Meaning a single page load will open and close a few Postgres connections. Without a connection pool, each query opens and closes its own connection, Each of the routes sends between 4 and 10 queries to Postgres. Reports and three (3) that are slower to build. The application displays tabular, aggregated and/or trend data to end users.įive (5) common routes (reports pages) were chosen to test with two fast The app being tested is an internal reporting application built using Flask. The connection between webapp an database is via local WiFi. On a Raspberry Pi 4 with Postgres data on an external USB3 SSD drive. The application is connected to Postgres 12 pip install psycopg_poolįor this round of testing I am using my laptop with 6-cores/12-threads andĦ4 GB RAM to run the webapp and simulated load. The psycopg3 connection pool is implemented in a dedicated psycopg_pool Here are one two three four sources on the topic. I am not directly looking at the impact on Postgres in this post. The remainder of this post illustrates how this impacts performance, Page load and can be a source of significant portion of the total overhead Many modern webapps run multiple queries for every From the application side this has the importantĬharacteristic of removing the overhead related to establishing connectionsįrom queries. With the new psycopg_pool package (remember, no "3"!) it is easy toĮstablish an efficient in-app connection pool. This post covers some of the shortcomings in psycopg2's implementation addressed by psycopg3. In psycopg3 is a feature that caught my attention a few The class_row implementation looks nifty though I haven't had time to try it out. It also covers advanced options such as class_row. The psycopg3 documentation on row factories explains this implementation in more detail. import psycopgĬur = conn.cursor(row_factory=_row) Outside of these basic name changes, no other application code required changing. Has changed to row_factory and has a new approach to returning data When establishing the cursor ( cur), cursor_factory The next example shows how psycopg3 is imported, simply referenced as psycopg. The following example shows a simplified example of how my projects haveĬur = conn.cursor(cursor_factory=) The friction has been very low and confined to edge case uses. Two projects to psycopg3 and started migrating two more projects. Luckily for us, psycopg3 got this part right! In the past week I fully migrated Of error after error throughout your application. It is frustrating when a "simple upgrade" turns into a cascade MigrationĮasy migration is an important feature to encourage developers to upgrade. The majority of this post is dedicated to examining pyscopg3's connection poolĪnd the difference this feature can make to your application's performance. This post highlights two pieces of happy news with psycopg3:Īs the first section shows, migration from psycopg2 to psycopg3 is quite easy. In 2020 Daniele started working full-time on creating psycopg3, If you use Postgres and Python together you are almost certainlyĭaniele Varrazzo has been the maintainer of the psycopg project for By Ryan Lambert - Published September 07, 2021
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