PDX DevOps Graphite replacement
-
Upload
nickchappell -
Category
Technology
-
view
8.822 -
download
1
description
Transcript of PDX DevOps Graphite replacement
Riemann + InfluxDB + Grafana
An easier to deploy and (hopefully) better performing replacement for Graphite
Me
Sysadmin at NetXposure Inc.First post-college job
https://github.com/nickchappell
Graphite
Other tools in the graphing/metrics space
Old school toolsRRD for data storage
RRDtool for generating graphs
Cacti for managing dashboards of RRD
graphs
Graphite architecture3 main components
whisper
graphite-web
Carbon takes in metrics
Whisper stores metrics
graphite-web retrieves metrics
carbon-cache/carbon-relay
API
Other dashboards
GraphiteWhat’s wrong with it?
A few different things, mainly with 2 parts of the stack
carbon-cache/carbon-relay whisper
graphite-web
graphite-webGraphite web is actually quite good and featureful (is that a word?) as an API
The built-in dashboard and graph builder isn’t very stylish, but is great for exploring and finding out what metrics you want to query via the API or graph in another dashboard
tool
What’s wrong with Whisper?Disk IO problems when running queries for lots of metrics
!graphite-web ends up having to touch lots and lots of files
What’s wrong with carbon?The stock Python interpreter, CPython, specifically, the GIL
(global interpreter lock)
Multiple threads are not allowed to execute native code at the same time
https://wiki.python.org/moin/GlobalInterpreterLock
Why? According to the Python folks, memory management is not thread safe
Your time series data is getting munged on somewhere in here
Does Graphite have any redeeming qualities?
The format that Graphite receives metrics in is dead-simple
metric_name value timestamp\n
foo.bar.baz 42 74857843
skynet.cyberdyne.mil.cpu-system 423 74857843
Metrics formatsFor better or worse, Graphite’s format is the lingua franca of
the metrics world right now
Most everything that outputs metrics can output them in Graphite’s format
Scaling GraphiteSome strategies include…
Split up the roles for Carbon into Carbon relays and Carbon caches
…decoupling components
Get a faster CPU
…throwing more hardware at it
Get faster disks (SSDs or set up RAM drives)
Put HAproxy in front of a few Carbon instances to spread the load around
To this end, set up multiple Carbon instances on the same machine, listening on different ports
and tie them to separate CPU cores
What are other people doing?
Some people are trying to rewrite parts of the stack
Others have set up some pretty impressive (but complex) Graphite architectures (next few slides)...
Dieterbe rewrote carbon-cache and carbon-relay in Go: https://github.com/graphite-ng/
http://grey-boundary.com/the-architecture-of-clustering-graphite/
http://adminberlin.de/graphite-scale-out/
http://librelist.com/browser/graphite/2013/9/10/graphite-carbon-using-apache-cassandra-via-backend-database-
plugin/
What are the Graphite people doing?
The Graphite folks have 2 projects in the works to overcome some of its problems
megacarbon: replacement for Carbon, supposedly will perform better
ceres: replacement for Whisper, supposedly will perform better and natively allow writes from multiple carbon/
megacarbon instances
Will these help?Probably not.
megacarbon is a branch of the main carbon repository that is ahead of carbon’s master branch, but hasn’t yet been merged
back in and may never be
ceres hasn’t been touched since December 2013
For that matter, whisper hasn’t been touched since January 2014
One more thing….Graphite is a PITA to install/deploy
It’s somewhat easier now that carbon, whisper and graphite-web are available in pip
Manual setup: • use apt/yum to install a bunch of pre-
requisite Python packages • set up Apache/Nginx with WSGI or Gunicorn
to run graphite-web, which is actually a Django app
• BYOIS/UF
Before, you had to clone each component’s Git repo and check out a tagged release
3 newer metrics/monitoring tools
Logstash
Heka
Riemann
Logstash
Accepts logs, processes and stores them in Elasticsearch
A web app, Kibana, accesses the index data in Elasticsearch
Sound familiar?
