python-mysql-replication VS gevent

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.

doing the using Python, https://github.com/julien-duponchelle/python-mysql-replication, the recommended way of doing this

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.

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.

A sub-question for the folks here: is anyone using the combination of gevent and PyPy for a production application? Or, more generally, other libraries that do deep monkey-patching across the Python standard library?

Things like https://github.com/gevent/gevent/issues/676 and the fix at https://github.com/gevent/gevent/commit/f466ec51ea74755c5bee... indicate to me that there are subtleties on how PyPy's memory management interacts with low-level tweaks like gevent that have relied on often-implicit historical assumptions about memory management timing.

Not sure if this is limited to gevent, either - other libraries like Sentry, NewRelic, and OpenTelemetry also have low-level monkey-patched hooks, and it's unclear whether they're low-level enough that they might run into similar issues.

For a stack without any monkey-patching I'd be overjoyed to use PyPy - but between gevent and these monitoring tools, practically every project needs at least some monkey-patching, and I think that there's a lack of clarity on how battle-tested PyPy is with tools like these.

I'm not sure how much it replicates the CSP model, but the closest thing I've found to Go-style concurrency in Python is gevent: https://github.com/gevent/gevent

I personally still prefer to use it in all my projects.

Greenlet seems to have some troubles compiling with Python 3.9. https://github.com/gevent/gevent/issues/1627