This section shows how to create a database for an ad analytics app, which can be used by companies to view, change, analyze, and manage their ads and campaigns (see an example app). Such an application has good characteristics of a typical multi-tenant system. Data from different tenants is stored in a central database, and each tenant has an isolated view of their own data.

Three Postgres Pro tables to represent this data will be used. To get started, download sample data for these tables:

curl https://examples.citusdata.com/tutorial/companies.csv > companies.csv
curl https://examples.citusdata.com/tutorial/campaigns.csv > campaigns.csv
curl https://examples.citusdata.com/tutorial/ads.csv > ads.csv

Now you can instruct citus to distribute tables created above across the different nodes in the cluster. To do so, run the create_distributed_table function and specify the table you want to shard and the column you want to shard on. In the example below, all the tables are sharded on the company_id column.

SELECT create_distributed_table('companies', 'id');
SELECT create_distributed_table('campaigns', 'company_id');
SELECT create_distributed_table('ads', 'company_id');

Sharding all tables on the company_id column allows citus to co-locate the tables together and allows for features like primary keys, foreign keys, and complex joins across your cluster.

Then you can go ahead and load the downloaded data into the tables using the standard psql \copy command. Make sure that you specify the correct file path if you downloaded the file to a different location.

\copy companies from 'companies.csv' with csv
\copy campaigns from 'campaigns.csv' with csv
\copy ads from 'ads.csv' with csv

After the data is loaded into the tables, you can run some queries. citus supports standard INSERT, UPDATE, and DELETE commands for inserting and modifying rows in a distributed table, which is the typical way of interaction for a user-facing application.

For example, you can insert a new company by running:

INSERT INTO companies VALUES (5000, 'New Company', 'https://randomurl/image.png', now(), now());

If you want to double the budget for all campaigns of the company, run the UPDATE command:

UPDATE campaigns
SET monthly_budget = monthly_budget*2
WHERE company_id = 5;

Another example of such an operation is to run transactions, which span multiple tables. For example, you can delete a campaign and all its associated ads atomically by running:

BEGIN;
DELETE FROM campaigns WHERE id = 46 AND company_id = 5;
DELETE FROM ads WHERE campaign_id = 46 AND company_id = 5;
COMMIT;

Each statement in a transaction causes round-trips between the coordinator and workers in the multi-node citus. For multi-tenant workloads, it is more efficient to run transactions in distributed functions. The efficiency gains become more apparent for larger transactions, but you can use the small transaction above as an example.

The tutorial above shows how to use citus to power a simple multi-tenant application. As a next step, you can look at the Multi-Tenant Apps section to see how you can model your own data for multi-tenancy.

This section shows how to create a database for a real-time analytics application. This application will insert large volumes of events data and enable analytical queries on that data with sub-second latencies. In this example, the GitHub events dataset is used. This dataset includes all public events on GitHub, such as commits, forks, new issues, and comments on these issues.

Two Postgres Pro tables are used to represent this data. To get started, download sample data for these tables:

curl https://examples.citusdata.com/tutorial/users.csv > users.csv
curl https://examples.citusdata.com/tutorial/events.csv > events.csv

To start first connect to the citus coordinator using psql.

If you are using citus installed as described in the Installing citus on a Single Node section, the coordinator node will be running on port 9700.

psql -p 9700

Then you can create the tables by using the standard Postgres Pro CREATE TABLE command:

CREATE TABLE github_events
(
    event_id bigint,
    event_type text,
    event_public boolean,
    repo_id bigint,
    payload jsonb,
    repo jsonb,
    user_id bigint,
    org jsonb,
    created_at timestamp
);

CREATE TABLE github_users
(
    user_id bigint,
    url text,
    login text,
    avatar_url text,
    gravatar_id text,
    display_login text
);

Next you can create indexes on events data just like you do in Postgres Pro. This example also shows how to create a GIN index to make querying on JSONB fields faster.

CREATE INDEX event_type_index ON github_events (event_type);
CREATE INDEX payload_index ON github_events USING GIN (payload jsonb_path_ops);

Now you can instruct citus to distribute the tables created above across the nodes in the cluster. To do so, you can call the create_distributed_table function and specify the table you want to shard and the column you want to shard on. In the example below, all the tables are sharded on the user_id column.

SELECT create_distributed_table('github_users', 'user_id');
SELECT create_distributed_table('github_events', 'user_id');

Sharding all tables on the user_id column allows citus to co-locate the tables together and allows for efficient joins and distributed roll-ups.

Then you can go ahead and load the downloaded data into the tables using the standard psql \copy command. Make sure that you specify the correct file path if you downloaded the file to a different location.

\copy github_users from 'users.csv' with csv
\copy github_events from 'events.csv' with csv

After the data is loaded into the tables, you can run some queries. First check how many users are contained in the distributed database.

SELECT count(*) FROM github_users;

Now analyze GitHub push events in the data. First compute the number of commits per minute by using the number of distinct commits in each push event.

SELECT date_trunc('minute', created_at) AS minute,
       sum((payload->>'distinct_size')::int) AS num_commits
FROM github_events
WHERE event_type = 'PushEvent'
GROUP BY minute
ORDER BY minute;

Also, there is a users table. You can also join the users with events and find the top ten users who created the most repositories.

SELECT login, count(*)
FROM github_events ge
JOIN github_users gu
ON ge.user_id = gu.user_id
WHERE event_type = 'CreateEvent' AND payload @> '{"ref_type": "repository"}'
GROUP BY login
ORDER BY count(*) DESC LIMIT 10;

citus also supports standard INSERT, UPDATE, and DELETE commands for inserting and modifying data. For example, you can update the user display login by running the following command:

UPDATE github_users SET display_login = 'no1youknow' WHERE user_id = 24305673;

As a next step, you can look at the Real-Time Apps section to see how you can model your own data and power real-time analytical applications.

Distributed schemas are relocatable within a citus cluster. The system can rebalance them as a whole unit across the available nodes, which allows for efficient sharing of resources without manual allocation.

By design, microservices own their storage layer, we do not make any assumptions on the type of tables and data that they will create and store. We, however, provide a schema for every service and assume that they use a distinct role to connect to the database. When a user connects, their role name is put at the beginning of the search_path, so if the role matches the schema name, you do not need any application changes to set the correct search_path.

Three services are used in the example:

To start first connect to the citus coordinator using psql.

If you are using citus installed as described in the Installing citus on a Single Node section, the coordinator node will be running on port 9700.

psql -p 9700

You can now create the database roles for every service:

CREATE USER user_service;
CREATE USER time_service;
CREATE USER ping_service;

There are two ways to distribute a schema in citus:

You can list the currently distributed schemas:

SELECT * FROM citus_schemas;

schema_name  | colocation_id | schema_size | schema_owner
-------------+---------------+-------------+--------------
user_service |             5 | 0 bytes     | user_service
time_service |             6 | 0 bytes     | time_service
ping_service |             7 | 0 bytes     | ping_service
(3 rows)

You now need to connect to the citus coordinator for every microservice. You can use the \c command to swap the user within an existing psql instance.

\c citus user_service

CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    email VARCHAR(255) NOT NULL
);

\c citus time_service

CREATE TABLE query_details (
    id SERIAL PRIMARY KEY,
    ip_address INET NOT NULL,
    query_time TIMESTAMP NOT NULL
);

\c citus ping_service

CREATE TABLE ping_results (
    id SERIAL PRIMARY KEY,
    host VARCHAR(255) NOT NULL,
    result TEXT NOT NULL
);

For the purpose of this tutorial a very simple set of services is used. You can obtain them by cloning this public repository:

git clone https://github.com/citusdata/citus-example-microservices.git

The repository contains the ping, time, and user services. All of them have the app.py file, which we run.

$ tree
.
├── LICENSE
├── README.md
├── ping
│   ├── app.py
│   ├── ping.sql
│   └── requirements.txt
├── time
│   ├── app.py
│   ├── requirements.txt
│   └── time.sql
└── user
    ├── app.py
    ├── requirements.txt
    └── user.sql

Before you run the services, however, edit the user/app.py, ping/app.py, and time/app.py files providing the connection configuration for your citus cluster:

# Database configuration
db_config = {
    'host': 'localhost',
    'database': 'citus',
    'user': 'ping_service',
    'port': 9700
}

After making the changes save all modified files and move on to the next step of running the services.

Now that we called some API functions, data has been stored and we can check if the citus_schemas view reflects what we expect:

SELECT * FROM citus_schemas;

schema_name  | colocation_id | schema_size | schema_owner
--------------+---------------+-------------+--------------
user_service |             1 | 112 kB      | user_service
time_service |             2 | 32 kB       | time_service
ping_service |             3 | 32 kB       | ping_service
(3 rows)

At the time of schemas creation you do not instruct citus on which computer to create them. It is done automatically. Execute the following query to see where each schema resides:

SELECT nodename,nodeport, table_name, pg_size_pretty(sum(shard_size))
  FROM citus_shards
GROUP BY nodename,nodeport, table_name;

nodename  | nodeport |         table_name         | pg_size_pretty
-----------+----------+----------------------------+----------------
localhost |     9701 | time_service.query_details | 32 kB
localhost |     9702 | user_service.users         | 112 kB
localhost |     9702 | ping_service.ping_results  | 32 kB

We can see that the time service landed on node localhost:9701, while the user and ping services share space on the second worker localhost:9702. This is only an example, and the data sizes here can be ignored, but let us assume that we are annoyed by the uneven storage space utilization between the nodes. It makes more sense to have the two smaller time and ping services reside on one computer, while the large user service resides alone.

We can do this by instructing citus to rebalance the cluster by disk size:

SELECT citus_rebalance_start();

NOTICE:  Scheduled 1 moves as job 1
DETAIL:  Rebalance scheduled as background job
HINT:  To monitor progress, run: SELECT * FROM citus_rebalance_status();
 citus_rebalance_start
-----------------------
                     1
(1 row)

When done, check how the new layout looks:

SELECT nodename,nodeport, table_name, pg_size_pretty(sum(shard_size))
  FROM citus_shards
GROUP BY nodename,nodeport, table_name;

nodename  | nodeport |         table_name         | pg_size_pretty
-----------+----------+----------------------------+----------------
localhost |     9701 | time_service.query_details | 32 kB
localhost |     9701 | ping_service.ping_results  | 32 kB
localhost |     9702 | user_service.users         | 112 kB
(3 rows)

We expect that the schemas have been moved and the cluster has become more balanced. This operation is transparent for the applications. Therefore, there is no need for a restart, and they will continue serving queries.