There are tons of other ways to use it, though!
Heka
Newest monitoring tool of the bunchWritten in Go
https://github.com/mozilla-services/heka
RiemannAn “event stream” processor
http://riemann.io
What is an event?“Event stream processor” sounds really abstract
Any data your systems or application could emit, like syslog messages, stack traces,
metrics, etc. are events
Riemann can have lots of overlap with Logstash when it comes to data that’s primarily
text
Riemann does for events what Logstash does for logs (aggregates and processes them,
sends them off elsewhere)
RiemannCan take in almost anything (log line, Graphite-format metric,
etc.)
Like Logstash/Heka, can process it and then send it elsewhere
Outputs include…
…IM like IRC
…a more traditional monitoring system like Nagios/Icinga
Riemann and HipChat
https://github.com/aphyr/riemann/blob/master/src/riemann/hipchat.clj
Riemann and Slack
https://github.com/aphyr/riemann/blob/master/src/riemann/slack.clj
Riemann has packages!One advantage over carbon-cache/relay: Riemann
has Debian and RPM packages, including init scripts!
The only dependency Riemann has is the JDK itself
RiemannWritten in Clojure, a functional programming language
Riemann being written in Clojure is kinda neat, but what makes it special is what Clojure runs on: the JVM
Clojure also brings one benefit that Graphite desperately needs: safe threading!
Unlike CPython, the JVM can actually run more than 1 thread at a time and can use more than 1 CPU
core
Clojure has software transactional memory and other tools for parallel/concurrent programming
Riemann events
Like Logstash, events in Riemann are pieces of text with multiple fields
{ :host riemann1.local, :service cpu-0.cpu-wait, :metric 3.399911, :tags collectd, :time 1405715017, :ttl 30 }
The Riemann indexRiemann can store incoming events in memory in the index
Events can be given a TTL
(in seconds)…
...and removed with the periodically-expire function:
{ :host riemann1.local, :service cpu-0.cpu-wait, :metric 3.399911, :tags collectd, :time 1405715017, :ttl 60 }
;Scan the index for expired events every 30 seconds: (periodically-expire 30)
Events in the index are what we can use for alerting!(I’ll mention possible tool integrations later on…)
Riemann configsRiemann configs are Clojure programs
https://github.com/nickchappell/vagrantfiles/blob/master/metrics/riemann/files/riemann/configs/riemann.config
Riemann as part of a Graphite replacement
Riemann can actually take Graphite-format metrics as an input
http://riemann.io/api/riemann.transport.graphite.html
Can Riemann be used in the place of Carbon?
Yes. It can be instructed to listen on arbitrary TCP and UDP ports
Riemann graphite-server config
TCP and UDP
graphite-server functions
Other parts of the stackSo, we have Carbon replaced. What else do we need?
A replacement for Whisper
A replacement for the API component of graphite-web
A replacement for the web UI component of graphite-web
InfluxDB
A time series database written in Go
InfluxDB specifics
Some conceptual similarities to SQL DBs:
A database in InfluxDB is just like a database in a SQL system
A series in InfluxDB is like a table in SQL
A point or event in a series is like a row in a table
Points can have columns of values
Points in a series don’t all have to have the same columns, so InfluxDB is sort of schema-less
InfluxDB storage engines
RocksDB and HyperLevelDB are based on LevelDB
All 3 LevelDB variants compress data on disk as its written (LMDB doesn't)
All 3 LevelDB variants can shrink storage engine files when getting rid of old metrics (LMDB deletes don't
actually reclaim disk space)
Uses LevelDB as the underlying storage engine (can also be configured to use RocksDB, HyperLevelDB
or LMDB)
InfluxDB is going to move to RocksDB as the default in the next version
http://influxdb.com/blog/2014/06/20/leveldb_vs_rocksdb_vs_hyperleveldb_vs_lmdb_performance.html
Storage engine benchmarks:
InfluxDB storage engines
SQL-ish query langageHas a SQL-like query language
select value from response_times!where time > '2013-08-12 23:32:01.232' and time < '2013-08-13';
Selecting with date/time ranges:
select value from response_times where time > now() - 1h limit 1000;Relative time ranges and limits:
select * from events!where (email =~ /.*gmail.* or email =~ /.*yahoo.*/) and state = 'ny';
Regexes and compound where statements:
select hosta.value + hostb.value!from cpu_load as hosta!inner join cpu_load as hostb!where hosta.host = 'hosta.influxdb.orb' and hostb.host = 'hostb.influxdb.org';
Joins:
http://influxdb.com/docs/v0.8/api/query_language.html
InfluxDB advantages over Whisper
All of the underlying storage engines can perform better than Whisper
InfluxDB has Debian and RPM packages! (with init scripts too!)
