prefer to rebalance faster at the expense of using more resources such as network bandwidth. In those situations, customers are able to configure a rebalance operation to perform a number of shard moves in parallel.
The citus.max_background_task_executors_per_node (integer) GUC allows tasks such as shard rebalancing to operate in parallel. You can increase it from its default value (1) as desired to boost parallelism.
ALTER SYSTEM SET citus.max_background_task_executors_per_node = 2; SELECT pg_reload_conf(); SELECT citus_rebalance_start();
What are the typical use cases?
Scaling out faster when adding new nodes to the cluster
Rebalancing the cluster faster to even out the utilization of nodes
Corner Cases and Gotchas
citus.max_background_task_executors_per_node value limits the number of parallel
task executors in general. Also, shards in the same colocation group will always move
sequentially so parallelism may be limited by the number of colocation groups.
How it Works
Citus’s 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. Here’s how to fix it.
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 ALTER TABLE my_table REPLICA IDENTITY USING INDEX my_table_idx;
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.
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 subsection, we discuss how you can deal with node failures without incurring any downtime on your Citus cluster.
Worker Node Failures
Citus uses PostgreSQL streaming replication, allowing it to tolerate worker-node failures. This option 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 our Managed Service which is pre-configured for replication and high-availability.
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.
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.
Use backup tools: 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.
Starting in version 11.0, Citus Community edition includes tenant isolation functionality!
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 provides 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:
Creates a new shard for
table_namewhich (a) includes rows whose distribution column has value
tenant_idand (b) excludes all other rows.
Moves the relevant rows from their current shard to the new shard.
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 citus_shards WHERE 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 citus_move_shard_placement( 102240, 'source_host', source_port, 'dest_host', dest_port);
citus_move_shard_placement will also move any shards which are co-located with the specified one, to preserve their co-location.
Viewing Query Statistics
Starting in version 11.0, Citus Community edition now includes the citus_stat_statements view!
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 our Managed Service 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
-- 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;
-[ RECORD 1 ]-+---------------------------------------------- queryid | -6844578505338488014 userid | 10 dbid | 13340 query | SELECT count(*) FROM foo; executor | adaptive partition_key | calls | 1 -[ RECORD 2 ]-+---------------------------------------------- queryid | 185453597994293667 userid | 10 dbid | 13340 query | insert into foo select generate_series($1,$2) executor | insert-select partition_key | calls | 1 -[ RECORD 3 ]-+---------------------------------------------- queryid | 1301170733886649828 userid | 10 dbid | 13340 query | SELECT * FROM foo WHERE id = $1 executor | adaptive partition_key | 42 calls | 1
We can see that Citus uses the adaptive executor most commonly to run queries. This executor fragments the query into constituent queries to run on relevant nodes, 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. 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.
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 scoped to a single tenant?”
SELECT sum(calls), partition_key IS NOT NULL AS single_tenant FROM citus_stat_statements GROUP BY 2;
. sum | single_tenant -----+--------------- 2 | f 1 | t
In a multi-tenant database, for instance, we would expect the vast majority of queries to be single tenant. Seeing too many multi-tenant queries may indicate that queries do not have the proper filters to match a tenant, and are using unnecessary resources.
To investigate which tenants in particular are most active, you can use the Tenant-level query statistics view.
The pg_stat_statements view limits the number of statements it tracks, and the duration of its records. Because citus_stat_statements tracks a strict subset of the queries in pg_stat_statements, a choice of equal limits for the two views would cause a mismatch in their data retention. Mismatched records can cause joins between the views to behave unpredictably.
There are three ways to help synchronize the views, and all three can be used together.
Have the maintenance daemon periodically sync the citus and pg stats. The GUC
citus.stat_statements_purge_intervalsets time in seconds for the sync. A value of 0 disables periodic syncs.
Adjust the number of entries in citus_stat_statements. The
citus.stat_statements_maxGUC removes old entries when new ones cross the threshold. The default value is 50K, and the highest allowable value is 10M. Note that each entry costs about 140 bytes in shared memory so set the value wisely.
pg_stat_statements.max. Its default value is 5000, and could be increased to 10K, 20K or even 50K without much overhead. This is most beneficial when there is more local (i.e. coordinator) query workload.
citus.stat_statements_max requires restarting the PostgreSQL service. Changing
citus.stat_statements_purge_interval, on the other hand, will come into effect with a call to pg_reload_conf().
