Scaling Out Data Ingestion¶
Citus lets you scale out data ingestion to very high rates, but there are several trade-offs to consider in terms of the throughput, durability, consistency and latency. In this section, we discuss several approaches to data ingestion and give examples of how to use them.
The best method to distribute tables and ingest your data depends on your use case requirements. Citus supports two distribution methods: append and hash; and the data ingestion methods differ between them. You can visit the Working with distributed tables section to learn about the tradeoffs associated with each distribution method.
Hash Distributed Tables¶
Hash distributed tables support ingestion using standard single row INSERT and UPDATE commands. This sub-section describes how you maximize insert throughput for hash distributed tables.
Real-time Inserts (0-50k/s)¶
On the Citus master, you can perform INSERT commands directly on hash distributed tables. The advantage of using INSERT is that the new data is immediately visible to SELECT queries, and durably stored on multiple replicas.
When processing an INSERT, Citus first finds the right shard placements based on the value in the distribution column, then it connects to the workers storing the shard placements, and finally performs an INSERT on each of them. From the perspective of the user, the INSERT takes several milliseconds to process because of the round-trips to the workers, but the master can process other INSERTs in other sessions while waiting for a response. The master also keeps connections to the workers open within the same session, which means subsequent queries will see lower response times.
-- Set up a distributed table containing counters CREATE TABLE counters (c_key text, c_date date, c_value int, primary key (c_key, c_date)); SELECT master_create_distributed_table('counters', 'c_key', 'hash'); SELECT master_create_worker_shards('counters', 128, 2); -- Enable timing to see reponse times \timing -- First INSERT requires connection set-up, second will be faster INSERT INTO counters VALUES ('num_purchases', '2016-03-04', 12); -- Time: 10.314 ms INSERT INTO counters VALUES ('num_purchases', '2016-03-05', 5); -- Time: 3.132 ms
INSERT is currently the only way of adding data to hash-distributed tables. To load data from a file into a hash-distributed table, Citus comes with a command-line tool called copy_to_distributed_table, which mimicks the behaviour of COPY by performing an INSERT for each row in an input (CSV) file. However, the script only uses a single connection, meaning every INSERT waits for several round-trips and throughput is very low by default. However, you can parallelize the script by splitting the input and doing the INSERTs in parallel using xargs, which gives vastly better throughput.
For example, for Linux systems you can use the split command to split the input file into 64 pieces.
mkdir chunks split -n l/64 github_events-2015-01-01-0.csv chunks/
Then, you can load each of the chunks in parallel using xargs.
export PGDATABASE=postgres find chunks/ -type f | xargs -n 1 -P 64 sh -c 'echo $0 `copy_to_distributed_table -C $0 github_events`'
To learn more about the copy_to_distributed_table script, you can visit the Hash Distribution section of our documentation.
To reach high throughput rates, applications should send INSERTs over a many separate connections and keep connections open to avoid the initial overhead of connection set-up.
Real-time Updates (0-50k/s)¶
On the Citus master, you can also perform UPDATE, DELETE, and INSERT … ON CONFLICT (UPSERT) commands on distributed tables. By default, these queries take an exclusive lock on the shard, which prevents concurrent modifications to guarantee that the commands are applied in the same order on all shard placements.
Given that every command requires several round-trips to the workers, and no two commands can run on the same shard at the same time, update throughput is very low by default. However, if you know that the order of the queries doesn’t matter (they are commutative), then you can turn on citus.all_modifications_commutative, in which case multiple commands can update the same shard concurrently.
For example, if your distributed table contains counters and all your DML queries are UPSERTs that add to the counters, then you can safely turn on citus.all_modifications_commutative since addition is commutative:
SET citus.all_modifications_commutative TO on; INSERT INTO counters VALUES ('num_purchases', '2016-03-04', 1) ON CONFLICT (c_key, c_date) DO UPDATE SET c_value = counters.c_value + 1;
Note that this query also takes an exclusive lock on the row in PostgreSQL, which may also limit the throughput. When storing counters, consider that using INSERT and summing values in a SELECT does not require exclusive locks.
When the replication factor is 1, it is always safe to enable citus.all_modifications_commutative. Citus does not do this automatically yet.
Masterless Citus (50k/s-500k/s)¶
This section is currently experimental and not a guide to setup masterless clusters in production. We are working on providing official support for masterless clusters including replication and automated fail-over solutions. Please contact us at email@example.com if your use case requires multiple masters.