We will build the back-end for an application that tracks online advertising performance and provides an analytics dashboard on top. It is a natural fit for a multi-tenant application because user requests for data concern one company (their own) at a time. Code for the full example application is available on GitHub.

Let's start by considering a simplified schema for this application. The application must keep track of multiple companies, each of which runs advertising campaigns. Campaigns have many ads, and each ad has associated records of its clicks and impressions.

Here is the example schema. We will make some minor changes later, which allow us to effectively distribute and isolate the data in a distributed environment.

CREATE TABLE companies (
  id bigserial PRIMARY KEY,
  name text NOT NULL,
  image_url text,
  created_at timestamp without time zone NOT NULL,
  updated_at timestamp without time zone NOT NULL
);

CREATE TABLE campaigns (
  id bigserial PRIMARY KEY,
  company_id bigint REFERENCES companies (id),
  name text NOT NULL,
  cost_model text NOT NULL,
  state text NOT NULL,
  monthly_budget bigint,
  blacklisted_site_urls text[],
  created_at timestamp without time zone NOT NULL,
  updated_at timestamp without time zone NOT NULL
);

CREATE TABLE ads (
  id bigserial PRIMARY KEY,
  campaign_id bigint REFERENCES campaigns (id),
  name text NOT NULL,
  image_url text,
  target_url text,
  impressions_count bigint DEFAULT 0,
  clicks_count bigint DEFAULT 0,
  created_at timestamp without time zone NOT NULL,
  updated_at timestamp without time zone NOT NULL
);

CREATE TABLE clicks (
  id bigserial PRIMARY KEY,
  ad_id bigint REFERENCES ads (id),
  clicked_at timestamp without time zone NOT NULL,
  site_url text NOT NULL,
  cost_per_click_usd numeric(20,10),
  user_ip inet NOT NULL,
  user_data jsonb NOT NULL
);

CREATE TABLE impressions (
  id bigserial PRIMARY KEY,
  ad_id bigint REFERENCES ads (id),
  seen_at timestamp without time zone NOT NULL,
  site_url text NOT NULL,
  cost_per_impression_usd numeric(20,10),
  user_ip inet NOT NULL,
  user_data jsonb NOT NULL
);

There are modifications we can make to the schema, which will give it a performance boost in a distributed environment like citus. To see how, we must become familiar with how the extension distributes data and executes queries.

The relational data model is great for applications. It protects data integrity, allows flexible queries, and accommodates changing data. Traditionally the only problem was that relational databases were not considered capable of scaling to the workloads needed for big SaaS applications. Developers had to put up with NoSQL databases, or a collection of backend services, to reach that size.

With citus you can keep your data model and make it scale. The extension appears to applications as a single Postgres Pro database, but it internally routes queries to an adjustable number of physical servers (nodes), which can process requests in parallel.

Multi-tenant applications have a nice property that we can take advantage of: queries usually always request information for one tenant at a time, not a mix of tenants. For instance, when a salesperson is searching prospect information in a CRM, the search results are specific to his employer; other businesses' leads and notes are not included.

Because application queries are restricted to a single tenant, such as a store or company, one approach for making multi-tenant application queries fast is to store all data for a given tenant on the same node. This minimizes network overhead between the nodes and allows citus to support all your application's joins, key constraints and transactions efficiently. With this, you can scale across multiple nodes without having to totally re-write or re-architect your application. See the figure below to learn more.

This can be done in citus by making sure every table in our schema has a column to clearly mark which tenant owns which rows. In the ad analytics application the tenants are companies, so we must ensure all tables have a company_id column.

We can tell citus to use this column to read and write rows to the same node when the rows are marked for the same company. In citus terminology company_id is the distribution column, which you can learn more about in the Choosing Distribution Column section.

In the previous section we identified the correct distribution column for our multi-tenant application: company_id. Even in a single-computer database it can be useful to denormalize tables with the addition of company_id, whether it be for row-level security or for additional indexing. The extra benefit, as we saw, is that including the extra column helps for multi-machine scaling as well.

The schema we have created so far uses a separate id column as primary key for each table. citus requires that primary and foreign key constraints include the distribution column. This requirement makes enforcing these constraints much more efficient in a distributed environment as only a single node has to be checked to guarantee them.

In SQL, this requirement translates to making primary and foreign keys composite by including company_id. This is compatible with the multi-tenant case because what we really need there is to ensure uniqueness on a per-tenant basis.

Putting it all together, here are the changes that prepare the tables for distribution by company_id.

CREATE TABLE companies (
  id bigserial PRIMARY KEY,
  name text NOT NULL,
  image_url text,
  created_at timestamp without time zone NOT NULL,
  updated_at timestamp without time zone NOT NULL
);

CREATE TABLE campaigns (
  id bigserial,       -- was: PRIMARY KEY
  company_id bigint REFERENCES companies (id),
  name text NOT NULL,
  cost_model text NOT NULL,
  state text NOT NULL,
  monthly_budget bigint,
  blacklisted_site_urls text[],
  created_at timestamp without time zone NOT NULL,
  updated_at timestamp without time zone NOT NULL,
  PRIMARY KEY (company_id, id) -- added
);

CREATE TABLE ads (
  id bigserial,       -- was: PRIMARY KEY
  company_id bigint,  -- added
  campaign_id bigint, -- was: REFERENCES campaigns (id)
  name text NOT NULL,
  image_url text,
  target_url text,
  impressions_count bigint DEFAULT 0,
  clicks_count bigint DEFAULT 0,
  created_at timestamp without time zone NOT NULL,
  updated_at timestamp without time zone NOT NULL,
  PRIMARY KEY (company_id, id),         -- added
  FOREIGN KEY (company_id, campaign_id) -- added
    REFERENCES campaigns (company_id, id)
);

CREATE TABLE clicks (
  id bigserial,        -- was: PRIMARY KEY
  company_id bigint,   -- added
  ad_id bigint,        -- was: REFERENCES ads (id),
  clicked_at timestamp without time zone NOT NULL,
  site_url text NOT NULL,
  cost_per_click_usd numeric(20,10),
  user_ip inet NOT NULL,
  user_data jsonb NOT NULL,
  PRIMARY KEY (company_id, id),      -- added
  FOREIGN KEY (company_id, ad_id)    -- added
    REFERENCES ads (company_id, id)
);

CREATE TABLE impressions (
  id bigserial,         -- was: PRIMARY KEY
  company_id bigint,    -- added
  ad_id bigint,         -- was: REFERENCES ads (id),
  seen_at timestamp without time zone NOT NULL,
  site_url text NOT NULL,
  cost_per_impression_usd numeric(20,10),
  user_ip inet NOT NULL,
  user_data jsonb NOT NULL,
  PRIMARY KEY (company_id, id),       -- added
  FOREIGN KEY (company_id, ad_id)     -- added
    REFERENCES ads (company_id, id)
);

You can learn more about migrating your own data model in the Identify Distribution Strategy section.

Once you have made the slight schema modification outlined earlier, your application can scale with very little work. You will just connect the app to citus and let the database take care of keeping the queries fast and the data safe.

Any application queries or update statements, which include a filter on company_id, will continue to work exactly as they are. As mentioned earlier, this kind of filter is common in multi-tenant apps. When using an Object-Relational Mapper (ORM) you can recognize these queries by methods such as where or filter.

ActiveRecord:

Impression.where(company_id: 5).count

Django:

Impression.objects.filter(company_id=5).count()

Basically when the resulting SQL executed in the database contains a WHERE company_id = :value clause on every table (including tables in JOIN queries), then citus will recognize that the query should be routed to a single node and execute it there as it is. This makes sure that all SQL functionality is available. The node is an ordinary Postgres Pro server after all.

Also, to make it even simpler, you can use our activerecord-multi-tenant library for Ruby on Rails, or django-multitenant for Django, which will automatically add these filters to all your queries, even the complicated ones. Check out our migration guides for Ruby on Rails and Django.

This guide is framework-agnostic, so we will point out some citus features using SQL. Use your imagination for how these statements would be expressed in your language of choice.

Here is a simple query and update operating on a single tenant.

-- Campaigns with highest budget

SELECT name, cost_model, state, monthly_budget
  FROM campaigns
 WHERE company_id = 5
 ORDER BY monthly_budget DESC
 LIMIT 10;

-- Double the budgets!

UPDATE campaigns
   SET monthly_budget = monthly_budget*2
 WHERE company_id = 5;

A common pain point for users scaling applications with NoSQL databases is the lack of transactions and joins. However, transactions work as you would expect them to in citus:

-- Transactionally reallocate campaign budget money

BEGIN;

UPDATE campaigns
   SET monthly_budget = monthly_budget + 1000
 WHERE company_id = 5
   AND id = 40;

UPDATE campaigns
   SET monthly_budget = monthly_budget - 1000
 WHERE company_id = 5
   AND id = 41;

COMMIT;

As a final demo of SQL support, we have a query that includes aggregates and window functions and it works the same in citus as it does in Postgres Pro. The query ranks the ads in each campaign by the count of their impressions.

SELECT a.campaign_id,
       RANK() OVER (
         PARTITION BY a.campaign_id
         ORDER BY a.campaign_id, count(*) desc
       ), count(*) as n_impressions, a.id
  FROM ads as a
  JOIN impressions as i
    ON i.company_id = a.company_id
   AND i.ad_id      = a.id
 WHERE a.company_id = 5
GROUP BY a.campaign_id, a.id
ORDER BY a.campaign_id, n_impressions desc;

In short, when queries are scoped to a tenant then the INSERT, UPDATE, DELETE, complex SQL commands, and transactions all work as expected.

Up until now all tables have been distributed by company_id, but sometimes there is data that can be shared by all tenants and does not “belong” to any tenant in particular. For instance, all companies using this example ad platform might want to get geographical information for their audience based on IP addresses. In a single computer database this could be accomplished by a lookup table for geo-ip, like the following. (A real table would probably use PostGIS, but bear with the simplified example.)

CREATE TABLE geo_ips (
  addrs cidr NOT NULL PRIMARY KEY,
  latlon point NOT NULL
    CHECK (-90  <= latlon[0] AND latlon[0] <= 90 AND
           -180 <= latlon[1] AND latlon[1] <= 180)
);
CREATE INDEX ON geo_ips USING gist (addrs inet_ops);

To use this table efficiently in a distributed setup, we need to find a way to co-locate the geo_ips table with clicks for not just one but every company. That way, no network traffic need be incurred at query time. This can be done in citus by designating geo_ips as a reference table.

