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tiny queue system based on starling, in scala
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README.md

Kestrel

Kestrel is a port of Blaine Cook's "starling" message queue system from ruby to scala: http://rubyforge.org/projects/starling/

In Blaine's words:

Starling is a powerful but simple messaging server that enables reliable distributed queuing with an absolutely minimal overhead. It speaks the MemCache protocol for maximum cross-platform compatibility. Any language that speaks MemCache can take advantage of Starling's queue facilities.

The concept of starling is to have a single server handle reliable, ordered message queues. When you put a cluster of these servers together, with no cross communication, and pick a server at random whenever you do a set or get, you end up with a reliable, loosely ordered message queue.

In many situations, loose ordering is sufficient. Dropping the requirement on cross communication makes it horizontally scale to infinity and beyond: no multicast, no clustering, no "elections", no coordination at all. No talking! Shhh!

Kestrel adds several additional features, like ginormous queues, reliable fetch, and blocking/timeout fetch -- as well as the scalability offered by actors and the JVM.

Features

Kestrel is:

  • fast

    It runs on the JVM so it can take advantage of the hard work people have put into java performance.

  • small

    Currently about 1.5K lines of scala (including comments), because it relies on Apache Mina (a rough equivalent of Danger's ziggurat or Ruby's EventMachine) and actors -- and frankly because Scala is extremely expressive.

  • durable

    Queues are stored in memory for speed, but logged into a journal on disk so that servers can be shutdown or moved without losing any data.

  • reliable

    A client can ask to "tentatively" fetch an item from a queue, and if that client disconnects from kestrel before confirming ownership of the item, the item is handed to another client. In this way, crashing clients don't cause lost messages.

Anti-Features

Kestrel is not:

  • strongly ordered

    While each queue is strongly ordered on each machine, a cluster will appear "loosely ordered" because clients pick a machine at random for each operation. The end result should be "mostly fair".

  • transactional

    This is not a database. Item ownership is transferred with acknowledgement, but kestrel does not concern itself with what happens to an item after a client has accepted it.

Use

Building from source is easy:

$ ant

Scala libraries and dependencies will be downloaded from maven repositories the first time you do a build. The finished distribution will be in dist.

A sample startup script is included, or you may run the jar directly. All configuration is loaded from kestrel.conf.

Performance

All of the below timings are on my 2GHz 2006-model macbook pro.

Since starling uses eventmachine in a single-thread single-process form, it has similar results for all access types (and will never use more than one core).

=========  =================  ==========
# Clients  Pushes per client  Total time
=========  =================  ==========
        1             10,000        3.8s
       10              1,000        2.9s
      100                100        3.1s
=========  =================  ==========

Kestrel uses N+1 I/O processor threads (where N = the number of available CPU cores), and a pool of worker threads for handling actor events. Therefore it handles more poorly for small numbers of heavy-use clients, and better for large numbers of clients.

=========  =================  ==========
# Clients  Pushes per client  Total time
=========  =================  ==========
        1             10,000        3.8s
       10              1,000        2.4s
      100                100        1.6s
=========  =================  ==========

Robey Pointer <robeypointer@gmail.com>

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