It is technically possible to create the distributed table on every node in the cluster. The big advantage is that all queries on distributed tables can be performed at a very high rate by spreading the queries across the workers. In this case, the replication factor should always be 1 to ensure consistency, which causes data to become unavailable when a node goes down. All nodes should have a hot standby and automated fail-over to ensure high availability.
To allow DML commands on the distribute table from any node, first create a distributed table on both the master and the workers:
CREATE TABLE data (key text, value text); SELECT master_create_distributed_table('data','key','hash');
Then on the master, create shards for the distributed table with a replication factor of 1.
/* Create 128 shards with a single replica on the workers */ SELECT master_create_worker_shards('data', 128, 1);
Finally, you need to copy and convert the shard metadata from the master to the workers. The logicalrelid column in pg_dist_shard may differ per node. If you have the dblink extension installed, then you can run the following commands on the workers to get the metadata from master-node.
INSERT INTO pg_dist_shard SELECT * FROM dblink('host=master-node port=5432', 'SELECT logicalrelid::regclass,shardid,shardstorage,shardalias,shardminvalue,shardmaxvalue FROM pg_dist_shard') AS (logicalrelid regclass, shardid bigint, shardstorage char, shardalias text, shardminvalue text, shardmaxvalue text); INSERT INTO pg_dist_shard_placement SELECT * FROM dblink('host=master-node port=5432', 'SELECT * FROM pg_dist_shard_placement') AS (shardid bigint, shardstate int, shardlength bigint, nodename text, nodeport int);
After these commands, you can connect to any node and perform both SELECT and DML commands on the distributed table. However, DDL commands won’t be supported.
Append Distributed Tables¶
If your use-case does not require real-time ingests, then using append distributed tables will give you the highest ingest rates. This approach is more suitable for use-cases which use time-series data and where the database can be a few minutes or more behind.
Master Node Bulk Ingestion (50k/s-100k/s)¶
To ingest data into an append-distributed table, your application can first create a staging table, copy or insert data into the staging table, and finally append the staging table to the distributed table. The simplest approach is to create the staging table on the master and append the table to a new shard using master_append_table_to_shard:
-- Set up the events table CREATE TABLE events (time timestamp, data jsonb); SELECT master_create_distributed_table('events', 'time', 'append'); -- Add data into a new staging table CREATE UNLOGGED TABLE stage_1 (LIKE events); COPY stage_1 FROM 'path-to-csv-file' WITH CSV; -- followed by CSV data -- Add the data in the staging table to a new shard in the events table and drop the staging table SELECT master_append_table_to_shard(master_create_empty_shard('events'), 'stage_1', 'master-node', 5432); DROP TABLE stage_1;
Note that copying to the staging table and appending to a shard need to be in separate transactions. Otherwise, the data would not yet be visible to the workers when trying to append. You can choose to make the staging table unlogged for better performance if you can easily reload the data. To learn more about the master_append_table_to_shard and master_create_empty_shard UDFs, please visit the User Defined Functions Reference section of the documentation.
The example above uses master_create_empty_shard to create a new shard every time new data is ingested, which allows many files to be ingested simultaneously, but may cause issues if queries end up involving thousands of shards.
One way to solve this problem is to define a function that selects either a new or existing shard:
SELECT master_append_table_to_shard(choose_shard('events'), 'stage_1', 'master-node', 5432);
An example of a shard selection function is given below. It appends to a shard until its size is greater than 1GB and then creates a new one, which has the drawback of only allowing one append at a time, but the advantage of bounding shard sizes.
CREATE OR REPLACE FUNCTION choose_shard(table_id regclass) RETURNS bigint AS $$ DECLARE shard_id bigint; BEGIN SELECT shardid INTO shard_id FROM pg_dist_shard JOIN pg_dist_shard_placement USING (shardid) WHERE logicalrelid = table_id AND shardlength < 1024*1024*1024; IF shard_id IS NULL THEN /* no shard smaller than 1GB, create a new one */ SELECT master_create_empty_shard(table_id::text) INTO shard_id; END IF; RETURN shard_id; END; $$ LANGUAGE plpgsql;
It may also be useful to create a sequence to generate a unique name for the staging table. This way each ingestion can be handled independently.
-- Create stage table name sequence CREATE SEQUENCE stage_id_sequence; -- Generate a stage table name SELECT 'stage_'||nextval('stage_id_sequence');
Worker Node Bulk Ingestion (100k/s-1M/s)¶
For very high data ingestion rates, data can be staged via the workers. This method scales out horizontally and provides the highest ingestion rates, but is more complex to setup. Hence, we recommend trying this method only if your data ingestion rates cannot be addressed by the previously described methods.
A relatively simple way to ingest data files directly into new shards in the distributed table is to use csql, a database client that comes with Citus. csql is the same as psql, but with an additional STAGE command that copies data directly from the client to the workers into a new shard. STAGE can break up files larger than the configured citus.shard_max_size into multiple shards. The main drawbacks of STAGE are that it requires going through the command-line, only ingests files, and always creates one or more new shards.
csql -h master-node -c "\\STAGE events FROM 'data.csv' WITH CSV"
An alternative to using STAGE is to create a staging table and use standard SQL clients to append it to the distributed table, which is similar to staging data via the master. An example of staging a file via a worker using psql is as follows:
stage_table=$(psql -tA -h worker-node-1 -c "SELECT 'stage_'||nextval('stage_id_sequence')") psql -h worker-node-1 -c "CREATE TABLE $stage_table (time timestamp, data jsonb)" psql -h worker-node-1 -c "\\COPY $stage_table FROM 'data.csv' WITH CSV" psql -h master-node -c "SELECT master_append_table_to_shard(choose_shard('events'), '$stage_table', 'worker-node-1', 5432)" psql -h worker-node-1 -c "DROP TABLE $stage_table"
The example above again uses a choose_shard function to select the shard to which to append. To ensure parallel data ingestion, this function should balance across many different shards.
An example choose_shard function belows randomly picks one of the 20 smallest shards or creates a new one if there are less than 20 under 1GB. This allows 20 concurrent appends, which allows data ingestion of up to 1 million rows/s (depending on indexes, size, capacity).
/* Choose a shard to which to append */ CREATE OR REPLACE FUNCTION choose_shard(table_id regclass) RETURNS bigint LANGUAGE plpgsql AS $function$ DECLARE shard_id bigint; num_small_shards int; BEGIN SELECT shardid, count(*) OVER () INTO shard_id, num_small_shards FROM pg_dist_shard JOIN pg_dist_shard_placement USING (shardid) WHERE logicalrelid = table_id AND shardlength < 1024*1024*1024 GROUP BY shardid ORDER BY RANDOM() ASC; IF num_small_shards IS NULL OR num_small_shards < 20 THEN SELECT master_create_empty_shard(table_id::text) INTO shard_id; END IF; RETURN shard_id; END; $function$;
A drawback of this approach is that shards may span longer time periods, which means that queries for a specific time period may involve shards that contain a lot of data outside of that period.
In addition to copying into temporary staging tables, it is also possible to set up tables on the workers which can continuously take INSERTs. In that case, the data has to be periodically moved into a staging table and then appended, but this requires more advanced scripting.
Pre-processing Data in Citus¶
The format in which raw data is delivered often differs from the schema used in the database. For example, the raw data may be in the form of log files in which every line is a JSON object, while in the database table it is more efficient to store common values in separate columns. Moreover, a distributed table should always have a distribution column. Fortunately, PostgreSQL is a very powerful data processing tool. You can apply arbitrary pre-processing using SQL before putting the results into a staging table.
For example, assume we have the following table schema and want to load the compressed JSON logs from githubarchive.org:
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 ); SELECT master_create_distributed_table('github_events', 'created_at', 'append');
To load the data, we can download the data, decompress it, filter out unsupported rows, and extract the fields in which we are interested into a staging table using 3 commands:
CREATE TEMPORARY TABLE prepare_1 (data jsonb); /* Load a file directly from Github archive and filter out rows with unescaped 0-bytes */ COPY prepare_1 FROM PROGRAM 'curl -s http://data.githubarchive.org/2016-01-01-15.json.gz | zcat | grep -v "\\u0000"' CSV QUOTE e'\x01' DELIMITER e'\x02'; /* Prepare a staging table */ CREATE UNLOGGED TABLE stage_1 AS SELECT (data->>'id')::bigint event_id, (data->>'type') event_type, (data->>'public')::boolean event_public, (data->'repo'->>'id')::bigint repo_id, (data->'payload') payload, (data->'actor') actor, (data->'org') org, (data->>'created_at')::timestamp created_at FROM prepare_1;
You can then use the master_append_table_to_shard function to append this staging table to the distributed table.
This approach works especially well when staging data via the workers, since the pre-processing itself can be scaled out by running it on many workers in parallel for different chunks of input data.