having moved inititalisation into startService to handle tub init cleanly,
I neglected the up-call to startService, which wound up not starting the
load_monitor.
also I changed the 'running' attribute to 'started' since 'running' is
the name used internally by MultiService itself.
this adds an interface, IStatsProducer, defining the get_stats() method
which the stats provider calls upon and registered producer, and made the
register_producer() method check that interface is implemented.
also refine the startup logic, so that the stats provider doesn't try and
connect out to the stats gatherer until after the node declares the tub
'ready'. this is to address an issue whereby providers would attach to
the gatherer without providing a valid furl, and hence the gatherer would
be unable to determine the tubid of the connected client, leading to lost
samples.
The filesystem which gets my vote for most undeservedly popular is ext3, and it has a hard limit of 32,000 entries in a directory. Many other filesystems (even ones that I like more than I like ext3) have either hard limits or bad performance consequences or weird edge cases when you get too many entries in a single directory.
This patch makes it so that there is a layer of intermediate directories between the "shares" directory and the actual storage-index directory (the one whose name contains the entire storage index (z-base-32 encoded) and which contains one or more share files named by their share number).
The intermediate directories are named by the first 14 bits of the storage index, which means there are at most 16384 of them. (This also means that the intermediate directory names are not a leading prefix of the storage-index directory names -- to do that would have required us to have intermediate directories limited to either 1024 (2-char), which is too few, or 32768 (3-chars of a full 5 bits each), which would overrun ext3's funny hard limit of 32,000.))
This closes#150, and please see the "convertshares.py" script attached to #150 to convert your old tahoe-0.7.0 storage/shares directory into a new tahoe-0.8.0 storage/shares directory.
We have a desire to collect runtime statistics from multiple nodes primarily
for server monitoring purposes. This implements a simple implementation of
such a system, as a skeleton to build more sophistication upon.
Each client now looks for a 'stats_gatherer.furl' config file. If it has
been configured to use a stats gatherer, then it instantiates internally
a StatsProvider. This is a central place for code which wishes to offer
stats up for monitoring to report them to, either by calling
stats_provider.count('stat.name', value) to increment a counter, or by
registering a class as a stats producer with sp.register_producer(obj).
The StatsProvider connects to the StatsGatherer server and provides its
provider upon startup. The StatsGatherer is then responsible for polling
the attached providers periodically to retrieve the data provided.
The provider queries each registered producer when the gatherer queries
the provider. Both the internal 'counters' and the queried 'stats' are
then reported to the gatherer.
This provides a simple gatherer app, (c.f. make stats-gatherer-run)
which prints its furl and listens for incoming connections. Once a
minute, the gatherer polls all connected providers, and writes the
retrieved data into a pickle file.
Also included is a munin plugin which knows how to read the gatherer's
stats.pickle and output data munin can interpret. this plugin,
tahoe-stats.py can be symlinked as multiple different names within
munin's 'plugins' directory, and inspects argv to determine which
data to display, doing a lookup in a table within that file.
It looks in the environment for 'statsfile' to determine the path to
the gatherer's stats.pickle. An example plugins-conf.d file is
provided.
