(sqlite)=

SQLite

SQLite is a fast and lightweight SQL database engine that stores data either in memory or in a single file on disk.
Use Cases
Despite its simplicity, SQLite is a powerful tool. For example, it‘s the primary storage system for a number of common applications including Firefox, Chrome, and many components of both Android and iOS. It’s well suited for caching, and requires no extra configuration or dependencies, which is why it's used as the default backend for requests-cache.
Usage Example
SQLite will be used as the default backend without providing any arguments. If you want to pass extra connection options or just want to be explicit, initialize your session with a {py:class}.SQLiteCache instance:
>>> from requests_cache import CachedSession, SQLiteCache
>>> backend = SQLiteCache()
>>> session = CachedSession(backend=backend)

Or initialize it by alias:
>>> session = CachedSession(backend='sqlite')

Connection Options
This backend accepts any keyword arguments for {py:func}sqlite3.connect:
>>> backend = SQLiteCache(timeout=30)

Cache Files
  • By default, a file named http_cache.sqlite will be created in the current working directory
  • You can specify a different cache filename using the first positional argument to {py:class}.SQLiteCache
  • If you specify a name without an extension, the default extension .sqlite will be used
  • See {ref}files for general info on specifying cache paths
Example
>>> backend = SQLiteCache('cache/http_cache.sqlite')

In-Memory Caching
SQLite also supports in-memory databases. You can enable this (in “shared” memory mode) with the use_memory option:
>>> backend = SQLiteCache(use_memory=True)

Or specify a memory URI with additional options:
>>> backend = SQLiteCache(':file:memdb1?mode=memory')

Or just :memory:, if you are only using the cache from a single thread:
>>> backend = SQLiteCache(':memory:')

Performance
When working with average-sized HTTP responses (< 1MB) and using a modern SSD for file storage, you can expect speeds of around:
  • Write: 2-8ms
  • Read: 0.2-0.6ms
Of course, this will vary based on hardware specs, response size, and other factors.
The fast_save option can be used to increase cache write performance, but with the possibility of data loss. See pragma: synchronous <https://www.sqlite.org/pragma.html#pragma_synchronous>_ for details.
>>> backend = SQLiteCache(fast_save=True)

Concurrency
SQLite supports concurrent access, so it is safe to use from a multi-threaded and/or multi-process application. It supports unlimited concurrent reads. Writes, however, are queued and run in serial, so if you need to make large volumes of concurrent requests, you may want to consider a different backend that's specifically made for that kind of workload, like {py:class}.RedisCache.
One option to consider is Write Ahead Logging <https://sqlite.org/wal.html>_. This comes with a number of tradeoffs, but most notably it allows read operations to not block writes. This can be enabled with the wal option:
>>> backend = SQLiteCache(wal=True)

Hosting Services and Filesystem Compatibility
There are some caveats to using SQLite with some hosting services, based on what kind of storage is available:
  • NFS:
    • SQLite may be used on a NFS, but is usually only safe to use from a single process at a time. See the SQLite FAQ for details.
    • PythonAnywhere is one example of a host that uses NFS-backed storage. Using SQLite from a multiprocess application will likely result in sqlite3.OperationalError: database is locked.
  • Ephemeral storage: