Entries tagged with cython
Kyoto Tycoon and Tokyo Tyrant with Python
I recently open-sourced a Python library for working with the networked key/value databases Kyoto Tycoon and Tokyo Tyrant. These databases sit atop Kyoto Cabinet and Tokyo Cabinet, respectively, and provide fast DBM implementations.
The Cabinet libraries expose a familiar key/value API backed by a number of different storage options, including persistent hash-tables and b-trees, as well as in-memory variants. A cabinet database can only be accessed by a single process at any given time, though they can be used safely in multi-threaded environments. For this reason, the author created an evented network layer that can expose these storage interfaces to multiple processes. These database servers are Kyoto Tycoon and Tokyo Tyrant, and in addition to providing a fast front-end to the underlying storage engines, they can add features like lua scripting, multi-master replication and LRU cache eviction.
Some scenarios in which you might find these databases useful:
- Memcached or Redis replacement. The LRU eviction provided by Kyoto Tycoon, combined with being scriptable with Lua, and backed with persistent storage, allows these databases to go beyond traditional key/value database roles.
- Time-series and event-logging. The B-Tree storage engine supports fast range scans and ordered key traversal, for rolling-up events, map/reduce workflows, and reporting.
- Full-text search, using the hash storage engine.
- Secondary indexes for external data-stores.
- Document database using lua tables in place of JSON objects.
Features of the kt library:
- Binary protocol support implemented as a C extension.
- Thread-safe and greenlet-safe.
- Simple and memorable APIs.
- Full-featured implementation of the protocols.
- Multiple serialization schemes, including JSON, msgpack and pickle.
If you're interested in trying out these fantastic databases with Python, the documentation for kt can be found here: http://kt-lib.readthedocs.io/en/latest/
Announcing sophy: fast Python bindings for Sophia Database
Sophia is a powerful key/value database with loads of features packed into a simple C API. In order to use this database in some upcoming projects I've got planned, I decided to write some Python bindings and the result is sophy. In this post, I'll describe the features of Sophia database, and then show example code using sophy, the Python wrapper.
Here is an overview of the features of the Sophia database:
- Append-only MVCC database
- ACID transactions
- Consistent cursors
- Compression
- Ordered key/value store
- Range searches
- Prefix searches
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. If you use Peewee, an equivalent implementation is included in the Peewee SQLite extensions.
Using SQLite4's LSM Storage Engine as a Stand-alone NoSQL Database with Python
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.
Introduction to the fast new UnQLite Python Bindings
About a year ago, I blogged about some Python bindings I wrote for the embedded NoSQL document store UnQLite. One year later I'm happy to announce that I've rewritten the library using Cython and operations are, in most cases, an order of magnitude faster.
This was my first real attempt at using Cython and the experience was just the right mix of challenging and rewarding. I bought the O'Reilly Cython Book which came in super handy, so if you're interested in getting started with Cython I recommend picking up a copy.
In this post I'll quickly touch on the features of UnQLite, then show you how to use the Python bindings. When you're done reading you should hopefully be ready to use UnQLite in your next Python project.