Entries tagged with sqlite

LSM Key/Value Storage in SQLite3

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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.

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Going Fast with SQLite and Python

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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.

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Multi-threaded SQLite without the OperationalErrors

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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.

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Five reasons you should use SQLite in 2016

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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.

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Updated instructions for compiling BerkeleyDB with SQLite for use with Python

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About three years ago I posted some instructions for building the Python SQLite driver for use with BerkeleyDB. While those instructions still work, they have the unfortunate consequence of stomping on any other SQLite builds you've installed in /usr/local. I haven't been able to build pysqlite with BerkeleyDB compiled in, because the source amalgamation generated by BerkeleyDB is invalid. So that leaves us with dynamically linking, and that requires that we use the BerkeleyDB libsqlite, which is exactly what the previous post described.

In this post I'll describe a better approach. Instead of building a modified version of libsqlite3, we'll modify pysqlite to use the BerkeleyDB libdb_sql library.

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SQLite Table-Valued Functions with Python

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.

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Using the SQLite JSON1 and FTS5 Extensions with Python

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. 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 json1 and 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 pysqlite and apsw, I've included instructions for building both of them. Finally, we'll use peewee ORM to run queries using the json1 and fts5 extensions.

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Using SQLite4's LSM Storage Engine as a Stand-alone NoSQL Database with Python

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SQLite and Key/Value databases are two of my favorite topics to blog about. Today I get to write about both, because in this post I will be demonstrating a Python wrapper for SQLite4's log-structured merge-tree (LSM) key/value store.

I don't actively follow SQLite's releases, but the recent release of SQLite 3.8.11 drew quite a bit of attention as the release notes described massive performance improvements over 3.8.0. While reading the release notes I happened to see a blurb about a new, experimental full-text search extension, and all this got me to wondering what was going on with SQLite4.

As I was reading about SQLite4, I saw that one of the design goals was to provide an interface for pluggable storage engines. At the time I'm writing this, SQLite4 has two built-in storage backends, one of which is an LSM key/value store. Over the past month or two I've been having fun with Cython, writing Python wrappers for the embedded key/value stores UnQLite and Vedis. I figured it would be cool to use Cython to write a Python interface for SQLite4's LSM storage engine.

After pulling down the SQLite4 source code and reading through the LSM header file (it's very small!), I started coding and the result is python-lsm-db (docs).

Read the rest of the post for examples of how to use the library.

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Meet Scout, a Search Server Powered by SQLite

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In my continuing adventures with SQLite, I had the idea of writing a RESTful search server utilizing SQLite's full-text search extension. You might think of it as a poor man's ElasticSearch – a very, very poor man.

So what is this project? Well, the idea I had was that instead of building out separate search implementations for my various projects, I would build a single lightweight search service I could use everywhere. I really like SQLite (and have previously blogged about using SQLite's full-text search with Python), and the full-text search extension is quite good, so it didn't require much imagination to take the next leap and expose it as a web-service.

Read on for more details.

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Extending SQLite with Python

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SQLite is an embedded database, which means that instead of running as a separate server process, the actual database engine resides within the application. This makes it possible for the database to call directly into the application when it would be beneficial to add some low-level, application-specific functionality. SQLite provides numerous hooks for inserting user code and callbacks, and, through virtual tables, it is even possible to construct a completely user-defined table. By extending the SQL language with Python, it is often possible to express things more elegantly than if we were to perform calculations after the fact.

In this post I'll describe how to extend SQLite with Python, adding functions and aggregates that will be callable directly from any SQL queries you execute. We'll wrap up by looking at SQLite's virtual table mechanism and seeing how to expose a SQL interface over external data sources.

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