FinagleCon

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FinagleCon was held at TwitterHQ in San Francisco. It is refreshing to see a nice working atmosphere with free food and drinks. Now for the contents.

Twitter’s RPC framework, Finagle, has been in production since August 2010 and has over 140 contributors. In addition to Twitter, it has been adopted by many large companies such as SoundCloud. Initially written in Java with FP constructs (monads, maps, etc.) all over, it was soon after rewritten in Scala.

Finagle is based on three core concepts: Simplicity, Composability, and Separation of Concerns. These concepts are shown through three primitive building blocks: Future, Service, and Filter.

  • Futures provide an easy interface to create asynchronous computation and to model sequential or asynchronous data-flows.
  • Services are functions that return futures, used to abstract away, possibly remote, service calls.
  • Filters are essentially decorators and are meant to contain modular blocks of re-usable, non-business logic. Example usages are LoggingFilter and RetryingFilter.

The use of Futures makes it easy to test asynchronous computations. Services and filters both can be created separately, each containing their specialized logic. This modularity makes it easy to test and reason about them separately. Services and filters are easily composed, just like functions do, which makes it convenient to test chains. Services and filters are meant to separate behaviour from domain logic.

As amazing as Finagle is, there are some things one should be aware of. To create a really resilient application with Finagle one has to be an expert in its internals. Many configuration parameters influence each other, e.g. queue size and time-outs. With a properly tuned setup Finagle is properly fast and resilient (the defaults are good as well, mind you). As most data centres are heterogenous in their setup, faster machines are added to the pool, and other conditions change, one has to keep attention to the tuning continuously in order to maintain optimal performance.

Some general advice, watch out for traffic amplification due to retries, keep your timeouts low so retry is useful, but not as low that you introduce spurious timeouts.

For extra points, keep hammering your application until it breaks, find out why it breaks, fix it, and repeat.

The future

In addition to this heads up we were also given a nice insight in the upcoming things for Finagle.

In order to make more informed decision, we will get a new Failure type which contains more information instead of ‘just’ a Throwable. In this new Failure, an added field indicates whether it is safe to retry.

There are several issues with the current way of fine-tuning Finagle, as mentioned, you need to be an expert to use all the configuration parameters properly. Next to this the configuration is static and doesn’t take into account changing environments and behaviour of downstream services. Because the tuning of the parameters is tightly coupled with the implementation of Finagle it is also hard to change the implementation significantly without significant re-tuning.

In order to battle the last two points, Finagle will introduce Service Level Objectives (SLO). The SLO is a higher-level goal that Finagle should strive to reach instead of low-level hardcoded parameters. What these SLO will be exactly is not yet known.

The community

The Finagle team will synchronize the internal Finagle repository with the Github repository every Monday. They will strive to publish a snapshot version of the change as well.

For someone looking to write his own protocol to connect to his service, finagle-serial is a nice project to start with. It is small enough to grasp within a day but big enough to be non-trivial.

It was found that the ParGCCardsPerStrideChunk garbage collection option, available from 7u40, can halve GC times on large heaps. It is recommended to try this parameter. Tuning seems to be hard to do and is generally done by copying a ‘known good set’ of parameters.

Scrooge is a good utility to use for Thrift and Scala as it is aware of Scala features such as Traits and Objects and can generate relevant transformations for them.

When you want to connect to multiple data-centres from a single data-centre one can use LatencyCompensation to include latency times.

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