Commit 4e15503b authored by Björn Rabenstein's avatar Björn Rabenstein Committed by GitHub

Merge pull request #706 from prometheus/beorn7/storage

Update storage documentation for the Prometheus 1.6 release
parents c2ef91ea d670cb17
......@@ -442,8 +442,6 @@ files:
Where `<filename_pattern>` may be a path ending in `.json`, `.yml` or `.yaml`. The last path segment
may contain a single `*` that matches any character sequence, e.g. `my/path/tg_*.json`.
NOTE: Prior to v0.20, `names:` was used instead of `files:`.
### `<gce_sd_config>`
CAUTION: GCE SD is in beta: breaking changes to configuration are still
......@@ -1063,4 +1061,4 @@ tls_config:
# Optional proxy URL.
[ proxy_url: <string> ]
```
\ No newline at end of file
```
......@@ -12,43 +12,47 @@ sample data, it has its own custom storage layer, which organizes
sample data in chunks of constant size (1024 bytes payload). These
chunks are then stored on disk in one file per time series.
## Memory usage
Prometheus keeps all the currently used chunks in memory. In addition,
it keeps the most recently used chunks in memory up to a threshold
configurable via the `storage.local.memory-chunks` flag. If you have a
lot of RAM available, you might want to increase it above the default
value of 1048576 (and vice versa, if you run into RAM problems, you
can try to decrease it). Note that the actual RAM usage of your server
will be much higher than what you would expect from multiplying
`storage.local.memory-chunks` by 1024 bytes. There is inevitable
overhead for managing the sample data in the storage layer. Also, your
server is doing many more things than just storing samples. The actual
overhead depends on your usage pattern. In extreme cases, Prometheus
has to keep more chunks in memory than configured because all those
chunks are in use at the same time. You have to experiment a bit. The
metrics `prometheus_local_storage_memory_chunks` and
`process_resident_memory_bytes`, exported by the Prometheus server,
will come in handy. As a rule of thumb, you should have at least three
times more RAM available than needed by the memory chunks alone.
This sections deals with the various configuration settings and issues you
might run into. To dive deeper into the topic, check out the following talks:
* [The Prometheus Time Series Database](https://www.youtube.com/watch?v=HbnGSNEjhUc).
* [Configuring Prometheus for High Performance](https://www.youtube.com/watch?v=zop94VbeFeU).
PromQL queries that involve a high number of time series will make heavy use of
the LevelDB backed indexes. If you need to run queries of that kind, tweaking
the index cache sizes might be required. The following flags are relevant:
## Memory usage
* `-storage.local.index-cache-size.label-name-to-label-values`: For regular
expression matching.
* `-storage.local.index-cache-size.label-pair-to-fingerprints`: Increase the
size if a large number of time series share the same label pair or name.
* `-storage.local.index-cache-size.fingerprint-to-metric` and
`-storage.local.index-cache-size.fingerprint-to-timerange`: Increase the size
if you have a large number of archived time series, i.e. series that have not
received samples in a while but are still not old enough to be purged
completely.
You have to experiment with the flag values to find out what helps. If a query
touches 100,000+ time series, hundreds of MiB might be reasonable. If you have
plenty of free memory available, using more of it for LevelDB cannot harm.
Prometheus keeps all the currently used chunks in memory. In addition, it keeps
as many most recently used chunks in memory as possible. You have to tell
Prometheus how much memory it may use for this caching. The flag
`storage.local.target-heap-size` allows you to set the heap size (in bytes)
Prometheus aims not to exceed. Note that the amount of physical memory the
Prometheus server will use is the result of complex interactions of the Go
runtime and the operating system and very hard to predict precisely. As a rule
of thumb, you should have at least 50% headroom in physical memory over the
configured heap size. (Or, in other words, set `storage.local.target-heap-size`
to a value of two thirds of the physical memory limit Prometheus should not
exceed.)
