![]() Typical runtime errors (such as Inde圎rror for tuple or KeyError forĭict) that a type checker cannot verify on the other hand typicalĪttributeError occurring with strong types can be checked. This is safer because, as we’re passing those strongly typed values, we avoid the Weather type), not tuple (or dict) of anonymous values ( int, This is safer because data comes from the database in the form of strong types Verified by a type checker such as mypy). Value, which matches what get_weather_reports() declares (and would be Returning rows of type Weather so fetchall() returns a list Namely, here, the cursor has a row factory The value obtained from this cursor’s fetchall() call is inferred fetchall ()īy passing a typed row factory to the cursor being used to fetch queries, execute ( "SELECT * FROM weather" ) return cur. The project is currently under magna carta. So let’s get back to our previous example to add type annotations:ĭef get_weather_reports ( conn : Connection ) -> list : with conn. The new COPY support is one of several new features that are being designed and implemented in psycopg3. Validation capabilities (for instance, libraries such as pydantic in the As such, one would typically use model types with strong In this context, the rowįactory feature of psycopg helps a lot because it brings strong typing into the However, leveraging this at the I/O boundary might be harder because this Provides additional safety guarantees without impacting runtime performances. Static typing, with type checkers such as mypy, This is typically where an ORM also helps.Īnother great benefit comes when combining row factories with Python static One would take great care of writing correct SQL queries and theirĬorresponding row factories, carefully test them, and the rest of applicationĬode would work nicely without being concerned about data coming from theĭatabase. Thus the time spent in this I/O boundary is reduced. Section, we perform transformation from “raw” Python types to domain-specific The I/O boundary - where one transforms data from/to one type system intoĪnother - is typically quite “unsafe” and requires special care, such asīy using row factories when issuing queries from psycopg as shown in previous In the context of databases, or I/O in general, the code involved working at Let’s assume we are working with weather data, collecting date reports for Leveraged to write safer applications, through static typing in particular. In addition, we’ll see how this lightweight feature can be Yet powerful, way to handle domain-specific models without any extraĭependency. ![]() In psycopg 3, row factories provide a simple, ![]() Providing an abstract layer to write queries from model classes and map This is often where ORMs come into play by These domain models are typically defined by application developers in theįorm of data classes in Python, mapped to one or more relations (usually Way to handle domain-specific data types: row factories. This conversion applies fine for built-in types, psycopg also exposes a native As such, one of its main goals is to automatically adapt PostgreSQLĪnd Python types together when issuing queries and retrieving results. Psycopg is a PostgreSQL database adapter for the Python programming
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