-- Make synchronized copies of geo_ips on all workers

SELECT create_reference_table('geo_ips');

Reference tables are replicated across all worker nodes, and citus automatically keeps them in sync during modifications. Notice that we call the create_reference_table function rather than the create_distributed_table function.

Now that geo_ips is established as a reference table, load it with example data:

\copy geo_ips from 'geo_ips.csv' with csv

Now joining clicks with this table can execute efficiently. We can ask, for example, the locations of everyone who clicked on ad 290.

SELECT c.id, clicked_at, latlon
  FROM geo_ips, clicks c
 WHERE addrs >> c.user_ip
   AND c.company_id = 5
   AND c.ad_id = 290;

Another challenge with multi-tenant systems is keeping the schemas for all the tenants in sync. Any schema change needs to be consistently reflected across all the tenants. In citus, you can simply use standard Postgres Pro DDL commands to change the schema of your tables, and the extension will propagate them from the coordinator node to the workers using a two-phase commit protocol.

For example, the advertisements in this application could use a text caption. We can add a column to the table by issuing the standard SQL on the coordinator:

ALTER TABLE ads
  ADD COLUMN caption text;

This updates all the workers as well. Once this command finishes, the citus cluster will accept queries that read or write data in the new caption column.

For a fuller explanation of how DDL commands propagate through the cluster, see the Modifying Tables section.

Given that all tenants share a common schema and hardware infrastructure, how can we accommodate tenants, which want to store information not needed by others? For example, one of the tenant applications using our advertising database may want to store tracking cookie information with clicks, whereas another tenant may care about browser agents. Traditionally databases using a shared schema approach for multi-tenancy have resorted to creating a fixed number of pre-allocated “custom” columns, or having external “extension tables”. However, Postgres Pro provides a much easier way with its unstructured column types, notably JSONB.

Notice that our schema already has a JSONB field in clicks called user_data. Each tenant can use it for flexible storage.

Suppose company five includes information in the field to track whether the user is on a mobile device. The company can query to find who clicks more, mobile or traditional visitors:

SELECT
  user_data->>'is_mobile' AS is_mobile,
  count(*) AS count
FROM clicks
WHERE company_id = 5
GROUP BY user_data->>'is_mobile'
ORDER BY count DESC;

The database administrator can even create a partial index to improve speed for an individual tenant's query patterns. Here is one to improve filters for clicks of the company with company_id = 5 from users on mobile devices:

CREATE INDEX click_user_data_is_mobile
ON clicks ((user_data->>'is_mobile'))
WHERE company_id = 5;

Additionally, Postgres Pro supports GIN indices on JSONB. Creating a GIN index on a JSONB column will create an index on every key and value within that JSON document. This speeds up a number of JSONB operators such as ?, ?|, and ?&.

CREATE INDEX click_user_data
ON clicks USING gin (user_data);

-- this speeds up queries like, "which clicks have
-- the is_mobile key present in user_data?"

SELECT id
  FROM clicks
 WHERE user_data ? 'is_mobile'
   AND company_id = 5;

Multi-tenant databases should be designed for future scale as business grows or tenants want to store more data. citus can scale out easily by adding new computers without having to make any changes or take application downtime.

Being able to rebalance data in the citus cluster allows you to grow your data size or number of customers and improve performance on demand. Adding new computers allows you to keep data in memory even when it is much larger than what a single computer can store.

Also, if data increases for only a few large tenants, then you can isolate those particular tenants to separate nodes for better performance.

To scale out your citus cluster, first add a new worker node to it with the citus_add_node function.

Once you add the node it is available in the system. However, at this point no tenants are stored on it and citus will not yet run any queries there. To move your existing data, you can ask citus to rebalance the data. This operation moves bundles of rows called shards between the currently active nodes to attempt to equalize the amount of data on each node.

SELECT citus_rebalance_start();

Applications do not need to undergo downtime during shard rebalancing. Read requests continue seamlessly, and writes are locked only when they affect shards, which are currently in flight. In citus writes to shards are blocked during rebalancing but reads are unaffected.

The previous section describes a general-purpose way to scale a cluster as the number of tenants increases. However, users often have two questions. The first is what will happen to their largest tenant if it grows too big. The second is what are the performance implications of hosting a large tenant together with small ones on a single worker node.

Regarding the first question, investigating data from large SaaS sites reveals that as the number of tenants increases, the size of tenant data typically tends to follow a Zipfian distribution. See the figure below to learn more.

For instance, in a database of 100 tenants, the largest is predicted to account for about 20% of the data. In a more realistic example for a large SaaS company, if there are 10,000 tenants, the largest will account for around 2% of the data. Even at 10TB of data, the largest tenant will require 200GB, which can pretty easily fit on a single node.

Another question is regarding performance when large and small tenants are on the same node. Standard shard rebalancing will improve overall performance but it may or may not improve the mixing of large and small tenants. The rebalancer simply distributes shards to equalize storage usage on nodes, without examining which tenants are allocated on each shard.

To improve resource allocation and make guarantees of tenant QoS it is worthwhile to move large tenants to dedicated nodes. The citus extension provides the tools to do this.

In our case, let's imagine that the company with company_id=5 is very large. We can isolate the data for this tenant in two steps. We will present the commands here, and you can consult the Tenant Isolation section to learn more about them.

First isolate the tenant's data to a dedicated shard suitable to move. The CASCADE option also applies this change to the rest of our tables distributed by company_id.

SELECT isolate_tenant_to_new_shard(
  'companies', 5, 'CASCADE'
);

The output is the shard ID dedicated to hold company_id=5:

┌─────────────────────────────┐
│ isolate_tenant_to_new_shard │
├─────────────────────────────┤
│                      102240 │
└─────────────────────────────┘

Next we move the data across the network to a new dedicated node. Create a new node as described in the previous section. Take note of its hostname.

-- 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;

-- Move the shard to your choice of worker (it will also move the
-- other shards created with the CASCADE option)

-- Note that you should set wal_level for all nodes to be >= logical
-- to use citus_move_shard_placement
-- You also need to restart your cluster after setting wal_level in
-- postgresql.conf files

SELECT citus_move_shard_placement(
  102240,
  'source_host', source_port,
  'dest_host', dest_port);

You can confirm the shard movement by querying the pg_dist_placement table again.

With this, you now know how to use citus to power your multi-tenant application for scalability. If you have an existing schema and want to migrate it for citus, see the Migrating an Existing App section.

To adjust a front-end application, specifically Ruby on Rails or Django, read Ruby on Rails or Django migration guides.

The data we are dealing with is an immutable stream of log data. We will insert directly into citus but it is also common for this data to first be routed through something like Kafka. Doing so has the usual advantages, and makes it easier to pre-aggregate the data once data volumes become unmanageably high.

We will use a simple schema for ingesting HTTP event data. This schema serves as an example to demonstrate the overall architecture; a real system might use additional columns.

-- This is run on the coordinator

CREATE TABLE http_request (
  site_id INT,
  ingest_time TIMESTAMPTZ DEFAULT now(),

  url TEXT,
  request_country TEXT,
  ip_address TEXT,

  status_code INT,
  response_time_msec INT
);

SELECT create_distributed_table('http_request', 'site_id');

When we call the create_distributed_table function we ask citus to hash-distribute http_request using the site_id column. That means all the data for a particular site will live in the same shard.

The user defined functions use the default configuration values for shard count. We recommend using 2-4x as many shards as CPU cores in your cluster. Using this many shards lets you rebalance data across your cluster after adding new worker nodes.

With this, the system is ready to accept data and serve queries. Keep the following loop running in a psql console in the background while you continue with the other commands in this article. It generates fake data every second or two.

DO $$
  BEGIN LOOP
    INSERT INTO http_request (
      site_id, ingest_time, url, request_country,
      ip_address, status_code, response_time_msec
    ) VALUES (
      trunc(random()*32), clock_timestamp(),
      concat('http://example.com/', md5(random()::text)),
      ('{China,India,USA,Indonesia}'::text[])[ceil(random()*4)],
      concat(
        trunc(random()*250 + 2), '.',
        trunc(random()*250 + 2), '.',
        trunc(random()*250 + 2), '.',
        trunc(random()*250 + 2)
      )::inet,
      ('{200,404}'::int[])[ceil(random()*2)],
      5+trunc(random()*150)
    );
    COMMIT;
    PERFORM pg_sleep(random() * 0.25);
  END LOOP;
END $$;

Once you are ingesting data, you can run dashboard queries such as:

SELECT
  site_id,
  date_trunc('minute', ingest_time) as minute,
  COUNT(1) AS request_count,
  SUM(CASE WHEN (status_code between 200 and 299) THEN 1 ELSE 0 END) as success_count,
  SUM(CASE WHEN (status_code between 200 and 299) THEN 0 ELSE 1 END) as error_count,
  SUM(response_time_msec) / COUNT(1) AS average_response_time_msec
FROM http_request
WHERE date_trunc('minute', ingest_time) > now() - '5 minutes'::interval
GROUP BY site_id, minute
ORDER BY minute ASC;

The setup described above works but has two drawbacks:

You can overcome both drawbacks by rolling up the raw data into a pre-aggregated form. Here, we will aggregate the raw data into a table, which stores summaries of 1-minute intervals. In a production system, you would probably also want something like 1-hour and 1-day intervals, these each correspond to zoom-levels in the dashboard. When the user wants request times for the last month the dashboard can simply read and chart the values for each of the last 30 days.

CREATE TABLE http_request_1min (
  site_id INT,
  ingest_time TIMESTAMPTZ, -- which minute this row represents

  error_count INT,
  success_count INT,
  request_count INT,
  average_response_time_msec INT,
  CHECK (request_count = error_count + success_count),
  CHECK (ingest_time = date_trunc('minute', ingest_time))
);

SELECT create_distributed_table('http_request_1min', 'site_id');

CREATE INDEX http_request_1min_idx ON http_request_1min (site_id, ingest_time);

This looks a lot like the previous code block. Most importantly: It also shards on site_id and uses the same default configuration for shard count. Because all three of those match, there is a 1-to-1 correspondence between http_request shards and http_request_1min shards, and citus will place matching shards on the same worker. This is called co-location; it makes queries such as joins faster and our rollups possible. See the figure below to learn more.

In order to populate http_request_1min we are going to periodically run INSERT INTO SELECT. This is possible because the tables are co-located. The following function wraps the rollup query up for convenience.

-- Single-row table to store when we rolled up last
CREATE TABLE latest_rollup (
  minute timestamptz PRIMARY KEY,

  -- "minute" should be no more precise than a minute
  CHECK (minute = date_trunc('minute', minute))
);

-- Initialize to a time long ago
INSERT INTO latest_rollup VALUES ('10-10-1901');

-- Function to do the rollup
CREATE OR REPLACE FUNCTION rollup_http_request() RETURNS void AS $$
DECLARE
  curr_rollup_time timestamptz := date_trunc('minute', now() - interval '1 minute');
  last_rollup_time timestamptz := minute from latest_rollup;
BEGIN
  INSERT INTO http_request_1min (
    site_id, ingest_time, request_count,
    success_count, error_count, average_response_time_msec
  ) SELECT
    site_id,
    date_trunc('minute', ingest_time),
    COUNT(1) as request_count,
    SUM(CASE WHEN (status_code between 200 and 299) THEN 1 ELSE 0 END) as success_count,
    SUM(CASE WHEN (status_code between 200 and 299) THEN 0 ELSE 1 END) as error_count,
    SUM(response_time_msec) / COUNT(1) AS average_response_time_msec
  FROM http_request
  -- Roll up only data new since last_rollup_time
  WHERE ingest_time <@ tstzrange(last_rollup_time, curr_rollup_time, '(]')
  GROUP BY 1, 2;

  -- Update the value in latest_rollup so that next time we run the
  -- rollup it will operate on data newer than curr_rollup_time
  UPDATE latest_rollup SET minute = curr_rollup_time;
END;
$$ LANGUAGE plpgsql;

* * * * * psql -c 'SELECT rollup_http_request();'

Alternatively, an extension such as pg_cron allows you to schedule recurring queries directly from the database.

The dashboard query from earlier is now a lot nicer:

SELECT site_id, ingest_time as minute, request_count,
       success_count, error_count, average_response_time_msec
  FROM http_request_1min
 WHERE ingest_time > date_trunc('minute', now()) - '5 minutes'::interval;

The rollups make queries faster, but we still need to expire old data to avoid unbounded storage costs. Simply decide how long you would like to keep data for each granularity and use standard queries to delete expired data. In the following example, we decided to keep raw data for one day, and per-minute aggregations for one month:

DELETE FROM http_request WHERE ingest_time < now() - interval '1 day';
DELETE FROM http_request_1min WHERE ingest_time < now() - interval '1 month';

In production you could wrap these queries in a function and call it every minute in a cron job.

Data expiration can go even faster by using table range partitioning on top of citus hash distribution. See the Timeseries Data section for a detailed example.

Those are the basics. We provided an architecture that ingests HTTP events and then rolls up these events into their pre-aggregated form. This way you can both store raw events and also power your analytical dashboards with subsecond queries.

The next sections extend upon the basic architecture and show you how to resolve questions, which often appear.

A common question in HTTP analytics deals with approximate distinct counts: How many unique visitors visited your site over the last month? Answering this question exactly requires storing the list of all previously seen visitors in the rollup tables, a prohibitively large amount of data. However, an approximate answer is much more manageable.

A datatype called HyperLogLog, or hll, can answer the query approximately; it takes a surprisingly small amount of space to tell you approximately how many unique elements are in a set. Its accuracy can be adjusted. We will use ones which, using only 1,280 bytes, will be able to count up to tens of billions of unique visitors with at most 2.2% error.

An equivalent problem appears if you want to run a global query, such as the number of unique IP addresses, which visited any of your client's sites over the last month. Without hll this query involves shipping lists of IP addresses from the workers to the coordinator for it to deduplicate. That is both a lot of network traffic and a lot of computation. By using hll you can greatly improve query speed.

You can install the hll extension, whose instructions are available in the GitHub repository, and enable it as follows:

CREATE EXTENSION hll;

Now we are ready to track IP addresses in our rollup with hll. First add a column to the rollup table.

ALTER TABLE http_request_1min ADD COLUMN distinct_ip_addresses hll;

Next use our custom aggregation to populate the column. Just add it to the query in our rollup function:

@@ -1,10 +1,12 @@
  INSERT INTO http_request_1min (
    site_id, ingest_time, request_count,
    success_count, error_count, average_response_time_msec
+   , distinct_ip_addresses
  ) SELECT
    site_id,
    date_trunc('minute', ingest_time),
    COUNT(1) as request_count,
    SUM(CASE WHEN (status_code between 200 and 299) THEN 1 ELSE 0 END) as success_count,
    SUM(CASE WHEN (status_code between 200 and 299) THEN 0 ELSE 1 END) as error_count,
    SUM(response_time_msec) / COUNT(1) AS average_response_time_msec
+   , hll_add_agg(hll_hash_text(ip_address)) AS distinct_ip_addresses
  FROM http_request

Dashboard queries are a little more complicated, you have to read out the distinct number of IP addresses by calling the hll_cardinality function:

SELECT site_id, ingest_time as minute, request_count,
       success_count, error_count, average_response_time_msec,
       hll_cardinality(distinct_ip_addresses) AS distinct_ip_address_count
  FROM http_request_1min
 WHERE ingest_time > date_trunc('minute', now()) - interval '5 minutes';

hll is not just faster, it lets you do things you could not previously. Say we did our rollups, but instead of using hll we saved the exact unique counts. This works fine, but you cannnot answer queries such as “how many distinct sessions were there during this one-week period in the past we've thrown away the raw data for?”.

With hll, this is easy. You can compute distinct IP counts over a time period with the following query:

SELECT hll_cardinality(hll_union_agg(distinct_ip_addresses))
FROM http_request_1min
WHERE ingest_time > date_trunc('minute', now()) - '5 minutes'::interval;

You can find more information about the hll extension in the project's GitHub repository.

The citus extension works well with Postgres Pro built-in support for unstructured data types. To demonstrate this, let's keep track of the number of visitors, which came from each country. Using a semi-structure data type saves you from needing to add a column for every individual country and ending up with rows that have hundreds of sparsely filled columns. It is recommended to use the JSONB format, here we will demonstrate how to incorporate JSONB columns into your data model.

First, add the new column to our rollup table:

ALTER TABLE http_request_1min ADD COLUMN country_counters JSONB;

Next, include it in the rollups by modifying the rollup function:

@@ -1,14 +1,19 @@
  INSERT INTO http_request_1min (
    site_id, ingest_time, request_count,
    success_count, error_count, average_response_time_msec
+   , country_counters
  ) SELECT
    site_id,
    date_trunc('minute', ingest_time),
    COUNT(1) as request_count,
    SUM(CASE WHEN (status_code between 200 and 299) THEN 1 ELSE 0 END) as success_count
    SUM(CASE WHEN (status_code between 200 and 299) THEN 0 ELSE 1 END) as error_count
    SUM(response_time_msec) / COUNT(1) AS average_response_time_msec
- FROM http_request
+   , jsonb_object_agg(request_country, country_count) AS country_counters
+ FROM (
+   SELECT *,
+     count(1) OVER (
+       PARTITION BY site_id, date_trunc('minute', ingest_time), request_country
+     ) AS country_count
+   FROM http_request
+ ) h

Now, if you want to get the number of requests that came from America in your dashboard, you can modify the dashboard query to look like this:

SELECT
  request_count, success_count, error_count, average_response_time_msec,
  COALESCE(country_counters->>'USA', '0')::int AS american_visitors
FROM http_request_1min
WHERE ingest_time > date_trunc('minute', now()) - '5 minutes'::interval;

We can mix the single-node table partitioning techniques with citus distributed sharding to make a scalable time-series database. It is the best of both worlds. It is especially elegant atop Postgres Pro declarative table partitioning. See the figure below to learn more.

For example, let's distribute and partition a table holding the historical GitHub events data.

Each record in this GitHub data set represents an event created in GitHub, along with key information regarding the event such as event type, creation date, and the user who created the event.

The first step is to create and partition the table by time as we would in a single-node Postgres Pro database:

-- Declaratively partitioned table
CREATE TABLE github_events (
  event_id bigint,
  event_type text,
  event_public boolean,
  repo_id bigint,
  payload jsonb,
  repo jsonb,
  actor jsonb,
  org jsonb,
  created_at timestamp
) PARTITION BY RANGE (created_at);

Notice the PARTITION BY RANGE (created_at). This tells Postgres Pro that the table will be partitioned by the created_at column in ordered ranges. We have not yet created any partitions for specific ranges, though.

Before creating specific partitions, let's distribute the table in citus. We will shard by repo_id, meaning the events will be clustered into shards per repository.

SELECT create_distributed_table('github_events', 'repo_id');

At this point citus has created shards for this table across worker nodes. Internally each shard is a table with the name github_events_N for each shard identifier N. Also, citus propagated the partitioning information, and each of these shards has Partition key: RANGE (created_at) declared.

A partitioned table cannot directly contain data, it is more like a view across its partitions. Thus the shards are not yet ready to hold data. We need to create partitions and specify their time ranges, after which we can insert data that match the ranges.

citus provides helper functions for partition management. We can create a batch of monthly partitions using the create_time_partitions function:

SELECT create_time_partitions(
  table_name         := 'github_events',
  partition_interval := '1 month',
  end_at             := now() + '12 months'
);

citus also includes the time_partitions view for an easy way to investigate the partitions it has created.

SELECT partition
  FROM time_partitions
 WHERE parent_table = 'github_events'::regclass;

┌────────────────────────┐
│       partition        │
├────────────────────────┤
│ github_events_p2021_10 │
│ github_events_p2021_11 │
│ github_events_p2021_12 │
│ github_events_p2022_01 │
│ github_events_p2022_02 │
│ github_events_p2022_03 │
│ github_events_p2022_04 │
│ github_events_p2022_05 │
│ github_events_p2022_06 │
│ github_events_p2022_07 │
│ github_events_p2022_08 │
│ github_events_p2022_09 │
│ github_events_p2022_10 │
└────────────────────────┘

As time progresses, you will need to do some maintenance to create new partitions and drop old ones. It is best to set up a periodic job to run the maintenance functions with an extension like pg_cron:

-- Set two monthly cron jobs:

-- 1. Ensure we have partitions for the next 12 months

SELECT cron.schedule('create-partitions', '0 0 1 * *', $$
  SELECT create_time_partitions(
      table_name         := 'github_events',
      partition_interval := '1 month',
      end_at             := now() + '12 months'
  )
$$);

-- 2. (Optional) Ensure we never have more than one year of data

SELECT cron.schedule('drop-partitions', '0 0 1 * *', $$
  CALL drop_old_time_partitions(
      'github_events',
      now() - interval '12 months' /* older_than */
  );
$$);

Some applications have data that logically divides into a small updatable part and a larger part that is “frozen”. Examples include logs, clickstreams, or sales records. In this case we can combine partitioning with columnar table storage to compress historical partitions on disk. citus columnar tables are currently append-only, meaning they do not support updates or deletes, but we can use them for the immutable historical partitions.

A partitioned table may be made up of any combination of row and columnar partitions. When using range partitioning on a timestamp key, we can make the newest partition a row table, and periodically roll the newest partition into another historical columnar partition.

Let's see an example, using GitHub events again. We will create a new table called github_columnar_events for disambiguation from the earlier example. To focus entirely on the columnar storage aspect, we will not distribute this table.

Next, download sample data:

wget http://examples.citusdata.com/github_archive/github_events-2015-01-01-{0..5}.csv.gz
gzip -c -d github_events-2015-01-01-*.gz >> github_events.csv

-- Our new table, same structure as the example in
-- the previous section

CREATE TABLE github_columnar_events ( LIKE github_events )
PARTITION BY RANGE (created_at);

-- Create partitions to hold two hours of data each

SELECT create_time_partitions(
  table_name         := 'github_columnar_events',
  partition_interval := '2 hours',
  start_from         := '2015-01-01 00:00:00',
  end_at             := '2015-01-01 08:00:00'
);

-- Fill with sample data
-- (note that this data requires the database to have UTF8 encoding)

\COPY github_columnar_events FROM 'github_events.csv' WITH (format CSV)

-- List the partitions, and confirm they are
-- using row-based storage (heap access method)

SELECT partition, access_method
  FROM time_partitions
 WHERE parent_table = 'github_columnar_events'::regclass;

┌─────────────────────────────────────────┬───────────────┐
│                partition                │ access_method │
├─────────────────────────────────────────┼───────────────┤
│ github_columnar_events_p2015_01_01_0000 │ heap          │
│ github_columnar_events_p2015_01_01_0200 │ heap          │
│ github_columnar_events_p2015_01_01_0400 │ heap          │
│ github_columnar_events_p2015_01_01_0600 │ heap          │
└─────────────────────────────────────────┴───────────────┘

-- Convert older partitions to use columnar storage

CALL alter_old_partitions_set_access_method(
  'github_columnar_events',
  '2015-01-01 06:00:00' /* older_than */,
  'columnar'
);

-- The old partitions are now columnar, while the
-- latest uses row storage and can be updated

SELECT partition, access_method
  FROM time_partitions
 WHERE parent_table = 'github_columnar_events'::regclass;

┌─────────────────────────────────────────┬───────────────┐
│                partition                │ access_method │
├─────────────────────────────────────────┼───────────────┤
│ github_columnar_events_p2015_01_01_0000 │ columnar      │
│ github_columnar_events_p2015_01_01_0200 │ columnar      │
│ github_columnar_events_p2015_01_01_0400 │ columnar      │
│ github_columnar_events_p2015_01_01_0600 │ heap          │
└─────────────────────────────────────────┴───────────────┘

To see the compression ratio for a columnar table, use VACUUM VERBOSE. The compression ratio for our three columnar partitions is pretty good:

VACUUM VERBOSE github_columnar_events;

INFO:  statistics for "github_columnar_events_p2015_01_01_0000":
storage id: 10000000003
total file size: 4481024, total data size: 4444425
compression rate: 8.31x
total row count: 15129, stripe count: 1, average rows per stripe: 15129
chunk count: 18, containing data for dropped columns: 0, zstd compressed: 18

INFO:  statistics for "github_columnar_events_p2015_01_01_0200":
storage id: 10000000004
total file size: 3579904, total data size: 3548221
compression rate: 8.26x
total row count: 12714, stripe count: 1, average rows per stripe: 12714
chunk count: 18, containing data for dropped columns: 0, zstd compressed: 18

INFO:  statistics for "github_columnar_events_p2015_01_01_0400":
storage id: 10000000005
total file size: 2949120, total data size: 2917407
compression rate: 8.51x
total row count: 11756, stripe count: 1, average rows per stripe: 11756
chunk count: 18, containing data for dropped columns: 0, zstd compressed: 18

One power of the partitioned table github_columnar_events is that it can be queried in its entirety like a normal table.

SELECT COUNT(DISTINCT repo_id)
  FROM github_columnar_events;

┌───────┐
│ count │
├───────┤
│ 16001 │
└───────┘

Entries can be updated or deleted, as long as there is a WHERE clause on the partition key, which filters entirely into row table partitions.

-- A monthly cron job

SELECT cron.schedule('compress-partitions', '0 0 1 * *', $$
  CALL alter_old_partitions_set_access_method(
    'github_columnar_events',
    now() - interval '6 months' /* older_than */,
    'columnar'
  );
$$);

The traditional way in which citus shards tables is the single database, shared schema model also known as row-based sharding, tenants co-exist as rows within the same table. The tenant is determined by defining the distribution column, which allows splitting up a table horizontally.

This is the most hardware efficient way of sharding. Tenants are densely packed and distributed among the nodes in the cluster. This approach, however, requires making sure that all tables in the schema have the distribution column and that all queries in the application filter by it. Row-based sharding shines in IoT workloads and for achieving the best margin out of hardware use.

Benefits:

Drawbacks:

Schema-based sharding is the shared database, separate schema model, the schema becomes the logical shard within the database. Multi-tenant apps can a use a schema per tenant to easily shard along the tenant dimension. Query changes are not required and the application usually only needs a small modification to set the proper search_path when switching tenants. Schema-based sharding is an ideal solution for microservices, and for ISVs deploying applications that cannot undergo the changes required to onboard row-based sharding.

Benefits:

Drawbacks:

There are several types of tables in a citus cluster, each used for different purposes.

The previous section described a shard as containing a subset of the rows of a distributed table in a smaller table within a worker node. This section gets more into the technical details.

The pg_dist_shard metadata table on the coordinator contains a row for each shard of each distributed table in the system. The row matches a shardid with a range of integers in a hash space (shardminvalue, shardmaxvalue):

SELECT * FROM pg_dist_shard;
 logicalrelid  | shardid | shardstorage | shardminvalue | shardmaxvalue
---------------+---------+--------------+---------------+---------------
 github_events |  102026 | t            | 268435456     | 402653183
 github_events |  102027 | t            | 402653184     | 536870911
 github_events |  102028 | t            | 536870912     | 671088639
 github_events |  102029 | t            | 671088640     | 805306367
 (4 rows)

If the coordinator node wants to determine which shard holds a row of github_events, it hashes the value of the distribution column in the row, and checks which shard's range contains the hashed value. (The ranges are defined so that the image of the hash function is their disjoint union.)

SELECT
    shardid,
    node.nodename,
    node.nodeport
FROM pg_dist_placement placement
JOIN pg_dist_node node
  ON placement.groupid = node.groupid
 AND node.noderole = 'primary'::noderole
WHERE shardid = 102027;

┌─────────┬───────────┬──────────┐
│ shardid │ nodename  │ nodeport │
├─────────┼───────────┼──────────┤
│  102027 │ localhost │     5433 │
└─────────┴───────────┴──────────┘

Since shards can be placed on nodes as desired, it makes sense to place shards containing related rows of related tables together on the same nodes. That way join queries between them can avoid sending as much information over the network, and can be performed inside a single citus node.

One example is a database with stores, products, and purchases. If all three tables contain — and are distributed by — the store_id column, then all queries restricted to a single store can run efficiently on a single worker node. This is true even when the queries involve any combination of these tables.

For the full explanation and examples of this concept, see the Table Co-Location section.

Spreading queries across multiple computers allows more queries to run at once, and allows processing speed to scale by adding new computers to the cluster. Additionally splitting a single query into fragments as described in the previous section boosts the processing power devoted to it. The latter situation achieves the greatest parallelism, meaning utilization of CPU cores.

Queries reading or affecting shards spread evenly across many nodes are able to run at “real-time” speed. Note that the results of the query still need to pass back through the coordinator node, so the speedup is most apparent when the final results are compact, such as aggregate functions like counting and descriptive statistics.

The Query Processing section explains more about how queries are broken into fragments and how their execution is managed.

The multi-tenant architecture uses a form of hierarchical database modeling to distribute queries across nodes in the distributed cluster. The top of the data hierarchy is known as the tenant_id, and needs to be stored in a column on each table. citus inspects queries to see which tenant_id they involve and routes the query to a single worker node for processing, specifically the node that holds the data shard associated with the tenant_id. Running a query with all relevant data placed on the same node is called co-location.

The following diagram illustrates co-location in the multi-tenant data model. It contains two tables, Accounts and Campaigns, each distributed by account_id. The shaded boxes represent shards, each of whose color represents which worker node contains it. Green shards are stored together on one worker node, and blue on another. Notice how a join query between Accounts and Campaigns would have all the necessary data together on one node when restricting both tables to the same account_id.

To apply this design in your own schema the first step is identifying what constitutes a tenant in your application. Common instances include company, account, organization, or customer. The column name will be something like company_id or customer_id. Examine each of your queries and ask yourself: would it work if it had additional WHERE clauses to restrict all tables involved to rows with the same tenant_id? Queries in the multi-tenant model are usually scoped to a tenant, for instance, queries on sales or inventory would be scoped within a certain store.

Best practices are as follows:

Consult the Multi-Tenant Applications section for a detailed example of building this kind of application.

While the multi-tenant architecture introduces a hierarchical structure and uses data co-location to route queries per tenant, real-time architectures depend on specific distribution properties of their data to achieve highly parallel processing.

We use “entity ID” as a term for distribution columns in the real-time model, as opposed to tenant IDs in the multi-tenant model. Typical entities are users, hosts, or devices.

Real-time queries typically ask for numeric aggregates grouped by date or category. citus sends these queries to each shard for partial results and assembles the final answer on the coordinator node. Queries run fastest when as many nodes contribute as possible, and when no single node must do a disproportionate amount of work.

Best practices are as follows:

Consult the Real-Time Dashboards section for a detailed example of building this kind of application.

In a time-series workload, applications query recent information while archiving old information.

The most common mistake in modeling timeseries information in citus is using the timestamp itself as a distribution column. A hash distribution based on time will distribute times seemingly at random into different shards rather than keeping ranges of time together in shards. However, queries involving time generally reference ranges of time (for example, the most recent data), so such a hash distribution would lead to network overhead.

Best practices are as follows:

Consult the Timeseries Data section for a detailed example of building this kind of application.

Relational databases are the first choice of data store for many applications due to their enormous flexibility and reliability. Historically the one criticism of relational databases is that they can run on only a single computer, which creates inherent limitations when data storage needs outpace server improvements. The solution to rapidly scaling databases is to distribute them, but this creates a performance problem of its own: relational operations such as joins then need to cross the network boundary. Co-location is the practice of dividing data tactically, where one keeps related information on the same computers to enable efficient relational operations, but takes advantage of the horizontal scalability for the whole dataset.

The principle of data co-location is that all tables in the database have a common distribution column and are sharded across computers in the same way, such that rows with the same distribution column value are always on the same computer, even across different tables. As long as the distribution column provides a meaningful grouping of data, relational operations can be performed within the groups.

CREATE TABLE event (
  tenant_id int,
  event_id bigint,
  page_id int,
  payload jsonb,
  primary key (tenant_id, event_id)
);

CREATE TABLE page (
  tenant_id int,
  page_id int,
  path text,
  primary key (tenant_id, page_id)
);

SELECT page_id, count(event_id)
FROM
  page
LEFT JOIN  (
  SELECT * FROM event
  WHERE (payload->>'time')::timestamptz >= now() - interval '1 week'
) recent
USING (tenant_id, page_id)
WHERE tenant_id = 6 AND path LIKE '/blog%'
GROUP BY page_id;

-- Naively use event_id and page_id as distribution columns

SELECT create_distributed_table('event', 'event_id');
SELECT create_distributed_table('page', 'page_id');

SELECT page_id FROM page WHERE path LIKE '/blog%' AND tenant_id = 6;

SELECT page_id, count(*) AS count
FROM event
WHERE page_id IN (/*…page IDs from first query…*/)
  AND tenant_id = 6
  AND (payload->>'time')::date >= now() - interval '1 week'
GROUP BY page_id ORDER BY count DESC LIMIT 10;

-- Co-locate tables by using a common distribution column
SELECT create_distributed_table('event', 'tenant_id');
SELECT create_distributed_table('page', 'tenant_id', colocate_with => 'event');

SELECT page_id, count(event_id)
FROM
  page
LEFT JOIN  (
  SELECT * FROM event
  WHERE (payload->>'time')::timestamptz >= now() - interval '1 week'
) recent
USING (tenant_id, page_id)
WHERE tenant_id = 6 AND path LIKE '/blog%'
GROUP BY page_id;

Once the scope of needed database changes is identified, the next major step is to modify the data structure for the application's existing database. First, tables requiring backfill are modified to add a column for the distribution key.

-- Denormalize line_items by including store_id

ALTER TABLE line_items ADD COLUMN store_id uuid;

UPDATE line_items
   SET store_id = orders.store_id
  FROM line_items
 INNER JOIN orders
 WHERE line_items.order_id = orders.order_id;

-- The function to backfill up to ten
-- thousand rows from line_items

CREATE FUNCTION backfill_batch()
RETURNS void LANGUAGE sql AS $$
  WITH batch AS (
    SELECT line_items_id, order_id
      FROM line_items
     WHERE store_id IS NULL
     LIMIT 1000
       FOR UPDATE
      SKIP LOCKED
  )
  UPDATE line_items AS li
     SET store_id = orders.store_id
    FROM batch, orders
   WHERE batch.line_item_id = li.line_item_id
     AND batch.order_id = orders.order_id;
$$;

-- Run the function every quarter hour
SELECT cron.schedule('*/15 * * * *', 'SELECT backfill_batch()');

-- Note the return value of cron.schedule

-- Assuming 42 is the job id returned
-- from cron.schedule

SELECT cron.unschedule(42);

At this time, having updated the database schema and application queries to work with citus, you are ready for the final step. It is time to migrate data to the citus cluster and cut over the application to its new database. The data migration procedure is presented in the Database Migration section.

Applications like event data pipelines and real-time dashboards require sub-second queries on large volumes of data. One way to make these queries fast is by calculating and saving aggregates ahead of time. This is called “rolling up” the data and it avoids the cost of processing raw data at run-time. As an extra benefit, rolling up timeseries data into hourly or daily statistics can also save space. Old data may be deleted when its full details are no longer needed and aggregates suffice.

For example, here is a distributed table for tracking page views by URL:

CREATE TABLE page_views (
  site_id int,
  url text,
  host_ip inet,
  view_time timestamp default now(),

  PRIMARY KEY (site_id, url)
);

SELECT create_distributed_table('page_views', 'site_id');

Once the table is populated with data, we can run an aggregate query to count page views per URL per day, restricting to a given site and year.

-- How many views per url per day on site 5?
SELECT view_time::date AS day, site_id, url, count(*) AS view_count
  FROM page_views
  WHERE site_id = 5 AND
    view_time >= date '2016-01-01' AND view_time < date '2017-01-01'
  GROUP BY view_time::date, site_id, url;

The setup described above works but has two drawbacks. First, when you repeatedly execute the aggregate query, it must go over each related row and recompute the results for the entire data set. If you are using this query to render a dashboard, it is faster to save the aggregated results in a daily page views table and query that table. Second, storage costs will grow proportionally with data volumes and the length of queryable history. In practice, you may want to keep raw events for a short time period and look at historical graphs over a longer time window.

To receive those benefits, we can create the daily_page_views table to store the daily statistics.

CREATE TABLE daily_page_views (
  site_id int,
  day date,
  url text,
  view_count bigint,
  PRIMARY KEY (site_id, day, url)
);

SELECT create_distributed_table('daily_page_views', 'site_id');

In this example, we distributed both page_views and daily_page_views on the site_id column. This ensures that data corresponding to a particular site will be co-located on the same node. Keeping the rows of the two tables together on each node minimizes network traffic between nodes and enables highly parallel execution.

Once we create this new distributed table, we can then run INSERT INTO ... SELECT to roll up raw page views into the aggregated table. In the following, we aggregate page views each day. citus users often wait for a certain time period after the end of day to run a query like this, to accommodate late arriving data.

-- Roll up yesterday's data
INSERT INTO daily_page_views (day, site_id, url, view_count)
  SELECT view_time::date AS day, site_id, url, count(*) AS view_count
  FROM page_views
  WHERE view_time >= date '2017-01-01' AND view_time < date '2017-01-02'
  GROUP BY view_time::date, site_id, url;

-- Now the results are available right out of the table
SELECT day, site_id, url, view_count
  FROM daily_page_views
  WHERE site_id = 5 AND
    day >= date '2016-01-01' AND day < date '2017-01-01';

The rollup query above aggregates data from the previous day and inserts it into the daily_page_views table. Running the query once each day means that no rollup tables rows need to be updated, because the new day's data does not affect previous rows.

The situation changes when dealing with late arriving data, or running the rollup query more than once per day. If any new rows match days already in the rollup table, the matching counts should increase. Postgres Pro can handle this situation with ON CONFLICT, which is its technique for doing UPSERTS. Here is an example.

-- Roll up from a given date onward,
-- updating daily page views when necessary
INSERT INTO daily_page_views (day, site_id, url, view_count)
  SELECT view_time::date AS day, site_id, url, count(*) AS view_count
  FROM page_views
  WHERE view_time >= date '2017-01-01'
  GROUP BY view_time::date, site_id, url
  ON CONFLICT (day, url, site_id) DO UPDATE SET
    view_count = daily_page_views.view_count + EXCLUDED.view_count;

DELETE FROM github_events
WHERE repo_id IN (24509048, 24509049);

UPDATE github_events
SET event_public = TRUE
WHERE (org->>'id')::int = 5430905;

SET citus.multi_shard_commit_protocol = '2pc';

-- Since github_events is distributed by repo_id,
-- this will execute in a single worker node

DELETE FROM github_events
WHERE repo_id = 206084;

BEGIN;

  -- Select events for a repo, but
  -- lock them for writing
  SELECT *
  FROM github_events
  WHERE repo_id = 206084
  FOR UPDATE;

  -- Calculate a desired value event_public using
  -- application logic that uses those rows

  -- Now make the update
  UPDATE github_events
  SET event_public = :our_new_value
  WHERE repo_id = 206084;

COMMIT;

As discussed in the previous sections, citus extends the latest Postgres Pro for distributed execution. This means that you can use standard Postgres Pro SELECT queries on the citus coordinator. The extension will then parallelize the SELECT queries involving complex selections, groupings and orderings, and JOINs to speed up the query performance. At a high level, citus partitions the SELECT query into smaller query fragments, assigns these query fragments to workers, oversees their execution, merges their results (and orders them if needed), and returns the final result to the user.

In the following sections, we discuss the different types of queries you can run using citus.

SELECT sum(value1), sum(distinct value2) FROM distributed_table;

SET citus.coordinator_aggregation_strategy TO 'disabled';

-- Multiple distinct counts in one query tend to be slow
SELECT count(distinct a), count(distinct b), count(distinct c)
FROM table_abc;

-- Starting from nothing, record that we saw an "a"
SELECT topn_add('{}', 'a');
-- => {"a": 1}

-- Record the sighting of another "a"
SELECT topn_add(topn_add('{}', 'a'), 'a');
-- => {"a": 2}

-- For normal_rand
CREATE EXTENSION tablefunc;

-- Count values from a normal distribution
SELECT topn_add_agg(floor(abs(i))::text)
  FROM normal_rand(1000, 5, 0.7) i;
-- => {"2": 1, "3": 74, "4": 420, "5": 425, "6": 77, "7": 3}

curl -L https://examples.citusdata.com/customer_reviews_2000.csv.gz | \
  gunzip > reviews.csv

CREATE TABLE customer_reviews
(
    customer_id TEXT,
    review_date DATE,
    review_rating INTEGER,
    review_votes INTEGER,
    review_helpful_votes INTEGER,
    product_id CHAR(10),
    product_title TEXT,
    product_sales_rank BIGINT,
    product_group TEXT,
    product_category TEXT,
    product_subcategory TEXT,
    similar_product_ids CHAR(10)[]
);

SELECT create_distributed_table('customer_reviews', 'product_id');

\COPY customer_reviews FROM 'reviews.csv' WITH CSV

-- Run below command from coordinator, it will be propagated to the worker nodes as well
CREATE EXTENSION topn;

-- A table to materialize the daily aggregate
CREATE TABLE reviews_by_day
(
  review_date date unique,
  agg_data jsonb
);

SELECT create_reference_table('reviews_by_day');

-- Materialize how many reviews each product got per day per customer
INSERT INTO reviews_by_day
  SELECT review_date, topn_add_agg(product_id)
  FROM customer_reviews
  GROUP BY review_date;

SELECT review_date, (topn(agg_data, 1)).*
FROM reviews_by_day
ORDER BY review_date
LIMIT 5;

