Now only purge if there is something to purge.
Also, set savedFirstTime and archived time range appropriately.
(Which is needed for the optimization.)
Change-Id: Idcd33319a84def3ce0318d886f10c6800369e7f9
Fix the behavior if preload for non-existent series is requested.
Instead of returning an error (which triggers a panic further up),
simply count those incidents. They can happen regularly, we just want
to know if they happen too frequently because that would mean the
indexing is behind or broken.
Change-Id: I4b2d1b93c4146eeea897d188063cb9574a270f8b
The root cause was that after chunkDesc eviction, the offset between
memory representation of chunk layout (via chunkDescs in memory) was
shiftet against chunks as layed out on disk. Keeping the offset up to
date is by no means trivial, so this commit is pretty involved.
Also, found a race that for some reason didn't bite us so far:
Persisting chunks was completel unlocked, so if chunks were purged on
disk at the same time, disaster would strike. However, locking the
persisting of chunk revealed interesting dead locks. Basically, never
queue under the fp lock.
Change-Id: I1ea9e4e71024cabbc1f9601b28e74db0c5c55db8
Checkpointing interval is now a command line flag.
Along the way, several things were refactored.
- Restructure the way the storage is started and stopped..
- Number of series in checkpoint is now a uint64, not a varint.
(Breaks old checkpoints, needs wipe!)
- More consistent naming and order of methods.
Change-Id: I883d9170c9a608ee716bb0ab3d0ded8ca03760d9
Add gauge for chunks and chunkdescs in memory (backed by a global
variable to be used later not only for instrumentation but also for
memory management).
Refactored instrumentation code once more (instrumentation.go is back :).
Change-Id: Ife39947e22a48cac4982db7369c231947f446e17
- Staleness delta is no a proper function parameter and not replicated
from package ast.
- Named type 'chunks' replaced by explicit '[]chunk' to avoid confusion.
- For the same reason, replaced 'chunkDescs' by '[]*chunkDescs'.
- Verified that math.Modf is not a speed enhancement over conversion
(actually 5x slower).
- Renamed firstTimeField, lastTimeField into chunkFirstTime and
chunkLastTime.
- Verified unpin() is sufficiently goroutine-safe.
- Decided not to update archivedFingerprintToTimeRange upon series
truncation and added a rationale why.
Change-Id: I863b8d785e5ad9f71eb63e229845eacf1bed8534
- Head chunk persisting only happens in evictOlderThan, so do it
there. (With the previous code, it would never happen.)
- Raw accesses to chunkDesc.chunk are now done via isEvicted (with
locking).
Change-Id: I48b07b56dfea4899b50df159b4ea566954396fcd
Also, fix problems in shutdown.
Starting serving and shutdown still has to be cleaned up properly.
It's a mess.
Change-Id: I51061db12064e434066446e6fceac32741c4f84c
Some other improvements on the way, in particular codec -> codable
renaming and addition of LookupSet methods.
Change-Id: I978f8f3f84ca8e4d39a9d9f152ae0ad274bbf4e2
Most important, the heads file will now persist all the chunk descs,
too. Implicitly, it will serve as the persisted form of the
fp-to-series map.
Change-Id: Ic867e78f2714d54c3b5733939cc5aef43f7bd08d
BinaryMarshaler instead of encodable.
BinaryUnmarshaler instead of decodable.
Left 'codable' in place for lack of a better word.
Change-Id: I8a104be7d6db916e8dbc47ff95e6ff73b845ac22
Large delta values often imply a difference between a large base value
and the large delta value, potentially resulting in small numbers with
a huge precision error. Since large delta values need 8 bytes anyway,
we are not even saving memory.
As a solution, always save the absoluto value rather than a delta once
8 bytes would be needed for the delta. Timestamps are then saved as 8
byte integers, while values are always saved as float64 in that case.
Change-Id: I01100d600515e16df58ce508b50982ffd762cc49
Go downloads moved to a different URL and require following redirects
(curl's '-L' option) now.
Go 1.3 deliberately randomizes ranges over maps, which uncovered some
bugs in our tests. These are fixed too.
Change-Id: Id2d9e185d8d2379a9b7b8ad5ba680024565d15f4
- Always spell out the time unit (e.g. milliseconds instead of ms).
- Remove "_total" from the names of metrics that are not counters.
- Make use of the "Namespace" and "Subsystem" fields in the options.
- Removed the "capacity" facet from all metrics about channels/queues.
These are all fixed via command line flags and will never change
during the runtime of a process. Also, they should not be part of
the same metric family. I have added separate metrics for the
capacity of queues as convenience. (They will never change and are
only set once.)
- I left "metric_disk_latency_microseconds" unchanged, although that
metric measures the latency of the storage device, even if it is not
a spinning disk. "SSD" is read by many as "solid state disk", so
it's not too far off. (It should be "solid state drive", of course,
but "metric_drive_latency_microseconds" is probably confusing.)
- Brian suggested to not mix "failure" and "success" outcome in the
same metric family (distinguished by labels). For now, I left it as
it is. We are touching some bigger issue here, especially as other
parts in the Prometheus ecosystem are following the same
principle. We still need to come to terms here and then change
things consistently everywhere.
Change-Id: If799458b450d18f78500f05990301c12525197d3
The first sort in groupByFingerprint already ensures that all resulting sample
lists contain only one fingerprint. We also already assume that all
samples passed into AppendSamples (and thus groupByFingerprint) are
chronologically sorted within each fingerprint.
The extra chronological sort is thus superfluous. Furthermore, this
second sort didn't only sort chronologically, but also compared all
metric fingerprints again (although we already know that we're only
sorting within samples for the same fingerprint). This caused a huge
memory and runtime overhead.