The default value of `storage.local.target-heap-size` is 2GiB and thus tailored
to 3GiB of physical memory usage. If you have less physical memory available,
you have to lower the flag value. If you have more memory available, you should
raise the value accordingly. Otherwise, Prometheus will not make use of the
memory and thus will perform much worse than it could.
Because Prometheus uses most of its heap for long-lived allocations of memory
chunks, the
[garbage collection target percentage](https://golang.org/pkg/runtime/debug/#SetGCPercent)
is set to 40 by default. You can still override this setting via the `GOGC`
environment variable as usual.
For high-performance set-ups, you might need to adjust more flags. Please read
through the sections below for details.
NOTE: Prior to v1.6, there was no flag `storage.local.target-heap-size`.
Instead, the number of chunks kept in memory had to be configured using the
flags `storage.local.memory-chunks` and `storage.local.max-chunks-to-persist`.
These flags still exist for compatibility reasons. However,
`storage.local.max-chunks-to-persist` has no effect anymore, and if
`storage.local.memory-chunks` is set to a non-zero value _x_, it is used to
override the value for `storage.local.target-heap-size` to 3072*_x_.
## Disk usage
......@@ -101,55 +105,81 @@ experimenting with your own use case is encouraged. Take into account, however,
that only newly created chunks will use the newly selected chunk encoding, so
it will take a while until you see the effects.
For more details about the trade-off between the chunk encodings, see
[this blog post](/blog/2016/05/08/when-to-use-varbit-chunks/).
## Settings for high numbers of time series
Prometheus can handle millions of time series. However, you have to adjust the
storage settings to handle much more than 100,000 active time
series. Essentially, you want to allow a certain number of chunks for each time
series to be kept in RAM. The default value for the
`storage.local.memory-chunks` flag (discussed above) is 1048576. Up to about
300,000 series, you still have three chunks available per series on
average. For more series, you should increase the `storage.local.memory-chunks`
value. Three times the number of series is a good first approximation. But keep
the implication for memory usage (see above) in mind.
If you have more active time series than configured memory chunks, Prometheus
will inevitably run into a situation where it has to keep more chunks in memory
than configured. If the number of chunks goes more than 10% above the
configured limit, Prometheus will throttle ingestion of more samples (by
skipping scrapes and rule evaluations) until the configured value is exceeded
by less than 5%. _Throttled ingestion is really bad for various reasons. You
really do not want to be in that situation._
Equally important, especially if writing to a spinning disk, is raising the
value for the `storage.local.max-chunks-to-persist` flag. As a rule of thumb,
keep it around 50% of the `storage.local.memory-chunks`
value. `storage.local.max-chunks-to-persist` controls how many chunks can be
waiting to be written to your storage device, may it be spinning disk or SSD
(which contains neither a disk nor a drive motor but we will refer to it as
“disk“ for the sake of simplicity). If that number of waiting chunks is
exceeded, Prometheus will once more throttle sample ingestion until the number
has dropped to 95% of the configured value. Before that happens, Prometheus
will try to speed up persisting chunks. See the
[section about persistence pressure](#persistence-pressure-and-rushed-mode)
below.
The more chunks you can keep in memory per time series, the more write
operations can be batched, which is especially important for spinning
disks. Note that each active time series will have an incomplete head chunk,
which cannot be persisted yet. It is a chunk in memory, but not a “chunk to
persist” yet. If you have 1M active time series, you need 3M
`storage.local.memory-chunks` to have three chunks for each series
available. Only 2M of those can be persistable, so setting
`storage.local.max-chunks-to-persist` to more than 2M can easily lead to more
than 3M chunks in memory, despite the setting for
`storage.local.memory-chunks`, which again will lead to the dreaded throttling
of ingestion (but Prometheus will try its best to speed up persisting of chunks
before it happens).
The other drawback of a high value of chunks waiting for persistence is larger
checkpoints.
Prometheus can handle millions of time series. However, with the above
mentioned default setting for `storage.local.target-heap-size`, you will be
limited to about 200,000 time series simultaneously present in memory. For more
series, you need more memory, and you need to configure Prometheus to make use
of it as described above.
Each of the aforementioned chunks contains samples of a single time series. A
time series is thus represented as a series of chunks, which ultimately end up
in a time series file (one file per time series) on disk.
A series that has recently received new samples will have an open incomplete
_head chunk_. Once that chunk is completely filled, or the series hasn't
received samples in a while, the head chunk is closed and becomes a chunk
waiting to be appended to its corresponding series file, i.e. it is _waiting
for persistence_. After the chunk has been persisted to disk, it becomes
_evictable_, provided it is not currently used by a query. Prometheus will
evict evictable chunks from memory to satisfy the configured target heap
size. A series with an open head chunk is called an _active series_. This is
different from a _memory series_, which also includes series without an open
head chunk but still other chunks in memory (whether waiting for persistence,
used in a query, or evictable). A series without any chunks in memory may be
_archived_, upon which it ceases to have any mandatory memory footprint.
The amount of chunks Prometheus can keep in memory depends on the flag value
for `storage.local.target-heap-size` and on the amount of memory used by
everything else. If there are not enough chunks evictable to satisfy the target
heap size, Prometheus will throttle ingestion of more samples (by skipping
scrapes and rule evaluations) until the heap has shrunk enough. _Throttled
ingestion is really bad for various reasons. You really do not want to be in
that situation._
Open head chunks, chunks still waiting for persistence, and chunks being used
in a query are not evictable. Thus, the reasons for the inability to evict
enough chunks include the following:
1. Queries that use too many chunks.
2. Chunks are piling up waiting for persistence because the storage layer
cannot keep up writing chunks.
3. There are too many active time series, which results in too many open head
chunks.
Currently, Prometheus has no defence against case (1). Abusive queries will
essentially OOM the server.
To defend against case (2), there is a concept of persistence urgency explained
in the next section.
Case (3) depends on the targets you monitor. To mitigate an unplanned explosion
of the number of series, you can limit the number of samples per individual
scrape (see `sample_limit` in the
[scrape config](https://prometheus.io/docs/operating/configuration/#scrape_config)).
If the number of active time series exceeds the number of memory chunks the
Prometheus server can afford, the server will quickly throttle ingestion as
described above. The only way out if this is to give Prometheus more RAM or
reduce the number of time series to ingest.
In fact, you want many more memory chunks than you have series in
memory. Prometheus tries to batch up disk writes as much as possible as it
helps for both HDD (write as much as possible after each seek) and SSD (tiny
writes create write amplification, which limits the effective throughput and
burns much more quickly through the lifetime of the device). The more
Prometheus can batch up writes, the more efficient is the process of persisting
chunks to disk. which helps case (2).
In conclusion, to keep the Prometheus server healthy, make sure it has plenty
of headroom of memory chunks available for the number of memory series. A
factor of three is a good starting point. Refer to the
[section about helpful metrics](#helpful-metrics) to find out what to look
for. A very broad rule of thumb for an upper limit of memory series is the
total available physical memory divided by 10,000, e.g. About 6M memory series
on a 64GiB server.
If you combine a high number of time series with very fast and/or large
scrapes, the number of pre-allocated mutexes for series locking might not be
......@@ -158,16 +188,35 @@ or processing expensive queries, try increasing the value of the
`storage.local.num-fingerprint-mutexes` flag. Sometimes tens of thousands or
even more are required.
## Persistence pressure and “rushed mode”
PromQL queries that involve a high number of time series will make heavy use of
the LevelDB-backed indexes. If you need to run queries of that kind, tweaking
the index cache sizes might be required. The following flags are relevant:
* `-storage.local.index-cache-size.label-name-to-label-values`: For regular
expression matching.
* `-storage.local.index-cache-size.label-pair-to-fingerprints`: Increase the
size if a large number of time series share the same label pair or name.
