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Metrics: where and how
graphite-oriented story
• Vsevolod Polyakov
• Platform Engineer at Grammarly
Graphite
All whisper-based systems
Default graphite architecture
what?
• RRD-like (gram.ly/gfsx)
• so.it.is.my.metric → /so/it/is/my/metric.wsp
• Fixed retention (by namepattern)
• Fixed size (actually no)
Retention and size
• 1s:1d → 1 036 828 bytes
• 10s:10d → 1 036 828 bytes
• 1s:365d → 378 432 028 bytes (1 TB ~ 3 000)
• 10s:365d → 37 843 228 bytes (1 TB ~ 30 000)
whisper calc
Retention and size
• 10s:30d,1m:120d,10m:365d → 4 564 864 bytes
• 240 864 metrics in 1 TB
• aggregation: average, sum, min, max, and last.
• can be assign per metric
How
• terraform (https://www.terraform.io/)
• docker (https://www.docker.com/)
• ansible (https://www.ansible.com/)
• rocker (https://github.com/grammarly/rocker)
• rocker-compose (https://github.com/grammarly/rocker-compose)
Default graphite architecture
carbon-cache.py
• single-core
• many options in config file
• default
link
architecture
carbon-cache.py
Start load testing
• m4.xlarge instance (4 CPU, 16 GB ram, 256 GB disk EBS gp2)
• retentions = 1s:1d
• MAX_CACHE_SIZE, MAX_UPDATES_PER_SECOND, MAX_CR
• defaults
• almost 1.5h to get limit :(
carbon-cache.py cache size → 75k reqs
results
• 75 000 reqs max
• 60 000 reqs flagman speed
• IO :(
Try to tune!
• WHISPER_SPARSE_CREATE =
true (don’t allocate space on
creation) non-linear IO load.
• CACHE_WRITE_STRATEGY =
sorted (default)
cache size 1k → 195k reqs
results
• 120 000 reqs flagman speed
• cache flush problem :(
Try to tune!
• CACHE_WRITE_STRATEGY = max
will give a strong flush preference to
frequently updated metrics and will
also reduce random file-io.
from 1k to 150k
results
• 90 000 reqs flagman speed
• cache flush problem :(
Try to tune!
• CACHE_WRITE_STRATEGY =
naive just flush. Better with random
IO.
from 45k to 135k
results
• 120 000 reqs flagman speed
• still CPU
sorted
max
naive
• Maybe it’s IO EBS limitation? → 512 GB disk.
• No.
go-carbon
• multi-core single daemon
• written in golang
• not many options to tune :(
link
Start load testing
• m4.xlarge instance (4 CPU, 16 GB ram, 256 GB disk EBS gp2)
• retentions = 1s:1d
• max-size = 0
• max-updates-per-second = 0
• almost 1h to get limit :(
1k → 130k reqs ~3k/min
results
• 120 000 reqs flagman speed
• but it’s without sparse.
• try to implement
try to tune!
remaining := whisper.Size() - whisper.MetadataSize()
whisper.file.Seek(int64(remaining-1), 0)
whisper.file.Write([]byte{0})
chunkSize := 16384
zeros := make([]byte, chunkSize)
for remaining > chunkSize {
// if _, err = whisper.file.Write(zeros); err != nil {
// return nil, err
// }
remaining -= chunkSize
}
if _, err = whisper.file.Write(zeros[:remaining]); err != nil {
return nil, err
}
180 000 reqs !
try to tune!
• max update operation = 1500
results
• TLDR 210 000 - 240 000 reqs flagman speed
• 31 000 000 cache size!
try to tune!
• max update operation = 0
• input-buffer = 400 000
results
• 270 000 reqs flagman speed
• 10-20 million req cache size!
try to tune!
• vm.dirty_background_ratio=40
• vm.dirty_ratio=60
300 000 reqs
results
• 300 000 reqs flagman speed
• 180k+ reqs ±without cache
Re:Lays
Default graphite architecture
arch forward
arch namedregexp
arch hash
arch hash replicafactor: 2
carbon-relay.py
• twisted based
• native
Start load testing
• c4.xlarge instance (4 CPU, 7.5 GB ram)
• ~1 Gb lan
• default parameters
• hashing
• 10 connections
WTF!
carbon-relay-ng
• golang-based
• web-panel
• live-updates
• aggregators
• spooling
link
<150 000 reqs
carbon-c-relay
• written in C
• advanced cluster management
from 100 000 to 1 600 000 reqs
1 400 000 flagman speed. Or not?
So…
go-carbon + carbon-c-relay = ♡
BTW. influx, 130k reqs on cluster
influx
openTSDB
single instance + hbase cluster = upto 150k reqs
ALSO
• zipper:
• https://github.com/grobian/carbonserver
• https://github.com/grobian/carbonwriter
• https://github.com/dgryski/carbonzipper
• https://github.com/dgryski/carbonapi
• https://github.com/dgryski/carbonmem
• https://github.com/jssjr/carbonate
plans
• Cyanite, retest
• newTS
• openTSDB tuninig
• zipper tuning
feel free to ask
• Vsevolod Polyakov
• ctrlok@gmail.com
• skype: ctrlok1987
• github.com/ctrlok
• twitter.com/ctrlok
• slack: HangOps
• Gitter: dev_ua/devops
• skype: DevOps from Ukraine

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  1. последние 2,5 года работаю в граммарли разрабатываем крутые штуки, надо писать много метрик, бла бла бла
  2. очень простой, все знают. Можно сказать стандарт индустрии для метрик
  3. простота быстро читать, быстро писать
  4. отключил логи, у амазона странные EBS
  5. если кеш не сбрасывается то это плохо
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