PonyORM
python-mysql-replication
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PonyORM | python-mysql-replication | |
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2 | 5 | |
3,516 | 2,254 | |
1.1% | - | |
5.8 | 9.2 | |
about 1 month ago | 25 days ago | |
Python | Python | |
Apache License 2.0 | - |
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PonyORM
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This Week in Python
pony – Pony Object Relational Mapper
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Show HN: Query any kind of data with SQL powered by Python
See also:
https://github.com/ponyorm/pony/
https://macropy3.readthedocs.io/en/latest/pinq.html#pinq
python-mysql-replication
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Is anyone using PyPy for real work?
I'm maintaining an internal change-data-capture application that uses a python library to decode mysql binlog and store the change records as json in the data lake (like Debezium). For our most busiest databases a single Cpython process couldn't process the amount of incoming changes in real time (thousands of events per second). It's not something that can be easily parallelized, as the bulk of the work is happening in the binlog decoding library (https://github.com/julien-duponchelle/python-mysql-replicati...).
So we've made it configurable to run some instances with Pypy - which was able to work through the data in realtime, i.e. without generating a lag in the data stream. The downside of using pypy was increased memory usage (4-8x) - which isn't really a problem. An actually problem that I didn't really track down was that the test suite (running pytest) was taking 2-3 times longer with Pypy than with CPython.
A few months ago I upgraded the system to run with CPython 3.11 and the performance improvements of 10-20% that come with that version now actually allowed us to drop Pypy and only run CPython. Which is more convenient and makes the deployment and configuration less complex.
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Why Binlog size grows drastically when isolation level set to "Repeatable Read" & When isolation level set to "Read Committed" the size of Binlog file reduces ?
doing the using Python, https://github.com/julien-duponchelle/python-mysql-replication, the recommended way of doing this
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How to Use BinLogs to Make an Aurora MySQL Event Stream
The BinLogStreamReader has several inputs that we need to retrieve. First we'll retrieve the cluster's secret with the database host/username/password and then we'll fetch the serverId we stored in S3.
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How is everyone ingesting backend relational data?
From backend relational tables to data warehouses my team has mostly relied on change data capture replication. We use MySQL upstream, and historically used AWS DMS or Attunity Replicate to replicate directly to SQL server. Recently we made the switch to Snowflake, and used mostly AWS DMS to replicate CDC data to S3 (lists individual inserts, updates, deletes), and then from there use snowpipes to copy to snowflake and then a job to merge that data into the target table to get the latest state. In addition we've used this library in production https://github.com/noplay/python-mysql-replication, and still use it today for one high volume, critical data source. Generally we see data go end to end in a matter of minutes, but occasionally there are spikes in latency.
- Robust data transfer mechanism?
What are some alternatives?
SQLAlchemy - The Database Toolkit for Python
AWS Data Wrangler - pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
Peewee - a small, expressive orm -- supports postgresql, mysql, sqlite and cockroachdb
PyMySQL - MySQL client library for Python
tortoise-orm - Familiar asyncio ORM for python, built with relations in mind
sparc-curation - code and files for SPARC curation workflows
Orator - The Orator ORM provides a simple yet beautiful ActiveRecord implementation.
preshed - 💥 Cython hash tables that assume keys are pre-hashed
GINO - GINO Is Not ORM - a Python asyncio ORM on SQLAlchemy core.
mycli - A Terminal Client for MySQL with AutoCompletion and Syntax Highlighting.
pyDAL - A pure Python Database Abstraction Layer
psycopg2cffi - Port to cffi with some speed improvements