Double bill! Running Java services in Production & Probabilistic data structures
Details
In May we have two talks lined up! You lucky things.
Doors open at 7pm and we will have beer, soft drinks and pizza supplied by our sponsors Brandwatch. Talks will begin around 7:30pm. Developers of all levels are welcome to join us.
This evening is going to be live streamed with a recording to be posted on this meetup shortly after. Stream starts at 7:30pm.
UPDATE: The URL for the stream is https://zoom.us/j/839149547
Now, the talks:
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Talk 1: Running your Java service in Production (“We don’t need no thought control…”)
- Matthew Byng-Maddick
Developers understand a lot about writing maintainable code: design patterns, TDD etc., but still our Operations teams often clash with us.
To help tear down the walls, Matthew, an ex-Facebook Site Reliability Engineer specialising in Hadoop, HBase and HDFS (all written in Java) will talk about what isn’t solved with just these code maintainability principles when it comes to actually having high-quality reliable services in a production environment.
This includes descriptions of how services might be monitored, what constitutes good logging and code structure, and issues often associated with deployment and startup, and how SRE teams can build tooling to help make things better in conjunction with developers.
Talk 2: Acceptably inaccurate: probabilistic data structures
- James Stanier
Writing software for the Internet puts engineers face to face with traffic that makes doing even simple things difficult. Seemingly straightforward operations such as counting, set membership and set cardinality become either extremely slow or prohibitively expensive to do using traditional methods.
Help is at hand, however: probabilistic techniques are both fascinating and extremely useful. By sacrificing a predictable amount of accuracy, we can perform operations at scales we never thought possible, and fast! We'll introduce approaches such as Bloom filters for set membership, count-min sketch for frequency in streams, and HyperLogLog for cardinality. No maths PhD is required.
We'll look at the before and after effects of using these techniques in real-world scenarios, and present the libraries that you can go away and play with right now.