prometheus

Commit Graph

Author	SHA1	Message	Date
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	11 years ago
Matt T. Proud	3e969a8ca2	Parameterize the buffer for marshal/unmarshal. We are not reusing buffers yet. This could introduce problems, so the behavior is disabled for now. Cursory benchmark data: - Marshal for 10,000 samples: -30% overhead. - Unmarshal for 10,000 samples: -15% overhead. Change-Id: Ib006bdc656af45dca2b92de08a8f905d8d728cac	11 years ago
Matt T. Proud	6ec72393c4	Correct size of unmarshalling destination buffer. The format header size is not deducted from the size of the byte stream when calculating the output buffer size for samples. I have yet to notice problems directly as a result of this, but it is good to fix. Change-Id: Icb07a0718366c04ddac975d738a6305687773af0	11 years ago
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	11 years ago
Julius Volz	a7d0973fe3	Add version field to LevelDB sample format. This doesn't add complex discriminator logic yet, but adds a single version byte to the beginning of each samples chunk. If we ever need to change the disk format again, this will make it easy to do so without having to wipe the entire database. Change-Id: I60c39274256f790bc2da83167a1effaa174588fe	11 years ago
Julius Volz	1eee448bc1	Store samples in custom binary encoding. This has been shown to provide immense decoding speed benefits. See also: https://groups.google.com/forum/#!topic/prometheus-developers/FeGl_qzGrYs Change-Id: I7d45b4650e44ddecaa91dad9d7fdb3cd0b9f15fe	11 years ago
Bjoern Rabenstein	6bc083f38b	Major code cleanup in storage. - Mostly docstring fixed/additions. (Please review these carefully, since most of them were missing, I had to guess them from an outsider's perspective. (Which on the other hand proves how desperately required many of these docstrings are.)) - Removed all uses of new(...) to meet our own style guide (draft). - Fixed all other 'go vet' and 'golint' issues (except those that are not fixable (i.e. caused by bugs in or by design of 'go vet' and 'golint')). - Some trivial refactorings, like reorder functions, minor renames, ... - Some slightly less trivial refactoring, mostly to reduce code duplication by embedding types instead of writing many explicit forwarders. - Cleaned up the interface structure a bit. (Most significant probably the removal of the View-like methods from MetricPersistenc. Now they are only in View and not duplicated anymore.) - Removed dead code. (Probably not all of it, but it's a first step...) - Fixed a leftover in storage/metric/end_to_end_test.go (that made some parts of the code never execute (incidentally, those parts were broken (and I fixed them, too))). Change-Id: Ibcac069940d118a88f783314f5b4595dce6641d5	11 years ago
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	11 years ago
Matt T. Proud	30b1cf80b5	WIP - Snapshot of Moving to Client Model.	12 years ago

9 Commits (e041c0cd463028825559da33bc9fd85ce084b30d)