The downside is that the server needs to keep track of the partially So you won't get the whole result set in one chunk. Instance when fetching new records or when moving using scroll()). Send commands to control the state of the cursor on the server (for Their interface is the same, but behind the scene they Transmitted to the client: if the query result is very large but theĬlient only needs the first few records it is possible to transmit Necessarily completely processed: the server might be able to produce
PYTHON POSTGRESQL HOW TO
In this article, we'll be discussing how to access a PostgreSQL database in Python using the psycopg2 driver.
PYTHON POSTGRESQL DRIVERS
There are many Python drivers for PostgreSQL, with 'psycopg' being the most popular one. When a database cursor is created, the query is not With Python, you can easily establish a connection to your PostgreSQL database. PostgreSQL also has its own concept of cursor (sometimes also called This is where Server-side cursors comes into play: Of-course fetchone helped but still we have the whole records in memory. So If your table has 1,000,000 records, this is what's going on in memory: curs.execute -> whole 1,000,000 result set + fetchone -> 1 Python objectĬurs.execute -> whole 1,000,000 result set + fetchall -> 1,000,000 Python objects
PYTHON POSTGRESQL CODE
Q: So how fetchone can help us memory wise ?Ī: It fetches only one record from the stored data and creates a single Python object and hands you in your Python code while fetchall will fetch and create n Python objects from this data and hands it to you all in one chunk. '.Īll the fetch* methods are there for accessing this stored data. It is when you execute a query like curs.execute('SELECT * FROM. The whole records are buffered in the client's memory. With normal cursors (client-side), Psycopg fetches all the records returned by the backend, transferring them to the client process. To make life easier, you can use the following: from functools import partialĭef run_and_iterate(curs, sql, parms=None, chunksize=1000):Ĭhunks_until_empty = iter(partial(fetchmany, chunksize), )įor row in run_and_iterate(cur, 'select * from waffles_table where num_waffles > %s', (10,)):Īs I was reading comments and answers I thought I should clarify something about fetchone and Server-side cursors for future readers. The fetchmany loop is tedious but I think it's the best solution here. Not sure a named cursor is a good fit without having a need to scroll forward/backward interactively? I could be wrong here. This is misleading for the library to say the least, and there should be a blurb in the documentation about this. Without using a named cursor (which would require an open transaction), you have to resort to using limit in the sql with an order-by, then analyzing the results and augmenting the next query with where (ordered_val = %(last_seen_val)s and primary_key > %(last_seen_pk)s OR ordered_val > %(last_seen_val)s) EDIT: using fetchmany (along with fetchone() and fetchall(), even with a row limit (arraysize) will still send the entire resultset, keeping it client-side (stored in the underlying c library, I think libpq) for any additional fetchmany() calls, etc.
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