┌─────────────┬────────────┬───────────┐
│ review_date │    item    │ frequency │
├─────────────┼────────────┼───────────┤
│ 2000-01-01  │ 0939173344 │        12 │
│ 2000-01-02  │ B000050XY8 │        11 │
│ 2000-01-03  │ 0375404368 │        12 │
│ 2000-01-04  │ 0375408738 │        14 │
│ 2000-01-05  │ B00000J7J4 │        17 │
└─────────────┴────────────┴───────────┘

SELECT (topn(topn_union_agg(agg_data), 5)).*
FROM reviews_by_day
WHERE review_date >= '2000-01-01' AND review_date < '2000-02-01'
ORDER BY 2 DESC;

┌────────────┬───────────┐
│    item    │ frequency │
├────────────┼───────────┤
│ 0375404368 │       217 │
│ 0345417623 │       217 │
│ 0375404376 │       217 │
│ 0375408738 │       217 │
│ 043936213X │       204 │
└────────────┴───────────┘

SET citus.limit_clause_row_fetch_count TO 10000;

A citus cluster consists of a coordinator instance and multiple worker instances. The data is sharded on the workers while the coordinator stores metadata about these shards. All queries issued to the cluster are executed via the coordinator. The coordinator partitions the query into smaller query fragments where each query fragment can be run independently on a shard. The coordinator then assigns the query fragments to workers, oversees their execution, merges their results, and returns the final result to the user. The query processing architecture can be described in brief by the diagram below.

citus query processing pipeline involves the two components:

We discuss them in greater detail in the subsequent sections.

SELECT page_id, count(distinct host_ip)
FROM page_views
WHERE page_id IN (
  SELECT page_id
  FROM page_views
  GROUP BY page_id
  ORDER BY count(*) DESC
  LIMIT 20
)
GROUP BY page_id;

GroupAggregate  (cost=0.00..0.00 rows=0 width=0)
  Group Key: remote_scan.page_id
  ->  Sort  (cost=0.00..0.00 rows=0 width=0)
    Sort Key: remote_scan.page_id
    ->  Custom Scan (Citus Adaptive)  (cost=0.00..0.00 rows=0 width=0)
      ->  Distributed Subplan 6_1
        ->  Limit  (cost=0.00..0.00 rows=0 width=0)
          ->  Sort  (cost=0.00..0.00 rows=0 width=0)
            Sort Key: COALESCE((pg_catalog.sum((COALESCE((pg_catalog.sum(remote_scan.worker_column_2))::bigint, '0'::bigint))))::bigint, '0'::bigint) DESC
            ->  HashAggregate  (cost=0.00..0.00 rows=0 width=0)
              Group Key: remote_scan.page_id
              ->  Custom Scan (Citus Adaptive)  (cost=0.00..0.00 rows=0 width=0)
                Task Count: 32
                Tasks Shown: One of 32
                ->  Task
                  Node: host=localhost port=9701 dbname=postgres
                  ->  HashAggregate  (cost=54.70..56.70 rows=200 width=12)
                    Group Key: page_id
                    ->  Seq Scan on page_views_102008 page_views  (cost=0.00..43.47 rows=2247 width=4)
      Task Count: 32
      Tasks Shown: One of 32
      ->  Task
        Node: host=localhost port=9701 dbname=postgres
        ->  HashAggregate  (cost=84.50..86.75 rows=225 width=36)
          Group Key: page_views.page_id, page_views.host_ip
          ->  Hash Join  (cost=17.00..78.88 rows=1124 width=36)
            Hash Cond: (page_views.page_id = intermediate_result.page_id)
            ->  Seq Scan on page_views_102008 page_views  (cost=0.00..43.47 rows=2247 width=36)
            ->  Hash  (cost=14.50..14.50 rows=200 width=4)
              ->  HashAggregate  (cost=12.50..14.50 rows=200 width=4)
                Group Key: intermediate_result.page_id
                ->  Function Scan on read_intermediate_result intermediate_result  (cost=0.00..10.00 rows=1000 width=4)

GroupAggregate  (cost=0.00..0.00 rows=0 width=0)
  Group Key: remote_scan.page_id
  ->  Sort  (cost=0.00..0.00 rows=0 width=0)
    Sort Key: remote_scan.page_id

->  Custom Scan (Citus Adaptive)  (cost=0.00..0.00 rows=0 width=0)
  ->  Distributed Subplan 6_1
.

->  Limit  (cost=0.00..0.00 rows=0 width=0)
  ->  Sort  (cost=0.00..0.00 rows=0 width=0)
    Sort Key: COALESCE((pg_catalog.sum((COALESCE((pg_catalog.sum(remote_scan.worker_column_2))::bigint, '0'::bigint))))::bigint, '0'::bigint) DESC
    ->  HashAggregate  (cost=0.00..0.00 rows=0 width=0)
      Group Key: remote_scan.page_id
      ->  Custom Scan (Citus Adaptive)  (cost=0.00..0.00 rows=0 width=0)
        Task Count: 32
        Tasks Shown: One of 32
        ->  Task
          Node: host=localhost port=9701 dbname=postgres
          ->  HashAggregate  (cost=54.70..56.70 rows=200 width=12)
            Group Key: page_id
            ->  Seq Scan on page_views_102008 page_views  (cost=0.00..43.47 rows=2247 width=4)
.

Task Count: 32
Tasks Shown: One of 32
->  Task
  Node: host=localhost port=9701 dbname=postgres
  ->  HashAggregate  (cost=84.50..86.75 rows=225 width=36)
    Group Key: page_views.page_id, page_views.host_ip
    ->  Hash Join  (cost=17.00..78.88 rows=1124 width=36)
      Hash Cond: (page_views.page_id = intermediate_result.page_id)
.

->  Seq Scan on page_views_102008 page_views  (cost=0.00..43.47 rows=2247 width=36)
->  Hash  (cost=14.50..14.50 rows=200 width=4)
  ->  HashAggregate  (cost=12.50..14.50 rows=200 width=4)
    Group Key: intermediate_result.page_id
    ->  Function Scan on read_intermediate_result intermediate_result  (cost=0.00..10.00 rows=1000 width=4)
.

When the user issues a query, the citus coordinator partitions it into smaller query fragments where each query fragment can be run independently on a worker shard. This allows citus to distribute each query across the cluster.

However, the way queries are partitioned into fragments (and which queries are propagated at all) varies by the type of query. In some advanced situations it is useful to manually control this behavior. citus provides utility functions to propagate SQL to workers, shards, or co-located placements.

Manual query propagation bypasses coordinator logic, locking, and any other consistency checks. These functions are available as a last resort to allow statements which citus otherwise does not run natively. Use them carefully to avoid data inconsistency and deadlocks.

-- List the work_mem setting of each worker database
SELECT run_command_on_workers($cmd$ SHOW work_mem; $cmd$);

-- Get the estimated row count for a distributed table by summing the
-- estimated counts of rows for each shard
SELECT sum(result::bigint) AS estimated_count
  FROM run_command_on_shards(
    'my_distributed_table',
    $cmd$
      SELECT reltuples
        FROM pg_class c
        JOIN pg_catalog.pg_namespace n on n.oid=c.relnamespace
       WHERE (n.nspname || '.' || relname)::regclass = '%s'::regclass
         AND n.nspname NOT IN ('citus', 'pg_toast', 'pg_catalog')
    $cmd$
  );

-- Suppose we have two distributed tables
CREATE TABLE little_vals (key int, val int);
CREATE TABLE big_vals    (key int, val int);
SELECT create_distributed_table('little_vals', 'key');
SELECT create_distributed_table('big_vals',    'key');

-- We want to synchronize them so that every time little_vals
-- are created, big_vals appear with double the value
--
-- First we make a trigger function, which will
-- take the destination table placement as an argument
CREATE OR REPLACE FUNCTION embiggen() RETURNS TRIGGER AS $$
  BEGIN
    IF (TG_OP = 'INSERT') THEN
      EXECUTE format(
        'INSERT INTO %s (key, val) SELECT ($1).key, ($1).val*2;',
        TG_ARGV[0]
      ) USING NEW;
    END IF;
    RETURN NULL;
  END;
$$ LANGUAGE plpgsql;

-- Next we relate the co-located tables by the trigger function
-- on each co-located placement
SELECT run_command_on_colocated_placements(
  'little_vals',
  'big_vals',
  $cmd$
    CREATE TRIGGER after_insert AFTER INSERT ON %s
      FOR EACH ROW EXECUTE PROCEDURE embiggen(%L)
  $cmd$
);

As citus provides distributed functionality by extending Postgres Pro, it is compatible with Postgres Pro constructs. This means that users can use the tools and features that come with the rich and extensible Postgres Pro 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.

SELECT * FROM dist WHERE EXISTS (SELECT 1 FROM local WHERE local.a = dist.a);
/*
ERROR:  direct joins between distributed and local tables are not supported
HINT:  Use CTEs or subqueries to select from local tables and use them in joins
*/

WITH cte AS (SELECT * FROM dist)
SELECT * FROM cte WHERE EXISTS (SELECT 1 FROM local WHERE local.a = cte.a);

-- This will not 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
HINT:  Consider using an equality filter on the distributed table's partition column.

-- 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

INSERT INTO a.widgets (map_id, widget_name)
VALUES (
    (SELECT mt.map_id FROM a.map_tags mt WHERE mt.map_license = '12345'),
    'Test'
);

INSERT INTO a.widgets (map_id, widget_name)
SELECT mt.map_id, 'Test'
  FROM a.map_tags mt
 WHERE mt.map_license = '12345';

This section contains reference information for the user defined functions provided by citus. These functions help in providing additional distributed functionality to citus other than the standard SQL commands.

There are various configuration parameters that affect the behaviour of citus. These include both standard Postgres Pro parameters and citus specific parameters. To learn more about Postgres Pro configuration parameters, you can visit the Server Configuration chapter.

The rest of this reference aims at discussing citus specific configuration parameters. These parameters can be set similar to Postgres Pro parameters by modifying postgresql.conf or by using the SET command.

As an example you can update a setting with:

ALTER DATABASE citus SET citus.multi_task_query_log_level = 'log';

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

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

citus 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.

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

SELECT citus_rebalance_start();

ALTER SYSTEM SET citus.max_background_task_executors_per_node = 2;
SELECT pg_reload_conf();

SELECT citus_rebalance_start();

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

ALTER TABLE my_table REPLICA IDENTITY
  USING INDEX my_table_idx;

SELECT * FROM citus_add_node(second_coordinator_hostname, second_coordinator_port);
SELECT * FROM citus_set_node_property(second_coordinator_hostname, second_coordinator_port, 'shouldhaveshards', false);

In this subsection, we discuss how you can deal with node failures without incurring any downtime on your citus cluster.