Can InfluxDB replace Whisper?Yes!
One particular feature it has that Whisper does not is clustering
A group of InfluxDB nodes can communicate via a Raft-based protocol to coordinate writes and reads and split up data into
shards
Whisper is still single-instance only
InfluxDB replaces Whisper for storage and graphite-web for metric retrieval
InfluxDB disadvantages
API and built-in math functions are not as featureful as graphite-web, at least not yet
This can be solved with development work, and unlike the Graphite projects, InfluxDB is being actively developed!
Riemann + InfluxDBGuess what Riemann can write data out to?
InfluxDB data partitioningDo I store1 series per host? 1 series per metric? 1 series per
hostname + metric name combo?
InfluxDB works best with large numbers of series with fewer columns in each one
Why? Points are indexed by time, not by any other columns.
Arbitrary column indexes are going to be added in the future, though
InfluxDB data partitioning
Time Name Host Metric Service32141234 cpu web0
178 cpu
32141235 disk_io web02
98844 disk_io32141236 load db1 5 load32141237 eth0_in ldap0
35875 eth0_in
Time Name Host Metric Service32141234 cpu web01 78 cpu32141235 cpu web01 45 cpu32141236 cpu web01 38 cpu32141237 cpu web01 92 cpu
Time Name Host Metric Service32141234 disk_io web01 87323 disk_io32141235 disk_io web01 98844 disk_io32141236 disk_io web01 9233 disk_io32141237 disk_io web01 93262 disk_io
Bad: only using 1 series in a DB
Good: Using multiple series in a DB
Time Name Host Metric Service32141234 cpu web02 78 cpu32141235 cpu web02 45 cpu32141236 cpu web02 38 cpu32141237 cpu web02 92 cpu
Time Name Host Metric Service32141234 disk_io web02 87323 disk_io32141235 disk_io web02 98844 disk_io32141236 disk_io web02 9233 disk_io32141237 disk_io web02 93262 disk_io
InfluxDB data partitioningIsn't a series for every host+service combo excessive?
Because of the way InfluxDB's storage engines
work, no!
We can include the series name as the first part of our
query:
By doing that, InfluxDB can ignore all of the other data in the other series and will only have to access 1 LevelDB/RocksDB file on disk per Grafana query
(show Riemann config that creates a series per host+service)
InfluxDB data partitioning
By doing this, we’re not really querying by hostname or metric name, ie. querying
by those columns
It’s kind of a hack, but it takes advantage of the characteristics of how
InfluxDB’s storage engines work
InfluxDB data partitioningI did this originally to get
around a quirk of Grafana’s UI
For InfluxDB data sources, Grafana doesn’t let you use where or do selects by
more than 1 column
Without splitting data up into more than 1 series, there’s no way to get metric values for an individual host, metric
or host+metric combo
Riemann and InfluxDB data partitioningRiemann’s built-in InfluxDB output function looks like this:
Riemann and InfluxDB data partitioning :series #(str (:host %) "." (:service %))
…tells Riemann to take this:{ :host riemann1.local, :service cpu-0.cpu-wait, :metric 3.399911, :tags collectd, :time 1405715017, :ttl 30 }
…and write it to InfluxDB with riemann1.local.cpu-0.cpu-wait as the automatically generated series name
InfluxDB behaves like Graphite with new metrics: it will automatically create a new series if it’s for a hostname.metric
combo it doesn’t already have a series for
Synchronous vs asynchronous InfluxDB writes
The included InfluxDB writer opens up a new socket to InfluxDB for EVERY single data
point that gets written!
