COPY will greatly improve performance for your pipelines, even if the data is from Python. Using pickles could also be of value here because they simplify data loads for automated testing. This is the one of the most efficient methods of loading data but using python data structures can be more straightforward. Our data is the default form for postgresql csv copy, tab delimited: The copy specifies columns in the order they are loaded from the disk. SQLAlchemy’s engine cannot perform a low-level copy operation so we have to pull a cursor from the raw connection object. Feel free to increase this for larger data loads. We will use sample csv data from github with 100 fictitious accounts and associated addresses and copy data directly in 4KB block sizes. COPY from postgres to another server, table, or database.COPY from postgres into Python or to file.This version allows you to write SQL to perform these copy operations: Version 2.0 of psycopg’s copy only supported a fraction on the features in PostgreSQL’s copy. It could also be useful to examine the dbapi_connection as a quick API reference or for hacking purposes. The _ConnectionFairy class serves as a proxy to the underlying DBAPI, giving us access to those properties and methods. The connection will not actually be closed but released back to the pool. This should call the close method of the raw connection, cleaning it up nicely. If we want to use psycopg more we have to access the raw connection but will not have context management. The engine’s connection will easily support raw SQL and ORM queries together but will not expose many of the driver’s features. The data model here is not perfectly 3NF for purposes of demonstration. This means we can swap engines and run queries in async or sync mode. Notice how the engine is decoupled from the tables in the new version. In addition let’s set up an async engine using a sqlalchemy extension. The connection string in alchemy has to start with “postgresql+psycopg://” for version 3, but If you need to use version 2 then stick with “postgresql://”. SQLAlchemy is absolutely a great choice for pure SQL or ORM development because it provides its own connection pool through the engine. Psycopg3 has great pooling but it requires custom recycling of dead connections in a separate thread. We will run PostgreSQL 14.1 from the docker alpine builds in docker-compose. SQLAlchemy 2.0 is installed from from github because it’s in pre-beta. Learn how to access new DBAPI from SQLAlchemy and examine Prepared Statements.Perform low-level SQL queries while taking advantage of Connection Pooling.Perform sync and async queries with the ORM.
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