gem-hunterColumnar storage extension for Postgres built as a foreign data wrapper. Check out https://github.com/citusdata/citus for a modernized columnar storage implementation built as a table access method.
Columnar storage is now part of the Citus extension, which uses the table access method API to give a much more native experience. It also supports streaming replication, archival, rollback, and simplifies pg_upgrade. You can use Citus just for columnar storage on a single PostgreSQL server, or combine it with distributed tables to parallelize queries locally or across a cluster of PostgreSQL servers.
Migration is simple, and you'll typically see improved compression thanks to zstd:
-- After adding adding shared_preload_libraries = 'citus' -- to postgresql.conf and restarting: CREATE EXTENSION IF NOT EXISTS citus; -- Create a table using the columnar access method, with the same columns -- as an existing cstore_fdw table CREATE TABLE customer_reviews_am ( LIKE customer_reviews_fdw INCLUDING ALL ) USING columnar; -- Copy data from an old cstore_fdw table to an access method table INSERT INTO customer_reviews_am SELECT * FROM customer_reviews_fdw; -- cstore_fdw data size SELECT pg_size_pretty(cstore_table_size('customer_reviews_fdw')); ┌────────────────┐ │ pg_size_pretty │ ├────────────────┤ │ 100 MB │ └────────────────┘ -- Citus Columnar data size SELECT pg_size_pretty(pg_table_size('customer_reviews_am')); ┌────────────────┐ │ pg_size_pretty │ ├────────────────┤ │ 64 MB │ └────────────────┘
Read more about it in the Citus columnar blog post by Jeff Davis.
Cstore_fdw is an open source columnar store extension for PostgreSQL. Columnar stores provide notable benefits for analytics use cases where data is loaded in batches. Cstore_fdw’s columnar nature delivers performance by only reading relevant data from disk, and it may compress data 6x-10x to reduce space requirements for data archival.
Cstore_fdw is developed by Citus Data and can be used in combination with Citus, a postgres extension that intelligently distributes your data and queries across many nodes so your database can scale and your queries are fast. If you have any questions about how Citus can help you scale or how to use Citus in combination with cstore_fdw, please let us know.
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This extension uses a format for its data layout that is inspired by ORC, the Optimized Row Columnar format. Like ORC, the cstore format improves upon RCFile developed at Facebook, and brings the following benefits:
Further, we used the Postgres foreign data wrapper APIs and type representations with this extension. This brings:
cstore_fdw depends on protobuf-c for serializing and deserializing table metadata. So we need to install these packages first:
# Fedora 17+, CentOS, and Amazon Linux
sudo yum install protobuf-c-devel
# Ubuntu 10.4+
sudo apt-get install protobuf-c-compiler
sudo apt-get install libprotobuf-c0-dev
# Ubuntu 18.4+
sudo apt-get install protobuf-c-compiler
sudo apt-get install libprotobuf-c-dev
# Mac OS X
brew install protobuf-c
Note. In CentOS 5, 6, and 7, you may need to install or update EPEL 5, 6, or 7 repositories. See this page for instructions.
Note. In Amazon Linux, the EPEL repository is installed by default, but not enabled. See these instructions for how to enable it.
Once you have protobuf-c installed on your machine, you are ready to build cstore_fdw. For this, you need to include the pg_config directory path in your make command. This path is typically the same as your PostgreSQL installation's bin/ directory path. For example:
PATH=/usr/local/pgsql/bin/:$PATH make
sudo PATH=/usr/local/pgsql/bin/:$PATH make install
Note. cstore_fdw requires PostgreSQL version from 9.3 to 12. It doesn't support earlier versions of PostgreSQL.
Before using cstore_fdw, you need to add it to shared_preload_libraries
in your postgresql.conf and restart Postgres:
shared_preload_libraries = 'cstore_fdw' # (change requires restart)
The following parameters can be set on a cstore foreign table object.
/cstore_fdw/my_table could result in
the files /cstore_fdw/my_table and /cstore_fdw/my_table.footer being used
to manage table data.none and pglz. The default is none.150000. Reducing this decreases the amount memory used for loading data
and querying, but also decreases the performance.10000. cstore_fdw compresses, creates skip indexes, and reads from disk
at the block granularity. Increasing this value helps with compression and results
in fewer reads from disk. However, higher values also reduce the probability of
skipping over unrelated row blocks.To load or append data into a cstore table, you have two options:
COPY command to load or append data from
a file, a program, or STDIN.INSERT INTO cstore_table SELECT ... syntax to load or
append data from another table.You can use the ANALYZE command to collect statistics
about the table. These statistics help the query planner to help determine the
most efficient execution plan for each query.
