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Kestrel is based on Blaine Cook's "starling" simple, distributed message queue, with added features and bulletproofing, as well as the scalability offered by actors and the JVM.

Each server handles a set of 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 put 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!

For more information about what it is and how to use it, check out the included guide.

Kestrel has a mailing list here:

Author's address: Robey Pointer <>


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 2500 lines of scala, because it relies on finagle and netty, and 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.


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, so no jobs will be lost, but kestrel does not support grouping multiple operations into an atomic unit.

Downloading it

The latest release is always on the homepage here:

Or the latest development versions & branches are on github:

Building it

Kestrel requires java 6 and sbt 0.7.4. On OS X 10.5, you may have to hard-code an annoying JAVA_HOME to use java 6:

$ export JAVA_HOME=/System/Library/Frameworks/JavaVM.framework/Versions/1.6/Home

Building from source is easy:

$ sbt clean update package-dist

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

Running it

You can run kestrel by hand, in development mode, via:

$ ./dist/kestrel-VERSION/scripts/

Like all ostrich-based servers, it uses the "stage" property to determine which config file to load, so sets -Dstage=development.

When running it as a server, a startup script is provided in dist/kestrel-VERSION/scripts/ The script assumes you have daemon, a standard daemonizer for Linux, but also available here for all common unix platforms.

The created archive kestrel-VERSION.tar.bz2 can be expanded into a place like /usr/local (or wherever you like) and executed within its own folder as a self-contained package. All dependent jars are included, and the startup script loads things from relative paths.

The default configuration puts logfiles into /var/log/kestrel/ and queue journal files into /var/spool/kestrel/.

The startup script logs extensive GC information to a file named stdout in the log folder. If kestrel has problems starting up (before it can initialize logging), it will usually appear in error in the same folder.


Queue configuration is described in detail in docs/ (an operational guide). Scala docs for the config variables are here.

Performance / Load

Several performance/load tests are included. To run them, first start up a kestrel instance locally.

$ sbt clean update package-dist
$ ./dist/kestrel-VERSION/scripts/

All of the tests can be run from scripts in scripts/load/, and all of them respond to "--help".


This test just spams a kestrel server with "put" operations, to see how quickly it can absorb and journal them.

A sample run (on a Mac laptop):

$ ./dist/kestrel/scripts/load/put-many -n 100000
Flushing queues first.
Put 100000 items of 1024 bytes in bursts of 1 to localhost:22133 in 1
  queues named spam using 100 clients.
Finished in 4410 msec (44.1 usec/put throughput).
Distribution in usec: min=0 max=3858 p50=1 p75=1 p90=1 p95=2 p99=10
  p999=316 p9999=472


This test has one producer that trickles out one item at a time, and a pile of consumers fighting for each item. It usually takes exactly as long as the number of items times the delay, but is useful as a validation test to make sure kestrel works as advertised without blowing up.

A sample run:

$ ./dist/kestrel/scripts/load/many-clients
many-clients: 100 items to localhost using 100 clients, kill rate 0%, at
  100 msec/item
Flushing queues first.
Received 100 items in 12138 msec.

This test always takes over 10 seconds -- it's a load test instead of a speed test.


This test starts up one producer and one consumer, and just floods items through kestrel as fast as it can.

A sample run:

$ ./dist/kestrel/scripts/load/flood
Flushing queues first.
flood: producers=1 consumers=1 each sending 10000 items of 1kB through
Finished in 884 msec (88.4 usec/put throughput).
Consumer(s) spun 6 times in misses.


This test starts up one producer and one consumer, seeds the queue with a bunch of items to cause it to fall behind, then does cycles of flooding items through the queue, separated by pauses. It's meant to test kestrel's behavior with a queue that's fallen behind and stays behind indefinitely, to make sure the journal files are cleaned up periodically without affecting performance too badly.

(In the past, journals were "packed" as they fell behind. These days, the old journal files are just erased as their items are read, so the name of this test is historic and a bit misleading.)

A sample run:

$ ./dist/kestrel/scripts/load/packing -c 10 -q small
packing: 25000 items of 1kB with 1 second pauses
Flushing queues first.
Wrote 25000 items starting at 0.
cycle: 1
Wrote 25000 items starting at 25000.
Read 25000 items in 2862 msec. Consumer spun 0 times in misses.
cycle: 2
Wrote 25000 items starting at 50000.
Read 25000 items in 2426 msec. Consumer spun 0 times in misses.
cycle: 10
Wrote 25000 items starting at 250000.
Read 25000 items in 2464 msec. Consumer spun 0 times in misses.
Read 25000 items in 2181 msec. Consumer spun 0 times in misses.

You can see the journals being built and erased in the kestrel log. Like "many-clients", this test is a load test instead of a speed test.


This test starts a producer and several consumers, with the consumers occasionally "forgetting" to acknowledge an item that they've read. It verifies that the un-acknowledged items are eventually handed off to another consmer.

A sample run:

$ ./dist/kestrel/scripts/load/leaky-reader -n 100000 -t 10
leaky-reader: 10 threads each sending 100000 items through spam
Flushing queues first.
Finished in 40220 msec (40.2 usec/put throughput).
Completed all reads

Like "many-clients", it's just a load test.

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