Entries tagged with sqlite
I recently wrote about Kyoto Tycoon (KT), a fast key/value database server. KT databases may be ordered (B-Tree / red-black tree) or unordered (hash table), and persistent or stored completely in-memory. Among other things, I'm using KT's hash database as a cache for things like HTML fragments, RSS feed data, etc. KT supports automatic, time-based expiration, so using it as a cache is a natural fit.
Besides using KT as a cache, in the past I have also used Redis and Sqlite. So I've released a small library I'm calling ucache which can be used with these storage backends and has a couple nice features. I will likely flesh it out and add support for additional storages as I find time to work on it.
In this post I'll provide instructions for building a standalone Python
sqlite3-compatible module which is powered by BerkeleyDB. This is possible because BerkeleyDB provides a SQL frontend, which essentially is the SQLite we all know and love with the b-tree code ripped out and replaced with BerkeleyDB.
The upcoming SQLite 3.25.0 release adds support for one of my favorite SQL language features: window functions. Over the past few years SQLite has released many changes to improve the efficiency of the query planner and virtual machine, plus many extension modules which can provide additional functionality. For example:
- Window function support,
which are available in
trunkand will be included in the next release (3.25.0).
- Postgresql-style UPSERT support, which was released on 2018-06-04 (3.24.0).
- FTS5 full-text search extension, which improves on the previous full-text search modules (3.9.0).
- json1 extension, which brings support for JSON to SQLite (3.9.0).
- lsm1 extension and virtual table, while not officially released, is included in the source.
- Eponymous-only virtual tables, or table-valued functions (3.9.0).
- csv virtual table for browsing CSVs directly with SQLite (3.14.0).
Unfortunately, many distributions and operating systems include very old versions of SQLite. When they do include a newer release, typically many of these additional modules are not compiled and are therefore unavailable for use in your Python applications.
In this post I'll walk through obtaining the latest version of SQLite's source code and how to compile it so it includes these exciting features. We'll be doing all of this with the goal of being able to use these features in our Python applications, so we'll also be discussing how to integrate Python with our custom SQLite library.
The Python standard library sqlite3 driver comes with a barely-documented hook for implementing basic authorization for SQLite databases. Using this hook, it is possible to register a callback that signals, via a return value, what data can be accessed by a connection.
SQLite databases are embedded in the same process as your application, so there is no master server process to act as a gatekeeper for the data stored in your database. Additionally, SQLite database files are readable by anyone with access to the database file itself (unless you are using an encryption library like sqlcipher or sqleet). Restricting access to a SQLite database, once a connection has been opened, is only possible through the use of an authorizer callback.
SQLite provides very granular settings for controlling access, along with two failure modes. Taken together, I think you'll be impressed by the degree of control that is possible.
Several months ago I was delighted to see a new extension appear in the SQLite source tree. The lsm1 extension is based on the LSM key/value database developed as an experimental storage engine for the now-defunct SQLite4 project. Since development has stopped on SQLite4 for the forseeable future, I was happy to see this technology being folded into SQLite3 and was curious to see what the SQLite developers had in mind for this library.
The SQLite4 LSM captured my interest several years ago as it seemed like a viable alternative to some of the other embedded key/value databases floating around (LevelDB, BerkeleyDB, etc), and I went so far as to write a set of Python bindings for the library. As a storage engine, it seems to offer stable performance, with fast reads of key ranges and fast-ish writes, though random reads may be slower than the usual SQLite3 btree. Like SQLite3, the LSM database supports a single-writer/multiple-reader transactional concurrency model, as well as nested transaction support.
The LSM implementation in SQLite3 is essentially the same as that in SQLite4, plus some additional bugfixes and performance improvements. Crucially, the SQLite3 implementation comes with a standalone extension that exposes the storage engine as a virtual table. The rest of this post will deal with the virtual table, its implementation, and how to use it.
In this post I'd like to share with you some techniques for effectively working with SQLite using Python. SQLite is a capable library, providing an in-process relational database for efficient storage of small-to-medium-sized data sets. It supports most of the common features of SQL with few exceptions. Best of all, most Python users do not need to install anything to get started working with SQLite, as the standard library in most distributions ships with the sqlite3 module.
SQLite's write lock and pysqlite's clunky transaction state-machine are a toxic combination for multi-threaded applications. Unless you are very diligent about keeping your write transactions as short as possible, you can easily wind up with one thread accidentally holding a write transaction open for an unnecessarily long time. Threads that are waiting to write will then have a much greater likelihood of timing out while waiting for the lock, giving the illusion of poor performance.
In this post I'd like to share a very effective technique for performing writes to a SQLite database from multiple threads.
If you haven't heard, SQLite is an amazing database capable of doing real work in real production environments. In this post, I'll outline 5 reasons why I think you should use SQLite in 2016.
One of the benefits of running an embedded database like SQLite is that you can configure SQLite to call into your application's code. SQLite provides APIs that allow you to create your own scalar functions, aggregate functions, collations, and even your own virtual tables. In this post I'll describe how I used the virtual table APIs to expose a nice API for creating table-valued (or, multi-value) functions in Python. The project is called
sqlite-vtfunc and is hosted on GitHub. If you use Peewee, an equivalent implementation is included in the Peewee SQLite extensions.
Back in September, word started getting around trendy programming circles about a new file that had appeared in the SQLite fossil repo named json1.c. I originally wrote up a post that contained some gross hacks in order to get pysqlite to compile and work with the new
json1 extension. With the release of SQLite 3.9.0, those hacks are no longer necessary.
SQLite 3.9.0 is a fantastic release. In addition to the much anticipated
json1 extension, there is a new version of the full-text search extension called
fts5 improves performance of complex search queries and provides an out-of-the-box BM25 ranking implementation. You can also boost the significance of particular fields in the ranking. I suggest you check out the release notes for the full list of enhancements
This post will describe how to compile SQLite with support for
fts5. We'll use the new SQLite library to compile a python driver so we can use the new features from python. Because I really like
apsw, I've included instructions for building both of them. Finally, we'll use peewee ORM to run queries using the