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${ noResults }
17 Commits (aed1d384a911e0931a587990ee76ec8b6b369d73)
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 |
Julius Volz | c7c0b33d0b |
Add regex-matching support for labels.
There are four label-matching ops for selecting timeseries now: - Equal: = - NotEqual: != - RegexMatch: =~ - RegexNoMatch: !~ Instead of looking up labels by a simple clientmodel.LabelSet (basically an equals op for every key/value pair in the set), timeseries fingerprint selection is now done via a list of metric.LabelMatchers. Change-Id: I510a83f761198e80946146770ebb64e4abc3bb96 |
11 years ago |
Julius Volz | 3f226c9724 |
Rename {Scalar,Vector}Literal to {Scalar,Vector}Selector.
Change-Id: Ie92301f47f5f49f30b3a62c365e377108982b080 |
11 years ago |
Bjoern Rabenstein | 682cf6fc51 |
Simplify QueryAnalizer.Visit().
Change-Id: I628582a1903b7273e78921e22a475f1dae5ebaae |
11 years ago |
Bjoern Rabenstein | fd63500ed3 |
Make rules/ast golint clean.
Mostly, that means adding compliant doc strings to exported items. Also, remove 'go vet' warnings where possible. (Some are unfortunately not to avoid, arguably bugs in 'go vet'.) Change-Id: I2827b6dd317492864c1383c3de1ea9eac5a219bb |
11 years ago |
Julius Volz | c4adfc4f25 |
Minor code cleanups.
Change-Id: Ib3729cf38b107b7f2186ccf410a745e0472e3630 |
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 | 7db518d3a0 |
Abstract high watermark cache into standard LRU.
Conflicts: storage/metric/memory.go storage/metric/tiered.go storage/metric/watermark.go Change-Id: Iab2aedbd8f83dc4ce633421bd4a55990fa026b85 |
11 years ago |
Julius Volz | aa5d251f8d |
Use github.com/golang/glog for all logging.
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11 years ago |
Julius Volz | d2da21121c |
Implement getValueRangeAtIntervalOp for faster range queries.
This also short-circuits optimize() for now, since it is complex to implement for the new operator, and ops generated by the query layer already fulfill the needed invariants. We should still investigate later whether to completely delete operator optimization code or extend it to support getValueRangeAtIntervalOp operators. |
12 years ago |
Matt T. Proud | 30b1cf80b5 |
WIP - Snapshot of Moving to Client Model.
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12 years ago |
Julius Volz | 51689d965d |
Add debug timers to instant and range queries.
This adds timers around several query-relevant code blocks. For now, the query timer stats are only logged for queries initiated through the UI. In other cases (rule evaluations), the stats are simply thrown away. My hope is that this helps us understand where queries spend time, especially in cases where they sometimes hang for unusual amounts of time. |
12 years ago |
Julius Volz | 5b105c77fc |
Repointerize fingerprints.
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12 years ago |
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. |
12 years ago |
Julius Volz | 56324d8ce2 |
Make AST query storage non-global.
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12 years ago |
Julius Volz | b836066c71 |
Eliminate need to get fingerprints during query execution time.
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12 years ago |
Julius Volz | 8e4c5b0cea |
Use AST query analyzer and views with tiered storage.
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12 years ago |