In a heavily loaded real Prometheus, this brought down disk flush times
from ~9 minutes to ~1 minute.
OLD:
BenchmarkLevelDBAppendRepeatingValues 5 331391808 ns/op 44542953 B/op 597788 allocs/op
BenchmarkLevelDBAppendsRepeatingValues 5 329893512 ns/op 46968288 B/op 3104373 allocs/op
NEW:
BenchmarkLevelDBAppendRepeatingValues 5 299298635 ns/op 43329497 B/op 567616 allocs/op
BenchmarkLevelDBAppendsRepeatingValues 20 92204601 ns/op 1779454 B/op 70975 allocs/op
Change-Id: Ie2d8db3569b0102a18010f9e106e391fda7f7883
This fixes the problem where samples become temporarily unavailable for
queries while they are being flushed to disk. Although the entire
flushing code could use some major refactoring, I'm explicitly trying to
do the minimal change to fix the problem since there's a whole new
storage implementation in the pipeline.
Change-Id: I0f5393a30b88654c73567456aeaea62f8b3756d9
Move rulemanager to it's own package to break cicrular dependency.
Make NewTestTieredStorage available to tests, remove duplication.
Change-Id: I33b321245a44aa727bfc3614a7c9ae5005b34e03
This optimizes the runtime and memory allocation behavior for label matchers
other than type "Equal". Instead of creating a new set for every union of
fingerprints, this simply adds new fingerprints to the existing set to achieve
the same effect.
The current behavior made a production Prometheus unresponsive when running a
NotEqual match against the "instance" label (a label with high value
cardinality).
BEFORE:
BenchmarkGetFingerprintsForNotEqualMatcher 10 170430297 ns/op 39229944 B/op 40709 allocs/op
AFTER:
BenchmarkGetFingerprintsForNotEqualMatcher 5000 706260 ns/op 217717 B/op 1116 allocs/op
Change-Id: Ifd78e81e7dfbf5d7249e50ad1903a5d9c42c347a
This fixes https://github.com/prometheus/prometheus/issues/390
The cause for the deadlock was a lock semantic in Go that wasn't
obvious to me when introducing this bug:
http://golang.org/pkg/sync/#RWMutex.Lock
Key phrase: "To ensure that the lock eventually becomes available, a
blocked Lock call excludes new readers from acquiring the lock."
In the memory series storage, we have one function
(GetFingerprintsForLabelMatchers) acquiring an RLock(), which calls
another function also acquiring the same RLock()
(GetLabelValuesForLabelName). That normally doesn't deadlock, unless a
Lock() call from another goroutine happens right in between the two
RLock() calls, blocking both the Lock() and the second RLock() call from
ever completing.
GoRoutine 1 GoRoutine 2
======================================
RLock()
... Lock() [DEADLOCK]
RLock() [DEADLOCK] Unlock()
RUnlock()
RUnlock()
Testing deadlocks is tricky, but the regression test I added does
reliably detect the deadlock in the original code on my machine within a
normal concurrent reader/writer run duration of 250ms.
Change-Id: Ib34c2bb8df1a80af44550cc2bf5007055cdef413
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
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
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
The idiomatic pattern for signalling a one-time message to multiple
consumers from a single producer is as follows:
```
c := make(chan struct{})
w := new(sync.WaitGroup) // Boilerplate to ensure synchronization.
for i := 0; i < 1000; i++ {
w.Add(1)
go func() {
defer w.Done()
for {
select {
case _, ok := <- c:
if !ok {
return
}
default:
// Do something here.
}
}
}()
}
close(c) // Signal the one-to-many single-use message.
w.Wait()
```
Change-Id: I755f73ba4c70a923afd342a4dea63365bdf2144b
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
In the case that a getValuesAtIntervalOp's ExtractSamples() is called
with a current time after the last chunk time, we return without
extracting any further values beyond the last one in the chunk
(correct), but also without advancing the op's time (incorrect). This
leads to an infinite loop in renderView(), since the op is called
repeatedly without ever being advanced and consumed.
This adds handling for this special case. When detecting this case, we
immediately set the op to be consumed, since we would always get a value
after the current time passed in if there was one.
Change-Id: Id99149e07b5188d655331382b8b6a461b677005c
This fixes a bug where an interval op might advance too far past the end
of the currently extracted chunk, effectively skipping over relevant
(to-be-extracted) values in the subsequent chunk. The result: missing
samples at chunk boundaries in the resulting view.
Change-Id: Iebf5d086293a277d330039c69f78e1eaf084b3c8
This also fixes the compaction test, which before worked only because
the input sample sorting was accidentally equal to the resulting on-disk
sample sorting.
Change-Id: I2a21c4b46ba562424b27058fc02eba84fa6a6006
- Most of this is the actual regression test in tiered_test.go.
- Working on that regression tests uncovered problems in
tiered_test.go that are fixed in this commit.
- The 'op.consumed = false' line added to freelist.go was actually not
fixing a bug. Instead, there was no bug at all. So this commit
removes that line again, but adds a regression test to make sure
that the assumed bug is indeed not there (cf. freelist_test.go).
- Removed more code duplication in operation.go (following the same
approach as before, i.e. embedding op type A into op type B if
everything in A is the same as in B with the exception of String()
and ExtractSample()). (This change make struct literals for ops more
clunky, but that only affects tests. No code change whatsoever was
necessary in the actual code after this refactoring.)
- Fix another op leak in tiered.go.
Change-Id: Ia165c52e33290ad4f6aba9c83d92318d4f583517
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
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