Note. We currently don't support updating table using DELETE, and UPDATE commands. We also don't support single row inserts.
To update an existing cstore_fdw installation from versions earlier than 1.6 you can take the following steps:
ALTER EXTENSION cstore_fdw UPDATE;As an example, we demonstrate loading and querying data to/from a column store table from scratch here. Let's start with downloading and decompressing the data files.
wget http://examples.citusdata.com/customer_reviews_1998.csv.gz
wget http://examples.citusdata.com/customer_reviews_1999.csv.gz
gzip -d customer_reviews_1998.csv.gz
gzip -d customer_reviews_1999.csv.gz
Then, let's log into Postgres, and run the following commands to create a column store foreign table:
-- load extension first time after install CREATE EXTENSION cstore_fdw; -- create server object CREATE SERVER cstore_server FOREIGN DATA WRAPPER cstore_fdw; -- create foreign table CREATE FOREIGN 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)[] ) SERVER cstore_server OPTIONS(compression 'pglz');
Next, we load data into the table:
\COPY customer_reviews FROM 'customer_reviews_1998.csv' WITH CSV; \COPY customer_reviews FROM 'customer_reviews_1999.csv' WITH CSV;
Note. If you are getting ERROR: cannot copy to foreign table "customer_reviews" when trying to run the COPY commands, double check that you
have added cstore_fdw to shared_preload_libraries in postgresql.conf
and restarted Postgres.
Next, we collect data distribution statistics about the table. This is optional, but usually very helpful:
ANALYZE customer_reviews;
Finally, let's run some example SQL queries on the column store table.
-- Find all reviews a particular customer made on the Dune series in 1998. SELECT customer_id, review_date, review_rating, product_id, product_title FROM customer_reviews WHERE customer_id ='A27T7HVDXA3K2A' AND product_title LIKE '%Dune%' AND review_date >= '1998-01-01' AND review_date <= '1998-12-31'; -- Do we have a correlation between a book's title's length and its review ratings? SELECT width_bucket(length(product_title), 1, 50, 5) title_length_bucket, round(avg(review_rating), 2) AS review_average, count(*) FROM customer_reviews WHERE product_group = 'Book' GROUP BY title_length_bucket ORDER BY title_length_bucket;
The example above illustrated how to load data into a PostgreSQL database running on a single host. However, sometimes your data is too large to analyze effectively on a single host. Citus is a product built by Citus Data that allows you to run a distributed PostgreSQL database to analyze your data using the power of multiple hosts. You can easily install and run other PostgreSQL extensions and foreign data wrappers—including cstore_fdw—alongside Citus.
You can create a cstore_fdw table and distribute it using the
create_distributed_table() UDF just like any other table. You can load data
using the copy command as you would do in single node PostgreSQL.
cstore_fdw partitions each column into multiple blocks. Skip indexes store minimum and maximum values for each of these blocks. While scanning the table, if min/max values of the block contradict the WHERE clause, then the block is completely skipped. This way, the query processes less data and hence finishes faster.
To use skip indexes more efficiently, you should load the data after sorting it on a column that is commonly used in the WHERE clause. This ensures that there is a minimum overlap between blocks and the chance of them being skipped is higher.
In practice, the data generally has an inherent dimension (for example a time field) on which it is naturally sorted. Usually, the queries also have a filter clause on that column (for example you want to query only the last week's data), and hence you don't need to sort the data in such cases.
Before uninstalling the extension, first you need to drop all the cstore tables:
postgres=# DROP FOREIGN TABLE cstore_table_1;
...
postgres=# DROP FOREIGN TABLE cstore_table_n;
Then, you should drop the cstore server and extension:
postgres=# DROP SERVER cstore_server;
postgres=# DROP EXTENSION cstore_fdw;
cstore_fdw automatically creates some directories inside the PostgreSQL's data directory to store its files. To remove them, you can run:
$ rm -rf $PGDATA/cstore_fdw
Then, you should remove cstore_fdw from shared_preload_libraries in
your postgresql.conf:
shared_preload_libraries = '' # (change requires restart)
Finally, to uninstall the extension you can run the following command in the extension's source code directory. This will clean up all the files copied during the installation:
$ sudo PATH=/usr/local/pgsql/bin/:$PATH make uninstall
TRUNCATE TABLEALTER TABLE ADD COLUMN and ALTER TABLE DROP COLUMN.COPY FROM.COPY TO.INSERT INTO cstore_table SELECT ....Copyright (c) Citus Data, Inc.
This module is free software; you can redistribute it and/or modify it under the Apache v2.0 License.
For all types of questions and comments about the wrapper, please contact us at engage @ citusdata.com.