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Measuring Nginx Cache Performance using Lua and Redis
Shortly after launching my Nginx-based cache + thumbnailing web-service, I realized I had no visibility into the performance of the service. I was curious what my hit-ratios were like, how much time was spent during a cache-miss, basic stuff like that. Nginx has monitoring tools, but it looks like they're only available to people who pay for Nginx Plus, so I decided to see if I could roll my own. In this post, I'll describe how I used Lua, cosockets, and Redis to extract real-time metrics from my thumbnail service.
Nginx: a caching, thumbnailing, reverse proxying image server?
A month or two ago, I decided to remove Varnish from my site and replace it with Nginx's built-in caching system. I was already using Nginx to proxy to my Python sites, so getting rid of Varnish meant one less thing to fiddle with. I spent a few days reading up on how to configure Nginx's cache and overhauling the various config files for my Python sites (so much for saving time). In the course of my reading I bookmarked a number of interesting Nginx modules to return to, among them the Image Filter module.
Five reasons you should use SQLite in 2016
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.
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 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.
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.
Why I won't be switching to Disque
Disque's alpha release announcement generated some buzz on HackerNews. If you missed it, Disque is a distributed message broker from Salvatore Sanfilippo, the author of Redis.
In the Limitations section of the README, Salvatore has written:
Disque was designed a bit in astronaut mode, not triggered by an actual use case of mine, but more in response to what I was seeing people doing with Redis as a message queue and with other message queues.
This admission makes me wary of using Disque, even if it reaches a stable release, because of my own experience with similar projects I've created but never actually used. These projects are usually fun opportunities for learning, but when it comes to maintenance, my experience has shown me that they quickly become a burden. Usually the problem is masked by the fact that if I'm not using it usually nobody else is either, but in the rare case I do end up with users, then eventually those users are going to submit bug reports and feature requests.
For a problem as complex as a distribute message broker, I imagine that there are going to be a lot of bug reports, strange edge-cases, and feature requests to support exotic use-cases. I hope that, in addition to his work on Redis, Salvatore can find the time to support Disque!
The other reason I don't foresee using Disque is alluded to in the author's own comments. He observes that many people are using Redis as a message broker, and decides that maybe there is a need for a "Redis of messaging". I would say the opposite is true, and that instead of another message server, people want to use Redis!
Redis integrates very nicely into the stack for web-based projects. It can be used as a cache, for locking, as a primary data store, for write-heavy portions of the application, and yes, as a message broker.
Perhaps the reason people are using Redis as a message broker is because they don't want to use something else?