Commit Graph

10 Commits

Author SHA1 Message Date
Bjoern Rabenstein 38fc24d0ed Fix targetpool_test.go and other tests.
Change-Id: I91a4dd1d39e01f174e1aaae653ce1ed7aecaa624
2014-11-25 17:08:26 +01:00
Julius Volz 7f5d3c2c29 Fix and improve the fp locker.
Benchmark:
$ go test -bench 'Fingerprint' -test.run 'Fingerprint' -test.cpu=1,2,4

OLD
BenchmarkFingerprintLockerParallel        500000              3618 ns/op
BenchmarkFingerprintLockerParallel-2      100000             12257 ns/op
BenchmarkFingerprintLockerParallel-4      500000             10164 ns/op
BenchmarkFingerprintLockerSerial        10000000               283 ns/op
BenchmarkFingerprintLockerSerial-2      10000000               284 ns/op
BenchmarkFingerprintLockerSerial-4      10000000               288 ns/op

NEW
BenchmarkFingerprintLockerParallel       1000000              1018 ns/op
BenchmarkFingerprintLockerParallel-2     1000000              1164 ns/op
BenchmarkFingerprintLockerParallel-4     2000000               910 ns/op
BenchmarkFingerprintLockerSerial        50000000                56.0 ns/op
BenchmarkFingerprintLockerSerial-2      50000000                47.9 ns/op
BenchmarkFingerprintLockerSerial-4      50000000                54.5 ns/op

Change-Id: I3c65a43822840e7e64c3c3cfe759e1de51272581
2014-11-25 17:07:45 +01:00
Julius Volz e7ed39c9a6 Initial experimental snapshot of next-gen storage.
Change-Id: Ifb8709960dbedd1d9f5efd88cdd359ee9fa9d26d
2014-11-25 17:02:00 +01:00
Julius Volz 01f652cb4c Separate storage implementation from interfaces.
This was initially motivated by wanting to distribute the rule checker
tool under `tools/rule_checker`. However, this was not possible without
also distributing the LevelDB dynamic libraries because the tool
transitively depended on Levigo:

rule checker -> query layer -> tiered storage layer -> leveldb

This change separates external storage interfaces from the
implementation (tiered storage, leveldb storage, memory storage) by
putting them into separate packages:

- storage/metric: public, implementation-agnostic interfaces
- storage/metric/tiered: tiered storage implementation, including memory
                         and LevelDB storage.

I initially also considered splitting up the implementation into
separate packages for tiered storage, memory storage, and LevelDB
storage, but these are currently so intertwined that it would be another
major project in itself.

The query layers and most other parts of Prometheus now have notion of
the storage implementation anymore and just use whatever implementation
they get passed in via interfaces.

The rule_checker is now a static binary :)

Change-Id: I793bbf631a8648ca31790e7e772ecf9c2b92f7a0
2014-04-16 13:30:19 +02:00
Julius Volz 86fc13a52e Convert metric.Values to slice of values.
The initial impetus for this was that it made unmarshalling sample
values much faster.

Other relevant benchmark changes in ns/op:

Benchmark                                 old        new   speedup
==================================================================
BenchmarkMarshal                       179170     127996     1.4x
BenchmarkUnmarshal                     404984     132186     3.1x

BenchmarkMemoryGetValueAtTime           57801      50050     1.2x
BenchmarkMemoryGetBoundaryValues        64496      53194     1.2x
BenchmarkMemoryGetRangeValues           66585      54065     1.2x

BenchmarkStreamAdd                       45.0       75.3     0.6x
BenchmarkAppendSample1                   1157       1587     0.7x
BenchmarkAppendSample10                  4090       4284     0.95x
BenchmarkAppendSample100                45660      44066     1.0x
BenchmarkAppendSample1000              579084     582380     1.0x
BenchmarkMemoryAppendRepeatingValues 22796594   22005502     1.0x

Overall, this gives us good speedups in the areas where they matter
most: decoding values from disk and accessing the memory storage (which
is also used for views).

Some of the smaller append examples take minimally longer, but the cost
seems to get amortized over larger appends, so I'm not worried about
these. Also, we're currently not bottlenecked on the write path and have
plenty of other optimizations available in that area if it becomes
necessary.

Memory allocations during appends don't change measurably at all.

Change-Id: I7dc7394edea09506976765551f35b138518db9e8
2014-03-11 18:23:37 +01:00
Julius Volz 740d448983 Use custom timestamp type for sample timestamps and related code.
So far we've been using Go's native time.Time for anything related to sample
timestamps. Since the range of time.Time is much bigger than what we need, this
has created two problems:

- there could be time.Time values which were out of the range/precision of the
  time type that we persist to disk, therefore causing incorrectly ordered keys.
  One bug caused by this was:

  https://github.com/prometheus/prometheus/issues/367

  It would be good to use a timestamp type that's more closely aligned with
  what the underlying storage supports.

- sizeof(time.Time) is 192, while Prometheus should be ok with a single 64-bit
  Unix timestamp (possibly even a 32-bit one). Since we store samples in large
  numbers, this seriously affects memory usage. Furthermore, copying/working
  with the data will be faster if it's smaller.

*MEMORY USAGE RESULTS*
Initial memory usage comparisons for a running Prometheus with 1 timeseries and
100,000 samples show roughly a 13% decrease in total (VIRT) memory usage. In my
tests, this advantage for some reason decreased a bit the more samples the
timeseries had (to 5-7% for millions of samples). This I can't fully explain,
but perhaps garbage collection issues were involved.

*WHEN TO USE THE NEW TIMESTAMP TYPE*
The new clientmodel.Timestamp type should be used whenever time
calculations are either directly or indirectly related to sample
timestamps.

For example:
- the timestamp of a sample itself
- all kinds of watermarks
- anything that may become or is compared to a sample timestamp (like the timestamp
  passed into Target.Scrape()).

When to still use time.Time:
- for measuring durations/times not related to sample timestamps, like duration
  telemetry exporting, timers that indicate how frequently to execute some
  action, etc.

*NOTE ON OPERATOR OPTIMIZATION TESTS*
We don't use operator optimization code anymore, but it still lives in
the code as dead code. It still has tests, but I couldn't get all of them to
pass with the new timestamp format. I commented out the failing cases for now,
but we should probably remove the dead code soon. I just didn't want to do that
in the same change as this.

Change-Id: I821787414b0debe85c9fffaeb57abd453727af0f
2013-12-03 09:11:28 +01:00
Matt T. Proud 30b1cf80b5 WIP - Snapshot of Moving to Client Model. 2013-06-25 15:52:42 +02:00
Julius Volz 138334fb31 Fix handling of negative deltas for non-counter values. 2013-05-28 17:36:53 +02:00
Julius Volz 750f862d9a Use GetBoundaryValues() for non-counter deltas. 2013-05-22 19:13:47 +02:00
Bernerd Schaefer 428d91c86f Rename test helper files to helpers_test.go
This ensures that these files are properly included only in testing.
2013-05-14 16:30:47 +02:00