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 ALTER DATABASE citus SET statement_timeout TO 300000; SELECT run_command_on_workers($cmd$ ALTER DATABASE citus SET statement_timeout TO 300000; $cmd$);
The timeout is specified in milliseconds.
To customize the timeout per query, use
SET LOCAL in a transaction:
BEGIN; -- this limit applies to just the current transaction SET LOCAL statement_timeout TO 300000; -- ... COMMIT;
Since Citus version 8.1.0 (released 2018-12-17) the traffic between the different nodes in the cluster is encrypted for NEW installations. This is done by using TLS with self-signed certificates. This means that this does not protect against Man-In-The-Middle attacks. This only protects against passive eavesdropping on the network.
Clusters originally created with a Citus version before 8.1.0 do not have any network encryption enabled between nodes (even if upgraded later). To set up self-signed TLS on on this type of installation follow the steps in official postgres documentation together with the citus specific settings described here, i.e. changing
sslmode=require. This setup should be done on coordinator and workers.
When Citus nodes communicate with one another they consult 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
-- key=value pairs separated by spaces. -- For example, ssl options: ALTER SYSTEM SET citus.node_conninfo = 'sslrootcert=/path/to/citus-ca.crt sslcrl=/path/to/citus-ca.crl sslmode=verify-full';
There is a whitelist of parameters that the GUC accepts, see the node_conninfo reference for details. As of Citus 8.1, the default value for node_conninfo is
sslmode=require, which prevents unencrypted communication between nodes. If your cluster was originally created before Citus 8.1 the value will be
sslmode=prefer. After setting up self-signed certificates on all nodes it’s recommended to change this setting to
After changing this setting it is important to reload the postgres configuration. Even though the changed setting might be visible in all sessions, the setting is only consulted by Citus when new connections are established. When a reload signal is received, Citus marks all existing connections to be closed which causes a reconnect after running transactions have been completed.
Citus versions before 9.2.4 require a restart for existing connections to be closed.
For these versions a reload of the configuration does not trigger connection ending and subsequent reconnecting. Instead the server should be restarted to enforce all connections to use the new settings.
-- only superusers can access this table -- add a password for user jdoe INSERT INTO pg_dist_authinfo (nodeid, rolename, authinfo) VALUES (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.
-- update user jdoe to use certificate authentication UPDATE pg_dist_authinfo SET authinfo = 'sslcert=/path/to/user.crt sslkey=/path/to/user.key' WHERE nodeid = 123 AND rolename = 'jdoe';
This changes the user from using a password to use a certificate and keyfile while connecting to node 123 instead. Make sure the user certificate is signed by a certificate that is trusted by the worker you are connecting to and authentication settings on the worker allow for certificate based authentication. Full documentation on how to use client certificates can be found in the postgres libpq documentation.
pg_dist_authinfo does not force any existing connection to reconnect.
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 and a ssl/tls connection to nodes in the local # network. The following ranges correspond to 24, 20, and 16-bit blocks # in Private IPv4 address spaces. hostssl all all 10.0.0.0/8 md5 # Require passwords and ssl/tls connections when the host connects to # itself as well. hostssl all all 127.0.0.1/32 md5 hostssl all all ::1/128 md5
The coordinator node needs to know roles’ passwords in order to communicate with the workers. Our Managed Service keeps track of that kind of information for you. 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.
Starting in version 11.0, Citus Community edition now supports row-level security for distributed tables.
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_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_2. Then run the following as an administrator:
CREATE TABLE events( 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 ALTER TABLE events ENABLE ROW LEVEL SECURITY;
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: new row violates row-level security policy for table "events_102055" */
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 is one thing to keep in mind. While including other extensions in shared_preload_libraries, you should make sure that Citus is the first extension.
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.
Connect to each of the worker nodes and run:
-- on every worker node CREATE DATABASE newbie; \c newbie CREATE EXTENSION citus;
Then, on the coordinator:
CREATE DATABASE newbie; \c newbie CREATE EXTENSION citus; SELECT * from citus_add_node('node-name', 5432); SELECT * from citus_add_node('node-name2', 5432); -- ... for all of them
Now the new database will be operating as another Citus cluster.