metastore README
This module provides a basic persistent key-value store. Can be used in-process for local operations, and remotely through XML-RPC. Makes use of sqlite for persisting data.
It was written to gather simulation metrics from a distributed simulation, with convenience and ease of use as its only requirement. This is not a high performance datastore in any sense.
Usage
The metastore module can either be used as a local in-process store, or as a remote store.
Local use
If running everything in one process, it is easiest just to import the metastore module, and start getting and putting values. This is what I use when testing locally, it imports sqlite3 and writes directly to the sqlite database.
import metastore
metastore.put("foo", "bar")
metastore.get("foo")
Optionally, to specify the sqlite database:
import metastore
metastore.get_store("path/to/store.sdb")
metastore.put("foo", "bar")
metastore.get("foo")
Remote usage
If running on multiple machines (or in multiple processes), running a central metastore server process may be desirable (see below how to start a server). Worker machines/processes can then connect to the remote server by invoking the use_remote method.
import metastore
metastore.use_remote("machine.running.metastore.server.com")
metastore.put("foo", "bar")
metastore.get("foo")
Start a server from command line
$ python metastore/__init__.py
Optionally, to specify the sqlite database:
$ python metastore/__init__.py path/to/store.sdb
Optionally, to specify the listening port:
$ python metastore/__init__.py path/to/store.sdb 1234
In case you want to run the server on a machine behind a firewall, consider taking a look at PageKite.
Start a server from within Python
import metastore
s = metastore.MetaServer()
s.serve()
Optionally, to specify the sqlite database:
import metastore
s = metastore.MetaServer(datafile="path/to/store.sdb")
s.serve()
Optionally, to specify the listening port:
import metastore
s = metastore.MetaServer(port=1234)
s.serve()
Example usage on the DAS3
This module was originally written to gather some simulation metrics on the DAS3 cluster. Nothing fancy, but beats writing metrics to text files and parsing them later.
First, make sure you have a Python module loaded. I use Python/2.5.2
module load python/2.5.2
Started the metastore server on the head node:
$ python metastore/__init__.py simulation_123.sdb 1234
Have each worker process write to the head node:
import metastore
metastore.use_remote("fs0.das3.cs.vu.nl", 1234)
metastore.put("foo", "bar")
Running the server on a worker node
The DAS usage policy states:
Program execution MUST be done on the compute nodes, NEVER on a headnode.
Therefore, you should ideally run the metastore server on a compute node, or
designate one of your worker processes to also run the metastore server.
In my experience, the metastore server process uses limited resources - at least under my workload (<10 req/sec). So, I decided to run the server on the head node. If you are on the DAS3, do what ever you think is best. If in doubt, consult with the DAS3 admins.
The following should work, but I haven't tested it. Make sure that the metastore server job you submit won't expire before the workers that are writing to the store. Here I use the anti-social switch and a timeout of two hours (which the DAS admins won't like during working hours).
prun -asocial -t 2:00:00 -np 1 -v python metastore/__init__.py simulation_123.sdb 1234
This should output something like:
Run on 1 hosts for 7200 seconds from Wed Feb 16 17:07:13 2011
: node083/0
And then you point your worker nodes to node083:
import metastore
metastore.use_remote("node083", 1234)
metastore.put("foo", "bar")
Credits and licence
Written by Bjorn Patrick Swift bjorn@swift.is in February 2011.
Licensed under a Creative Commons Attribution 3.0 Unported License.