duckdb_fdw
connector-x
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duckdb_fdw | connector-x | |
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4 | 11 | |
235 | 1,769 | |
- | 3.6% | |
7.3 | 7.9 | |
2 months ago | 7 days ago | |
PLpgSQL | Rust | |
MIT License | MIT License |
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duckdb_fdw
- Querying Postgres Tables Directly from DuckDB
- Postgres and Parquet in the Data Lke
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DuckDB quacks Arrow: A zero-copy data integration between Arrow and DuckDB
I should also add that there is a duckdb fdw, so you could have DuckDB read from your parquet files and do faster transformations before you pull your data into Postgres!
https://github.com/alitrack/duckdb_fdw
- DuckDB Postgres Foreign Data Wrapper
connector-x
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How moving from Pandas to Polars made me write better code without writing better code
This was originally a blocker, however, we managed to set up a multi-stage Docker build to build from source. Here is the Github issue where we, along with community members, managed to solve it.
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I used multiprocessing and multithreading at the same time to drop the execution time of my code from 155+ seconds to just over 2+ seconds
There's packages like connector-x and polars that do a lot of what you're mentioning out of the box. I used these two to massively speed up an SQLalchemy + Pandas based ETL in the past as well.
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Rust in Data Science?
Thanks for sharing connector-x, I will also start to use it. I wonder if there are a list of tools like that. I know Ruff, Polars, pydantic-core.
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Querying Postgres Tables Directly from DuckDB
I was trying https://github.com/sfu-db/connector-x and hacking around with this https://github.com/spitz-dan-l/postgres-binary-parser but it turned out that a COPY to csv using asyncpg and then converting to parquet was the fastest.
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An alternativt to TradingView ?
if you store the OHLC data in a relational database, use connector-x to load the data into pandas dataframe
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Python and ETL
For SQL reading I'd really recommend connector-x, they do a great job preventing unneeded serialization and don't have to go through python.
- Fastest library to load data from DB to DataFrames
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Waiting for your data loading from database to dataframes?
Indeed, currently we do not support persistent connections among different queries. We target more on the bulk loading scenario where the bottleneck is caused by the data size and the connection construction overhead is negligible. However, one possible solution to the problem is to expose our connection pool object that we use inside Rust to users, so the next call could reuse the same pool. We do not plan for this yet, but happy to see whether this is a common need! Feel free to open an issue in our github repo: https://github.com/sfu-db/connector-x
Feel free to ask any questions here or open an issue in our github repo: https://github.com/sfu-db/connector-x . You can also join our discord community: https://discord.com/invite/xwbkFNk and ask question under connector channel!
- ConnectorX: The fastest tool to load data from databases to dataframes
What are some alternatives?
subzero-starter-kit - Starter Kit and tooling for authoring GraphQL/REST API backends with subZero
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
odbc2parquet - A command line tool to query an ODBC data source and write the result into a parquet file.
Rudderstack - Privacy and Security focused Segment-alternative, in Golang and React
aquameta - Web development platform built entirely in PostgreSQL
lightweight-charts - Performant financial charts built with HTML5 canvas
postgres_vectorization_test - Vectorized executor to speed up PostgreSQL
mmr - Python based algorithmic trading platform for Interactive Brokers
parquet_fdw - Parquet foreign data wrapper for PostgreSQL
postgres-binary-parser - Cython implementation of a parser for PostgreSQL's COPY WITH BINARY format
parquet_s3_fdw - ParquetS3 Foreign Data Wrapper for PostgresSQL
duckdb - DuckDB is an in-process SQL OLAP Database Management System