If you were to take a look at my blog, you’d think I’d flipped a table and left the tech industry. Not the case at all. I’m still writing, but less frequently, and on the TLP blog. I intend to start writing here again, but the material will likely focus around topics other than Cassandra, since I’m already writing about it elsewhere. Here are the posts I’ve authored in the last 6 months or so:
- Instacluster announced on the Apache Cassandra user list that they are making their supported branch of the Cassandra 3.7 tick tock release publicly available (see GitHub repo). Bug fixes that go into 3.8, 3.9, etc will be back ported to the Instacluster LTS. You can read the blog post about the decision. Some people I’ve talked to are concerned about having different commercial entities doing long term supported releases, and this concern is understandable.
- I’ve spent the last 4 years working in the big data world with Cassandra because it’s the only practical solution if you have a requirement to scale out, uptime is a priority, and you need predictable performance. I’ve heard different ways of describing where Cassandra fits in your architecture, but I think the best way to think of it is close to your customer. Think of the servers your mobile apps communicate with or what holds your product inventory.
- One of the problems of learning a new database is getting used to a new way of data modeling. PostgreSQL looks different from Redis, which is different from a graph, and is different from Cassandra. Cassandra Dataset Manager aims to reduce the time spent in a frustrating trial and error process trying to learn proper data modeling techniques for Apache Cassandra and Datastax Enterprise by providing curated data models which have been designed by professionals with years of experience.
- I posted a short preview showing off some of the work I’ve been doing recently on Cassandra Dataset Manager, a tool to help new Cassandra users learn how to create proper data models. There’s documentation, but it’s still under heavy development.
- Apache Cassandra 3.3 was released last week. As per the Tick Tock release schedule, this release is focused on bug fixes and no new features were introduced. For practical purposes, consider this a bug fix release to Cassandra 3.2. All told there were almost 50 bugs fixed in this release. Many of the bugs fixed in this version also applied to Cassandra 3.0.3, which was also released last week. With any Cassandra release, it’s a good idea to read the Changelog and News before upgrading.
- If you’ve looked into using Cassandra at all, you probably have heard plenty of warnings about its secondary indexes. If you’ve come from a relational background, you may have been surprised when you were told to create multiple tables (materialized views) instead of relying on indexes. This is because Cassandra is a distributed database, and the impact of doing a query that hits your entire cluster is you lose your linear scalability.
- Introduction Building a web app relying on database calls with CPython (the standard Python distribution) is pretty easy, but can suffer from performance problems. Python itself isn’t particularly fast, and in 2.x, it’s concurrency story is especially weak. For starters, there’s the dreaded GIL. The GIL prevents us from taking advantage of multi core systems, so even if we use try to use threads we’re missing out on their main performance benefit, which is parallel computation.
- The 3.0 release of Apache Cassandra marked an important milestone. One of the biggest updates was CASSANDRA-8099, the JIRA to modernize the storage engine. It was also the first release in the new Tick Tock cycle, which lands a new release of Cassandra every month. Even .x numbers (such as 3.2) are feature releases, and odd .x numbers (such as 3.1) are bug fix releases. Cassandra 3.2, released about a week ago, is the first feature release following 3.
- In a previous post, I introduced a new project, KillrAnswers. I had originally planned on writing KillrAnswers using Rust, leveraging the Cap’n Proto library for RPC and object serialization. I’ve had some time to think about this, and decided to switch back to Python. I also started my own RPC project, FrankDux, based on ZeroMQ and MessagePack for object serialization instead of Cap’n Proto. Let’s get the obvious question out of the way - why not use Rust?