python-mysql-replication
Numba
python-mysql-replication | Numba | |
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5 | 124 | |
2,255 | 9,452 | |
- | 1.1% | |
9.1 | 9.9 | |
about 1 month ago | 7 days ago | |
Python | Python | |
- | BSD 3-clause "New" or "Revised" License |
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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?
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
[0]: https://numba.pydata.org/
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Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
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This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
- Numba Supports Python 3.11
What are some alternatives?
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).
NetworkX - Network Analysis in Python
PyMySQL - MySQL client library for Python
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
PonyORM - Pony Object Relational Mapper
Dask - Parallel computing with task scheduling
sparc-curation - code and files for SPARC curation workflows
cupy - NumPy & SciPy for GPU
preshed - 💥 Cython hash tables that assume keys are pre-hashed
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
mycli - A Terminal Client for MySQL with AutoCompletion and Syntax Highlighting.
SymPy - A computer algebra system written in pure Python