* `-storage.local.index-cache-size.fingerprint-to-metric` and
`-storage.local.index-cache-size.fingerprint-to-timerange`: Increase the size
if you have a large number of archived time series, i.e. series that have not
received samples in a while but are still not old enough to be purged
completely.
You have to experiment with the flag values to find out what helps. If a query
touches 100,000+ time series, hundreds of MiB might be reasonable. If you have
plenty of memory available, using more of it for LevelDB cannot harm. More
memory for LevelDB will effectively reduce the number of memory chunks
Prometheus can afford.
## Persistence urgency and “rushed mode”
Naively, Prometheus would all the time try to persist completed chunk to disk
as soon as possible. Such a strategy would lead to many tiny write operations,
using up most of the I/O bandwidth and keeping the server quite busy. Spinning
disks are more sensitive here, but even SSDs will not like it. Prometheus tries
instead to batch up write operations as much as possible, which works better if
it is allowed to use more memory. Setting the flags described above to values
that lead to full utilization of the available memory is therefore crucial for
high performance.
disks will appear to be very slow because of the many slow seeks required, and
SSDs will suffer from write amplification. Prometheus tries instead to batch up
write operations as much as possible, which works better if it is allowed to
use more memory.
Prometheus will also sync series files after each write (with
`storage.local.series-sync-strategy=adaptive`, which is the default) and use
......@@ -176,16 +225,12 @@ series”, see [below](#crash-recovery)), both attempting to minimize data loss
in case of a crash.
But what to do if the number of chunks waiting for persistence grows too much?
Prometheus calculates a score for urgency to persist chunks, which depends on
the number of chunks waiting for persistence in relation to the
`storage.local.max-chunks-to-persist` value and on how much the number of
chunks in memory exceeds the `storage.local.memory-chunks` value (if at all,
and only if there is a minimum number of chunks waiting for persistence so that
faster persisting of chunks can help at all). The score is between 0 and 1,
where 1 corresponds to the highest urgency. Depending on the score, Prometheus
will write to disk more frequently. Should the score ever pass the threshold
of 0.8, Prometheus enters “rushed mode” (which you can see in the logs). In
rushed mode, the following strategies are applied to speed up persisting chunks:
Prometheus calculates a score for urgency to persist chunks. The score is
between 0 and 1, where 1 corresponds to the highest urgency. Depending on the
score, Prometheus will write to disk more frequently. Should the score ever
pass the threshold of 0.8, Prometheus enters “rushed mode” (which you can see
in the logs). In rushed mode, the following strategies are applied to speed up
persisting chunks:
* Series files are not synced after write operations anymore (making better use
of the OS's page cache at the price of an increased risk of losing data in
......@@ -200,6 +245,13 @@ rushed mode, the following strategies are applied to speed up persisting chunks:
Prometheus leaves rushed mode once the score has dropped below 0.7.
Throttling of ingestion happens if the urgency score reaches 1. Thus, the
rushed mode is not _per se_ something to be avoided. It is, on the contrary, a
measure the Prometheus server takes to avoid the really bad situation of
throttled ingestion. Occasionally entering rushed mode is OK, if it helps and
ultimately leads to leaving rushed mode again. _If rushed mode is entered but
the urgency score still goes up, the server has a real problem._
## Settings for very long retention time
If you have set a very long retention time via the `storage.local.retention`
......@@ -216,33 +268,26 @@ flag. If you have a lot of disk space but want to minimize rewrites (at the
cost of wasted disk space), increase the flag value to higher values, e.g. 0.3
for 30% of required chunk removal.
## Helpful metrics
Out of the metrics that Prometheus exposes about itself, the following are
particularly useful for tuning the flags above:
* `prometheus_local_storage_memory_series`: The current number of series held
in memory.
* `prometheus_local_storage_memory_chunks`: The current number of chunks held
in memory.
* `prometheus_local_storage_chunks_to_persist`: The number of memory chunks
that still need to be persisted to disk.
* `prometheus_local_storage_persistence_urgency_score`: The urgency score as
discussed [above](#persistence-pressure-and-rushed-mode).
