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
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.
It runs on the JVM so it can take advantage of the hard work people have put into java performance.
Currently about 2K 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.
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.
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:
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".
This is not a database. Item ownership is transferred with acknowledgement, but kestrel does not support multiple outstanding operations, and treats each enqueued item as an atomic unit.
Kestrel requires java 6 (for JMX support) and ant 1.7. If you see an error
about missing JMX classes, it usually means you're building with java 5. On a
mac, 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
You can run kestrel by hand via:
$ java -jar ./dist/kestrel-VERSION/kestrel-VERSION.jar
To run in development mode (using
development.conf instead of
production.conf), add a
$ java -Dstage=development -jar ./dist/kestrel-VERSION/kestrel-VERSION.jar
When running it as a server, a startup script is provided in
dist/kestrel-VERSION/scripts/kestrel.sh. 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
/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
The startup script logs extensive GC information to a file named
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/guide.md (an operational
guide). There are a few global config options that should be self-explanatory:
Host to accept connections on.
Port to listen on. 22133 is the standard.
Seconds after which an idle client is disconnected, or 0 to have no idle timeout.
The folder to store queue journal files in. Each queue (and each client of a fanout queue) gets its own file here.
Logfile configuration, as described in configgy.
Frequency (in seconds) that a timer thread should scan active queues for expired items. By default, this is off (0) and no automatic expiration scanning happens; instead, items expire when a client does a
GETon a queue. When this is set, a background thread will periodically flush expired items from the head of every queue.
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 ========= ================= ==========
A single-threaded set of 5 million puts gives a fair idea of throughput distribution, this time on a 2.5GHz 2008-model macbook pro:
$ ant -f tests.xml put-many-1 -Ditems=5000000 [java] Finished in 1137250 msec (227.5 usec/put throughput). [java] Transactions: min=106.00; max=108581.00 91335.00 60721.00; median=153.00; average=201.14 usec [java] Transactions distribution: 5.00%=129.00 10.00%=134.00 25.00%=140.00 50.00%=153.00 75.00%=177.00 90.00%=251.00 95.00%=345.00 99.00%=586.00 99.90%=5541.00 99.99%=26910.00
This works out to about 3.23MB/sec (over loopback) and about 4400 puts/sec.
Robey Pointer <firstname.lastname@example.org>