cstore_fdw
pg_squeeze
Our great sponsors
cstore_fdw | pg_squeeze | |
---|---|---|
6 | 2 | |
1,738 | 401 | |
0.4% | 2.5% | |
2.6 | 8.3 | |
about 3 years ago | about 2 months ago | |
C | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cstore_fdw
-
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.
-
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
-
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
-
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, ... ),
-
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
-
Creating a simple data pipeline
The citus extension for postgresql. https://github.com/citusdata/cstore_fdw
pg_squeeze
- Pg_squeeze: An extension to fix table bloat
-
PlanetScale Is Now GA
> I am estimating that your database space isn't MySQL, which is just fine of course.
You are absolutely right :) My background is strongly on Postgres, you can see from my profile more information if you want to.
So yes, I apologize if some of my questions are not applying or become to obvious for cases that are MySQL-based. But for the most part, I believe principles of operation are the same.
> [other comments]
As mentioned, thank you very much for the detailed information. This completes the picture that I was looking for. I will definitely go in more detail for some of the links provided.
This principle of operation is not too different from something I proposed to a Postgres project some time ago (https://github.com/cybertec-postgresql/pg_squeeze/issues/18). This tool indeed is conceptually pretty similar. It's a shame that supporting schema changes is not part of their focus at this point. It wouldn't do throttling either, but it shouldn't be a difficult feature to add, I guess.
For other users here that may be interested in the Postgres world, there are two tools that perform similar operation (creating a shadow table and filling it in the background), but are both focused on rewriting the table to avoid bloat, rather than for doing a schema migration:
* pg_repack (https://reorg.github.io/pg_repack/): the most used one, relies on triggers
What are some alternatives?
ZLib - A massively spiffy yet delicately unobtrusive compression library.
gh-ost - GitHub's Online Schema-migration Tool for MySQL
odbc2parquet - A command line tool to query an ODBC data source and write the result into a parquet file.
vitess - Vitess is a database clustering system for horizontal scaling of MySQL.
zstd - Zstandard - Fast real-time compression algorithm
tengo - Go La Tengo: a MySQL automation library
cute_headers - Collection of cross-platform one-file C/C++ libraries with no dependencies, primarily used for games
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
parquet_fdw - Parquet foreign data wrapper for PostgreSQL
bzip3 - A better and stronger spiritual successor to BZip2.
duckdb_fdw - DuckDB Foreign Data Wrapper for PostgreSQL
Bailo - Managing the lifecycle of machine learning to support scalability, impact, collaboration, compliance and sharing.