PSYCOPG3 DRIVER

In both cases we're creating a factorial of a certain number and then we're executing an INSERT statement into the database.īefore we jump into how that compares performance-wise, a quick note on the background of how this works. In Denis’ example program, there are two modes, there's the pipeline mode and the regular mode. You want to have that step-by-step process that pipeline mode enables. But, sometimes you have data that you're calculating as you're inserting, and so having to calculate everything upfront would be quite expensive. If you had all the data already available, you should just do a multi-row insert, that's going to be much more efficient. Looking at another example in the blog post: for each value that gets inserted we take the factorial. The idea behind pipeline mode is that you have independent statements. You can't say: send this query, and then when I have the result, do this other thing. Pipeline mode isn't limited to doing writes, you can also use it with reads, but oftentimes writes are going to be more independent. The example we see in Denis’ blog post is using a table and using inserts. Then things that are done inside this block will run in pipeline mode instead of running as regular queries. In psycopg, with these recent changes, we simply have to call 'connection.pipeline'. The main benefit of pipeline mode is that you're saving on network latency.

If you have a really fast query, that query itself might execute under one millisecond, but the network latency could be as much as two or three milliseconds! The main benefit of this is that you're saving on network latency, which if you're in the cloud and maybe you're talking across availability zones, network latency quickly adds up. If you are not familiar with pipeline mode in Postgres, we talked about this on Episode 32 of 5mins of Postgres a couple of weeks ago, and, really, the main difference between using pipeline mode and regular query execution, is that pipeline mode will send multiple queries to the server without having to wait for results. In this blog post, he describes why pipeline mode matters, the performance improvements it brings, and how you can use it in Python. Pipeline mode is a contribution to psycopg by Denis Laxalde from the Dalibo team. Pipeline mode in psycopg 3.1 and Postgres But I want to focus today on pipeline mode. There are a couple of interesting announcements in here, including things like supporting CockroachDB and better cursor support. They've recently released the psycopg 3.1 release.

PSYCOPG3 DRIVER

Let's dive in! psycopg 3.1 for Python and Postgresįirst of all, I want to give a quick shout-out to Daniele Varrazzo who has been maintaining pyscopg and has started the new psycopg3 project that aims to bring a modern Postgres driver to Python. What we have discussed in this episode of 5mins of Postgres Getting 2x - 10x better Postgres performance with pipeline mode

Psycopg3