* `prometheus_local_storage_rushed_mode` is 1 if Prometheus is in “rushed
mode”, 0 otherwise.
## Crash recovery
Prometheus saves chunks to disk as soon as possible after they are
complete. Incomplete chunks are saved to disk during regular
checkpoints. You can configure the checkpoint interval with the flag
`storage.local.checkpoint-interval`. Prometheus creates checkpoints
more frequently than that if too many time series are in a "dirty"
more frequently than that if too many time series are in a “dirty”
state, i.e. their current incomplete head chunk is not the one that is
contained in the most recent checkpoint. This limit is configurable
via the `storage.local.checkpoint-dirty-series-limit` flag.
More active time series to cycle through lead in general to more chunks waiting
for persistence, which in turns leads to larger checkpoints and ultimately more
time needed for checkpointing. There is a clear trade-off between limiting the
loss of data in case of a crash and the ability to scale to high number of
active time series. To not spend the majority of the disk throughput for
checkpointing, you have to increase the checkpoint interval. Prometheus itself
limits the time spent in checkpointing to 50% by waiting after each
checkpoint's completion for at least as long as the previous checkpoint took.
Nevertheless, should your server crash, you might still lose data, and
your storage might be left in an inconsistent state. Therefore,
Prometheus performs a crash recovery after an unclean shutdown,
......@@ -253,7 +298,9 @@ that cannot be recovered is moved to a directory called `orphaned`
you do not need it anymore.
The crash recovery usually takes less than a minute. Should it take much
longer, consult the log to find out what has gone wrong.
longer, consult the log to find out what is going on. With increasing number of
time series in the storage (archived or not), the re-indexing tends to dominate
the recovery time and can take tens of minutes in extreme cases.
## Data corruption
......@@ -268,3 +315,42 @@ storage directory:
1. Stop Prometheus.
1. `rm -r <storage path>/*`
1. Start Prometheus.
## Helpful metrics
Out of the metrics that Prometheus exposes about itself, the following are
particularly useful to tweak flags and find out about the required
resources. They also help to create alerts to find out in time if a Prometheus
server has problems or is out of capacity.
* `prometheus_local_storage_memory_series`: The current number of series held
in memory.
* `prometheus_local_storage_open_head_chunks`: The number of open head chunks.
* `prometheus_local_storage_chunks_to_persist`: The number of memory chunks
that still need to be persisted to disk.
* `prometheus_local_storage_memory_chunks`: The current number of chunks held
in memory. If you substract the previous two, you get the number of persisted
chunks (which are evictable if not currently in use by a query).
* `prometheus_local_storage_series_chunks_persisted`: A histogram of the number
of chunks persisted per batch.
* `prometheus_local_storage_persistence_urgency_score`: The urgency score as
discussed [above](#persistence-pressure-and-rushed-mode).
* `prometheus_local_storage_rushed_mode` is 1 if Prometheus is in “rushed
mode”, 0 otherwise. Can be used to calculate the percentage of time
Prometheus is in rushed mode.
* `prometheus_local_storage_checkpoint_last_duration_seconds`: How long the
last checkpoint took.
* `prometheus_local_storage_checkpoint_last_size_bytes`: Size of the last
checkpoint in bytes.
* `prometheus_local_storage_checkpointing` is 1 while Prometheus is
checkpointing, 0 otherwise. Can be used to calculate the percentage of time
Prometheus is checkpointing.
* `prometheus_local_storage_inconsistencies_total`: Counter for storage
inconsistencies found. If this is greater than 0, restart the server for
recovery.
* `prometheus_local_storage_persist_errors_total`: Counter for persist errors.
* `prometheus_local_storage_memory_dirty_series`: Current number of dirty series.
* `process_resident_memory_bytes`: Broadly speaking the physical memory
occupied by the Prometheus process.
* `go_memstats_alloc_bytes`: Go heap size (allocated objects in use plus allocated
objects not in use anymore but not yet garbage-collected).
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment