- 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
This fixes https://github.com/prometheus/prometheus/issues/381.
For any stale series we dropped from memory, this bug caused us to also drop
any other series from the labelpair->fingerprints memory index if they had any
label/value-pairs in common with the intentionally dropped series.
To fix this issue more easily, I converted the labelpair->fingerprints index
map values to a utility.Set of clientmodel.Fingerprints. This makes handling
this index much easier in general.
Change-Id: If5e81e202e8c542261bbd9797aa1257376c5c074
Currently, rendering a view is capable of handling multiple ops for
the same fingerprint efficiently. However, this capability requires a
lot of complexity in the code, which we are not using at all because
the way we assemble a viewRequest will never have more than one
operation per fingerprint.
This commit weeds out the said capability, along with all the code
needed for it. It is still possible to have more than one operation
for the same fingerprint, it will just be handled in a less efficient
way (as proven by the unit tests).
As a result, scanjob.go could be removed entirely.
This commit also contains a few related refactorings and removals of
dead code in operation.go, view,go, and freelist.go. Also, the
docstrings received some love.
Change-Id: I032b976e0880151c3f3fdb3234fb65e484f0e2e5
We have seven different types all called like LevelDB.*Options. One
of them is the plain LevelDBOptions. All others are just wrapping that
type without adding anything except clunkier handling.
If there ever was a plan to add more specific options to the various
LevelDB.*Options types, history has proven that nothing like that is
going to happen anytime soon.
To keep the code a bit shorter and more focused on the real (quite
significant) complexities we have to deal with here, this commit
reduces all uses of LevelDBOptions to the actual LevelDBOptions type.
1576 fewer characters to read...
Change-Id: I3d7a2b7ffed78b337aa37f812c53c058329ecaa6
- 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
Problem description:
====================
If a rule evaluation referencing a metric/timeseries M happens at a time
when M doesn't have a memory timeseries yet, looking up the fingerprint
for M (via TieredStorage.GetMetricForFingerprint()) will create a new
Metric object for M which gets both: a) attached to a new empty memory
timeseries (so we don't have to ask disk for the Metric's fingerprint
next time), and b) returned to the rule evaluation layer. However, the
rule evaluation layer replaces the name label (and possibly other
labels) of the metric with the name of the recorded rule. Since both
the rule evaluator and the memory storage share a reference to the same
Metric object, the original memory timeseries will now also be
incorrectly renamed.
Fix:
====
Instead of storing a reference to a shared metric object, take a copy of
the object when creating an empty memory timeseries for caching
purposes.
Change-Id: I9f2172696c16c10b377e6708553a46ef29390f1e
The storage itself should be closed before any of the objects passed into it
are closed (otherwise closing the storage can randomly freeze). Defers are
executed in reverse order, so closing the storage should be the last of the
defer statements.
Change-Id: Id920318b876f5b94767ed48c81221b3456770620
This used to work with Go 1.1, but only because of a compiler bug.
The bug is fixed in Go 1.2, so we have to fix our code now.
Change-Id: I5a9f3a15878afd750e848be33e90b05f3aa055e1
Prometheus needs long-term storage. Since we don't have enough resources
to build our own timeseries storage from scratch ontop of Riak,
Cassandra or a similar distributed datastore at the moment, we're
planning on using OpenTSDB as long-term storage for Prometheus. It's
data model is roughly compatible with that of Prometheus, with some
caveats.
As a first step, this adds write-only replication from Prometheus to
OpenTSDB, with the following things worth noting:
1)
I tried to keep the integration lightweight, meaning that anything
related to OpenTSDB is isolated to its own package and only main knows
about it (essentially it tees all samples to both the existing storage
and TSDB). It's not touching the existing TieredStorage at all to avoid
more complexity in that area. This might change in the future,
especially if we decide to implement a read path for OpenTSDB through
Prometheus as well.
2)
Backpressure while sending to OpenTSDB is handled by simply dropping
samples on the floor when the in-memory queue of samples destined for
OpenTSDB runs full. Prometheus also only attempts to send samples once,
rather than implementing a complex retry algorithm. Thus, replication to
OpenTSDB is best-effort for now. If needed, this may be extended in the
future.
3)
Samples are sent in batches of limited size to OpenTSDB. The optimal
batch size, timeout parameters, etc. may need to be adjusted in the
future.
4)
OpenTSDB has different rules for legal characters in tag (label) values.
While Prometheus allows any characters in label values, OpenTSDB limits
them to a to z, A to Z, 0 to 9, -, _, . and /. Currently any illegal
characters in Prometheus label values are simply replaced by an
underscore. Especially when integrating OpenTSDB with the read path in
Prometheus, we'll need to reconsider this: either we'll need to
introduce the same limitations for Prometheus labels or escape/encode
illegal characters in OpenTSDB in such a way that they are fully
decodable again when reading through Prometheus, so that corresponding
timeseries in both systems match in their labelsets.
Change-Id: I8394c9c55dbac3946a0fa497f566d5e6e2d600b5
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
This fixes part 2) of https://github.com/prometheus/prometheus/issues/367
(uninitialized time.Time mapping to a higher LevelDB key than "normal"
timestamps).
Change-Id: Ib079974110a7b7c4757948f81fc47d3d29ae43c9
This fixes part 1) of https://github.com/prometheus/prometheus/issues/367 (the
storing of samples with the wrong fingerprint into a compacted chunk, thus
corrupting it).
Change-Id: I4c36d0d2e508e37a0aba90b8ca2ecc78ee03e3f1
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
While a hack, this change should allow us to serve queries
expeditiously during a flush operation.
Change-Id: I9a483fd1dd2b0638ab24ace960df08773c4a5079
The background curation should be staggered to ensure that disk
I/O yields to user-interactive operations in a timely manner. The
lack of routine prioritization necessitates this.
Change-Id: I9b498a74ccd933ffb856e06fedc167430e521d86
Move the stream to an interface, for a number of additional changes
around it are underway.
Conflicts:
storage/metric/memory.go
Change-Id: I4a5fc176f4a5274a64ebdb1cad52600954c463c3
AppendSample will be repcated with AppendSamples, which will take
advantage of bulks appends. This is a necessary step for indexing
pipeline decoupling.
Change-Id: Ia83811a87bcc89973d3b64d64b85a28710253ebc
This commit is the first of several and should not be regarded as the
desired end state for these cleanups. What this one does it, however,
is wrap the query index writing behind an interface type that can be
injected into the storage stack and have its lifecycle managed
separately as needed. It also would mean we can swap out underlying
implementations to support remote indexing, buffering, no-op indexing
very easily.
In the future, most of the individual index interface members in the
tiered storage will go away in favor of agents that can query and
resolve what they need from the datastore without the user knowing
how and why they work.
There are too many parameters to constructing a LevelDB storage
instance for a construction method, so I've opted to take an
idiomatic approach of embedding them in a struct for easier
mediation and versioning.
When samples get flushed to disk, they lose sub-second precision anyways. By
already dropping sub-second precision, data fetched from memory vs. disk will
behave the same. Later, we should consider also storing a more compact
representation than time.Time in memory if we're not going to use its full
precision.
Current series always get watermarks written out upon append now. This
drops support for old series without any watermarks by always reporting
them as too old (stale) during 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.
