cstore_fdw
citus
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cstore_fdw | citus | |
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6 | 61 | |
1,738 | 9,840 | |
0.4% | 3.6% | |
2.6 | 9.4 | |
about 3 years ago | 6 days ago | |
C | C | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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cstore_fdw
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Moving a Billion Postgres Rows on a $100 Budget
Columnar store PostgreSQL extension exists, here are two but I think I’m missing at least another one:
https://github.com/citusdata/cstore_fdw
https://github.com/hydradatabase/hydra
You can also connect other stores using the foreign data wrappers, like parquet files stored on an object store, duckdb, clickhouse… though the joins aren’t optimised as PostgreSQL would do full scan on the external table when joining.
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Anything can be a message queue if you use it wrongly enough
I'm definitely not from Citus data -- just a pg zealot fighting the culture war.
If you want to reach people who can actually help, you probably want to check this link:
https://github.com/citusdata/cstore_fdw/issues
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Pg_squeeze: An extension to fix table bloat
That appears to be the case:
https://github.com/citusdata/cstore_fdw
>Important notice: Columnar storage is now part of Citus
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Ingesting an S3 file into an RDS PostgreSQL table
either we go for RDS, but we stick to the AWS handpicked extensions (exit timescale, citus or their columnar storage, ... ),
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Postgres and Parquet in the Data Lke
Re: performance overhead, with FDWs we have to re-munge the data into PostgreSQL's internal row-oriented TupleSlot format again. Postgres also doesn't run aggregations that can take advantage of the columnar format (e.g. CPU vectorization). Citus had some experimental code to get that working [2], but that was before FDWs supported aggregation pushdown. Nowadays it might be possible to basically have an FDW that hooks into the GROUP BY execution and runs a faster version of the aggregation that's optimized for columnar storage. We have a blog post series [3] about how we added agg pushdown support to Multicorn -- similar idea.
There's also DuckDB which obliterates both of these options when it comes to performance. In my (again limited, not very scientific) benchmarking of on a customer's 3M row table [4] (278MB in cstore_fdw, 140MB in Parquet), I see a 10-20x (1/2s -> 0.1/0.2s) speedup on some basic aggregation queries when querying a Parquet file with DuckDB as opposed to using cstore_fdw/parquet_fdw.
I think the dream is being able to use DuckDB from within a FDW as an OLAP query engine for PostgreSQL. duckdb_fdw [5] exists, but it basically took sqlite_fdw and connected it to DuckDB's SQLite interface, which means that a lot of operations get lost in translation and aren't pushed down to DuckDB, so it's not much better than plain parquet_fdw.
This comment is already getting too long, but FDWs can indeed participate in partitions! There's this blog post that I keep meaning to implement where the setup is, a "coordinator" PG instance has a partitioned table, where each partition is a postgres_fdw foreign table that proxies to a "data" PG instance. The "coordinator" node doesn't store any data and only gathers execution results from the "data" nodes. In the article, the "data" nodes store plain old PG tables, but I don't think there's anything preventing them from being parquet_fdw/cstore_fdw tables instead.
[0] https://github.com/citusdata/cstore_fdw
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Creating a simple data pipeline
The citus extension for postgresql. https://github.com/citusdata/cstore_fdw
citus
- SPQR 1.3.0: a production-ready system for horizontal scaling of PostgreSQL
- Citus: PostgreSQL extension that transforms Postgres into a distributed database
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Figma's Databases team lived to tell the scale
I see they don't mention Citus (https://github.com/citusdata/citus), which is already a fairly mature native Postgres extension. From the details given in the article, in sounds like they just reimplemented it.
I wonder if they were unaware of it or disregarded it for a reason —I currently am in a similar situation as the one described in the blog, trying to shard a massive Postgres DB.
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PostgreSQL Is Enough
It is possible, if you pay for it. You can do Multi-AZ Clustered Instances in RDS, where you get the benefits of Multi-AZ failover with traffic sharing.
If you can run your own infra – at least on an EC2 level – you can do things like Citus [0] for Postgres, which is about as close to "just add database nodes" as you'll get.
[0]: https://www.citusdata.com/
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Vitess 18
So while searching for something like this for postgres I came across citus. Any one know how that stacks up?
https://github.com/citusdata/citus
- In-Depth Guide: Citus Technical Readme
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Revolutionizing Database Scaling with CitusDB
References: CitusDB
- Squeeze the hell out of the system you have
- Show HN: Hydra 1.0 – open-source column-oriented Postgres
- Schema-based sharding comes to PostgreSQL with Citus
What are some alternatives?
ZLib - A massively spiffy yet delicately unobtrusive compression library.
Greenplum - Greenplum Database - Massively Parallel PostgreSQL for Analytics. An open-source massively parallel data platform for analytics, machine learning and AI.
odbc2parquet - A command line tool to query an ODBC data source and write the result into a parquet file.
yugabyte-db - YugabyteDB - the cloud native distributed SQL database for mission-critical applications.
zstd - Zstandard - Fast real-time compression algorithm
vitess - Vitess is a database clustering system for horizontal scaling of MySQL.
cute_headers - Collection of cross-platform one-file C/C++ libraries with no dependencies, primarily used for games
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
parquet_fdw - Parquet foreign data wrapper for PostgreSQL
stolon - PostgreSQL cloud native High Availability and more.