Skip to content
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@ The question "Does it Scale?" has become somewhat of a meme in software engineer

At PostgresML, we are very concerned with scale. Our engineering background took us through scaling PostgreSQL to 100 TB+, so we're certain that it scales, but could we scale machine learning alongside it?

In this post, we'll discuss how we horizontally scaled PostgresML to achieve more than **1 million XGBoost predictions per second** on commodity hardware.
In this post, we'll discuss how we horizontally scale PostgresML to achieve more than **1 million XGBoost predictions per second** on commodity hardware.

If you missed our previous post and are wondering why someone would combine machine learning and Postgres, take a look at our PostgresML vs. Python [benchmark](/blog/postgresml-is-8x-faster-than-python-http-microservices).

Expand Down Expand Up @@ -96,7 +96,7 @@ PostgresML bypasses that limitation because of how Postgres itself handles concu
_PostgresML concurrency_
</center>

PostgreSQL uses the fork/multiprocessing architecture to serve multiple clients concurrenctly: each new client connection becomes an independent OS process. During connection startup, PostgresML loads all models inside the process' memory space. This means that each connection has its own copy of the XGBoost model and PostgresML ends up serving multiple XGBoost predictions at the same time without any lock contention.
PostgreSQL uses the fork/multiprocessing architecture to serve multiple clients concurrently: each new client connection becomes an independent OS process. During connection startup, PostgresML loads all models inside the process' memory space. This means that each connection has its own copy of the XGBoost model and PostgresML ends up serving multiple XGBoost predictions at the same time without any lock contention.

## Results

Expand Down
pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy