Commit graph

13 commits

Author SHA1 Message Date
Matt T. Proud 4a87c002e8 Update low-level i'faces to reflect wireformats.
This commit fixes a critique of the old storage API design, whereby
the input parameters were always as raw bytes and never Protocol
Buffer messages that encapsulated the data, meaning every place a
read or mutation was conducted needed to manually perform said
translations on its own.  This is taxing.

Change-Id: I4786938d0d207cefb7782bd2bd96a517eead186f
2013-09-04 17:13:58 +02:00
Matt T. Proud 2b42fd0068 Snapshot of no more frontier.
Change-Id: Icd52da3f52bfe4529829ea70b4865ed7c9f6c446
2013-08-23 17:13:58 +02:00
Julius Volz aa5d251f8d Use github.com/golang/glog for all logging. 2013-08-12 17:54:36 +02:00
Matt T. Proud 30b1cf80b5 WIP - Snapshot of Moving to Client Model. 2013-06-25 15:52:42 +02:00
Matt T. Proud a73f061d3c Persist solely Protocol Buffers.
An design question was open for me in the beginning was whether to
serialize other types to disk, but Protocol Buffers quickly won out,
which allows us to drop support for other types.  This is a good
start to cleaning up a lot of cruft in the storage stack and
can let us eventually decouple the various moving parts into
separate subsystems for easier reasoning.

This commit is not strictly required, but it is a start to making
the rest a lot more enjoyable to interact with.
2013-06-08 11:02:35 +02:00
Matt T. Proud 86f63b078b Fix fallthrough compaction value ordering.
We discovered a regression whereby data chunks could be appended out
of order if the fallthrough case was hit.
2013-06-07 14:41:00 +02:00
Julius Volz 5b105c77fc Repointerize fingerprints. 2013-05-21 14:28:14 +02:00
Matt T. Proud 8f4c7ece92 Destroy naked returns in half of corpus.
The use of naked return values is frowned upon.  This is the first
of two bulk updates to remove them.
2013-05-16 10:53:25 +03:00
Matt T. Proud d538b0382f Include long-tail data deletion mechanism.
This commit introduces the long-tail deletion mechanism, which will
automatically cull old sample values.  It is an acceptable
hold-over until we get a resampling pipeline implemented.

Kill legacy OS X documentation, too.
2013-05-13 10:54:36 +02:00
Matt T. Proud 161c8fbf9b Include deletion processor for long-tail values.
This commit extracts the model.Values truncation behavior into the actual
tiered storage, which uses it and behaves in a peculiar way—notably the
retention of previous elements if the chunk were to ever go empty.  This is
done to enable interpolation between sparse sample values in the evaluation
cycle.  Nothing necessarily new here—just an extraction.

Now, the model.Values TruncateBefore functionality would do what a user
would expect without any surprises, which is required for the
DeletionProcessor, which may decide to split a large chunk in two if it
determines that the chunk contains the cut-off time.
2013-05-10 12:19:12 +02:00
Matt T. Proud 4298bab2b0 Publicize Curator and Processors.
This commit publicizes the curation and processor frameworks for
purposes of making them available in the main processor loop.
2013-05-02 12:37:24 +02:00
Matt T. Proud 3fa260f180 Complete sentence. 2013-04-28 20:26:44 +02:00
Matt T. Proud b3e34c6658 Implement batch database sample curator.
This commit introduces to Prometheus a batch database sample curator,
which corroborates the high watermarks for sample series against the
curation watermark table to see whether a curator of a given type
needs to be run.

The curator is an abstract executor, which runs various curation
strategies across the database.  It remarks the progress for each
type of curation processor that runs for a given sample series.

A curation procesor is responsible for effectuating the underlying
batch changes that are request.  In this commit, we introduce the
CompactionProcessor, which takes several bits of runtime metadata and
combine sparse sample entries in the database together to form larger
groups.  For instance, for a given series it would be possible to
have the curator effectuate the following grouping:

- Samples Older than Two Weeks: Grouped into Bunches of 10000
- Samples Older than One Week: Grouped into Bunches of 1000
- Samples Older than One Day: Grouped into Bunches of 100
- Samples Older than One Hour: Grouped into Bunches of 10

The benefits hereof of such a compaction are 1. a smaller search
space in the database keyspace, 2. better employment of compression
for repetious values, and 3. reduced seek times.
2013-04-27 17:38:18 +02:00