When administering a citus cluster it is useful to know what queries users are running, which nodes are involved, and which execution method citus is using for each query. The extension records query statistics in a metadata view called citus_stat_statements, named analogously to Postgres Pro pg_stat_statements. 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 a self-hosted Postgres Pro instance 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 will 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;

Results:

-[ 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 that provides flexibility to run these kind of queries.

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

citus provides distributed functionality by extending Postgres Pro using the hook and extension APIs. This allows users to benefit from the features that come with the rich Postgres Pro ecosystem. These features include, but are not 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 Postgres Pro tools such as pgAdmin and pg_upgrade.

As citus is an extension which can be installed on any Postgres Pro 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.

There are several extensions, which may be useful when working with citus:

Each Postgres Pro 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 will 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.

The usual way to find table sizes in Postgres Pro, pg_total_relation_size, drastically under-reports the size of distributed tables. All this function does on a citus cluster is reveal the size of tables on the coordinator node. In reality the data in distributed tables lives on the worker nodes (in shards), not on the coordinator. A true measure of distributed table size is obtained as a sum of shard sizes. citus provides helper functions to query this information.

These functions are analogous to three of the standard Postgres Pro object size functions, with the additional note that if they cannot connect to a node, they error out.

Here is an example of using one of the helper functions to list the sizes of all distributed tables:

SELECT logicalrelid AS name,
       pg_size_pretty(citus_table_size(logicalrelid)) AS size
  FROM pg_dist_partition;

Output:

┌───────────────┬───────┐
│     name      │ size  │
├───────────────┼───────┤
│ github_users  │ 39 MB │
│ github_events │ 37 MB │
└───────────────┴───────┘

In Postgres Pro (and other MVCC databases), an UPDATE or DELETE of a row does not immediately remove the old version of the row. The accumulation of outdated rows is called bloat and must be cleaned to avoid decreased query performance and unbounded growth of disk space requirements. Postgres Pro runs a process called the auto-vacuum daemon that periodically vacuums (removes) outdated rows.

It is not just user queries which scale in a distributed database, vacuuming does too. In Postgres Pro big busy tables have great potential to bloat, both from lower sensitivity to Postgres Pro vacuum scale factor parameter, and generally because of the extent of their row churn. Splitting a table into distributed shards means both that individual shards are smaller tables and that auto-vacuum workers can parallelize over different parts of the table on different machines. Ordinarily auto-vacuum can only run one worker per table.

Due to the above, auto-vacuum operations on a citus cluster are probably good enough for most cases. However, for tables with particular workloads, or companies with certain “safe” hours to schedule a vacuum, it might make more sense to manually vacuum a table rather than leaving all the work to auto-vacuum.

To vacuum a table, simply run this on the coordinator node:

VACUUM my_distributed_table;

Using vacuum against a distributed table will send the VACUUM command to every one of that table's placements (one connection per placement). This is done in parallel. All options are supported (including the table_and_columns list) except for VERBOSE. The VACUUM command also runs on the coordinator, and does so before any workers nodes are notified. Note that unqualified vacuum commands (i.e. those without a table specified) do not propagate to worker nodes.

Postgres Pro ANALYZE command collects statistics about the contents of tables in the database. Subsequently, the query planner uses these statistics to help determine the most efficient execution plans for queries.

The auto-vacuum daemon, discussed in the previous section, will automatically issue ANALYZE commands whenever the content of a table has changed sufficiently. The daemon schedules ANALYZE strictly as a function of the number of rows inserted or updated; it has no knowledge of whether that will lead to meaningful statistical changes. Administrators might prefer to manually schedule ANALYZE operations instead, to coincide with statistically meaningful table changes.

To analyze a table, run this on the coordinator node:

ANALYZE my_distributed_table;

citus propagates the ANALYZE command to all worker node placements.

citus provides append-only columnar table storage for analytic and data warehousing workloads. When columns (rather than rows) are stored contiguously on disk, data becomes more compressible, and queries can request a subset of columns more quickly.

CREATE TABLE contestant (
    handle TEXT,
    birthdate DATE,
    rating INT,
    percentile FLOAT,
    country CHAR(3),
    achievements TEXT[]
) USING columnar;

-- Convert to row-based (heap) storage
SELECT alter_table_set_access_method('contestant', 'heap');

-- Convert to columnar storage (indexes will be dropped)
SELECT alter_table_set_access_method('contestant', 'columnar');

-- Insert these values into a single columnar stripe

INSERT INTO contestant VALUES
  ('a','1990-01-10',2090,97.1,'XA','{a}'),
  ('b','1990-11-01',2203,98.1,'XA','{a,b}'),
  ('c','1988-11-01',2907,99.4,'XB','{w,y}'),
  ('d','1985-05-05',2314,98.3,'XB','{}'),
  ('e','1995-05-05',2236,98.2,'XC','{a}');

VACUUM VERBOSE contestant;

INFO:  statistics for "contestant":
storage id: 10000000000
total file size: 24576, total data size: 248
compression rate: 1.31x
total row count: 5, stripe count: 1, average rows per stripe: 5
chunk count: 6, containing data for dropped columns: 0, zstd compressed: 6

-- First a wide table using row storage
CREATE TABLE perf_row(
  c00 int8, c01 int8, c02 int8, c03 int8, c04 int8, c05 int8, c06 int8, c07 int8, c08 int8, c09 int8,
  c10 int8, c11 int8, c12 int8, c13 int8, c14 int8, c15 int8, c16 int8, c17 int8, c18 int8, c19 int8,
  c20 int8, c21 int8, c22 int8, c23 int8, c24 int8, c25 int8, c26 int8, c27 int8, c28 int8, c29 int8,
  c30 int8, c31 int8, c32 int8, c33 int8, c34 int8, c35 int8, c36 int8, c37 int8, c38 int8, c39 int8,
  c40 int8, c41 int8, c42 int8, c43 int8, c44 int8, c45 int8, c46 int8, c47 int8, c48 int8, c49 int8,
  c50 int8, c51 int8, c52 int8, c53 int8, c54 int8, c55 int8, c56 int8, c57 int8, c58 int8, c59 int8,
  c60 int8, c61 int8, c62 int8, c63 int8, c64 int8, c65 int8, c66 int8, c67 int8, c68 int8, c69 int8,
  c70 int8, c71 int8, c72 int8, c73 int8, c74 int8, c75 int8, c76 int8, c77 int8, c78 int8, c79 int8,
  c80 int8, c81 int8, c82 int8, c83 int8, c84 int8, c85 int8, c86 int8, c87 int8, c88 int8, c89 int8,
  c90 int8, c91 int8, c92 int8, c93 int8, c94 int8, c95 int8, c96 int8, c97 int8, c98 int8, c99 int8
);

-- Next a table with identical columns using columnar storage
CREATE TABLE perf_columnar(LIKE perf_row) USING COLUMNAR;

INSERT INTO perf_row
  SELECT
    g % 00500, g % 01000, g % 01500, g % 02000, g % 02500, g % 03000, g % 03500, g % 04000, g % 04500, g % 05000,
    g % 05500, g % 06000, g % 06500, g % 07000, g % 07500, g % 08000, g % 08500, g % 09000, g % 09500, g % 10000,
    g % 10500, g % 11000, g % 11500, g % 12000, g % 12500, g % 13000, g % 13500, g % 14000, g % 14500, g % 15000,
    g % 15500, g % 16000, g % 16500, g % 17000, g % 17500, g % 18000, g % 18500, g % 19000, g % 19500, g % 20000,
    g % 20500, g % 21000, g % 21500, g % 22000, g % 22500, g % 23000, g % 23500, g % 24000, g % 24500, g % 25000,
    g % 25500, g % 26000, g % 26500, g % 27000, g % 27500, g % 28000, g % 28500, g % 29000, g % 29500, g % 30000,
    g % 30500, g % 31000, g % 31500, g % 32000, g % 32500, g % 33000, g % 33500, g % 34000, g % 34500, g % 35000,
    g % 35500, g % 36000, g % 36500, g % 37000, g % 37500, g % 38000, g % 38500, g % 39000, g % 39500, g % 40000,
    g % 40500, g % 41000, g % 41500, g % 42000, g % 42500, g % 43000, g % 43500, g % 44000, g % 44500, g % 45000,
    g % 45500, g % 46000, g % 46500, g % 47000, g % 47500, g % 48000, g % 48500, g % 49000, g % 49500, g % 50000
  FROM generate_series(1,50000000) g;

INSERT INTO perf_columnar
  SELECT
    g % 00500, g % 01000, g % 01500, g % 02000, g % 02500, g % 03000, g % 03500, g % 04000, g % 04500, g % 05000,
    g % 05500, g % 06000, g % 06500, g % 07000, g % 07500, g % 08000, g % 08500, g % 09000, g % 09500, g % 10000,
    g % 10500, g % 11000, g % 11500, g % 12000, g % 12500, g % 13000, g % 13500, g % 14000, g % 14500, g % 15000,
    g % 15500, g % 16000, g % 16500, g % 17000, g % 17500, g % 18000, g % 18500, g % 19000, g % 19500, g % 20000,
    g % 20500, g % 21000, g % 21500, g % 22000, g % 22500, g % 23000, g % 23500, g % 24000, g % 24500, g % 25000,
    g % 25500, g % 26000, g % 26500, g % 27000, g % 27500, g % 28000, g % 28500, g % 29000, g % 29500, g % 30000,
    g % 30500, g % 31000, g % 31500, g % 32000, g % 32500, g % 33000, g % 33500, g % 34000, g % 34500, g % 35000,
    g % 35500, g % 36000, g % 36500, g % 37000, g % 37500, g % 38000, g % 38500, g % 39000, g % 39500, g % 40000,
    g % 40500, g % 41000, g % 41500, g % 42000, g % 42500, g % 43000, g % 43500, g % 44000, g % 44500, g % 45000,
    g % 45500, g % 46000, g % 46500, g % 47000, g % 47500, g % 48000, g % 48500, g % 49000, g % 49500, g % 50000
  FROM generate_series(1,50000000) g;

VACUUM (FREEZE, ANALYZE) perf_row;
VACUUM (FREEZE, ANALYZE) perf_columnar;

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

.
 compression_ratio
--------------------
 8.0196135873627944
(1 row)