Django provides an friendly Object Relational Mapping (ORM) framework. In several of my data analysis projects, I used Django ORM to process millions of logcat data generated by hundreds of Android phones. Here are some of the experiences and tips that helps making the processing just a bit faster.


First of all, set DEBUG to False in With DEBUG as True, Django will keep in memory the DB queries it has run so far, which lead to memory leak if you have a large batch of importing work.

Control Transaction Manually

By default, Django will wrap each database operation a separate transaction, and commit them automatically. Accessing database frequently definitely will slow you down, especially when all you want to do is just to insert (a large amount of) data. Django's transaction module provides several functions to let you control when to commit the transaction. My favorite one is to use transaction.commit_on_success to wrap the function that import data for a individual device. An addition benefit is, now you know the data importing for each device either finished completely, or didn't get imported at all. So if something wrong happens during the importing, or you have to stop it in the middle for some reason. Next time when you rerun the importing, you won't get duplicate rows!

Bulk Create Rows

When you have lots of data that you want to import into the database, instead of call each objects save function individually, you can store them in a list and use the object manager's bulk_create function. It'll insert the list of objects into the database "in an efficient manner". Use this technique together with the transaction.commite_on_success mentioned above, the data importing should be fast enough.


Now all the raw data is imported into database, the next thing you want to do is probably run second pass of processing, filtering, or whatever. When the data size is large, it's unlikely that you need to use them again and again. Most of the time, you just want to iterate through each log line, get some statistical information, or some simple computation. So after you construct your (crazy) query set, you want to add an .iterator() function after it, so Django knows you just want to iterate the data once, and will not bother to cache them. Otherwise, Django will cache the query results, and soon you will find your system freezes, and the kernel does nothing but swapping...

Reset Queries And Garbage Collection

Every now and then you can also reset Django queries manually with the reset_queries function, and trigger garbage collection using gc.collect(). They'll help you to further reduce memory usage.


Database access optimization