7 VMs with 8-9 collectd plugins enabled on each resulted in 2000 open sockets from
Riemann to InfluxDB!
It only stopped increasing when InfluxDB cleared out the older sockets after they sat
unused!
Synchronous vs asynchronous InfluxDB writes
Github issue for this: !
https://github.com/aphyr/riemann/issues/411
Comment with code sample for an asynchronous InfluxDB writer:
https://github.com/aphyr/riemann/issues/411#issue-36716498
Capacitor, the Clojure InfluxDB library Riemann uses, can send writes in batches
Asynchronous InfluxDB writes
(fn [event] (let [series (format "%s.%s" (:host event) (:service event))] (write-influxdb-async series { :host (:host event) :time (:time event) :value (:metric event) :name (:service event)})))
The let [series... statement is what creates series names as hostname.metricname with the async writer
Asynchronous InfluxDB writes
Much better performance!Asynchronous InfluxDB writes
3 sockets open instead of 2000+
Much less CPU usage by Riemann and InfluxDB:
Performance should improve when InfluxDB adds support for protobuf inputs
GrafanaNow, we just need a dashboard...
Grafana data sourcesBased on Kibana 3 (it's just HTML, JS and CSS)
Deploy it by unzipping a tarball to a place where your webserver can serve the contents
Edit config.js to add data sources:
Grafana can graph data from Graphite, InfluxDB and
OpenTSDB
Grafana and ElasticsearchGrafana can store dashboards in Elasticsearch
Usage is tiny, only as many JSON docs as dashboards that you save
Elasticsearch is not used to store metrics!
Edit config.js to add an Elasticsearch
server:
A new Graphite stack
Riemann
collectdcollectdcollectdcollectd
statsd statsd
InfluxDB
Grafana
Show me some graphs!
(do a demo)
Demo time!
(do an install of Riemann)
(do an install of InfluxDB)
(do an install of Grafana)
Disadvantages of this new stackGrafana doesn't take advantage of all of InfluxDB's features
InfluxDB doesn't have as many built-in functions as graphite-web
Riemann's documentation is sparse, and if you've never written Clojure, there's a learning curve for
writing configs to do more than basic stuff
There are a bunch of dashboard tools that can talk to graphite-web's API
Not as many can talk to InfluxDB
InfluxDB and GraphiteInfluxDB actually has a Graphite listener built in
So why use Riemann in the middle? Alerting on metrics!
Riemann and alertingHaving events in the index means we can keep track of
metrics over short periods of time
In the collectd metrics for load average across every machine, calculate an average over the last 5 mins and send an email
alert if it's over a certain threshold
In the collectd metrics for load average on an individual machine, calculate a derivative over the last 5 mins and send
a Nagios alert if the derivative is above a certain level
Native Riemann outputs
Some tools, like CollectD, can output data to Riemann in Riemann's native binary protobuf format
Graphite's format for metrics has become the most commonly used format
For things that only output Graphite format metrics, Riemann's Graphite server functionality is incredibly
useful
Pie in the sky: Scaling Riemann
Because of the way it works internally, Riemann doesn't have support for clustering the way InfluxDB does
https://github.com/jdmaturen/reimann/blob/master/riemann.config.guide#L234
You can set up multiple Riemann servers, and they can forward events to a central one, or one of several behind
HAproxy
Specifically, there's no way to share the in-memory index across nodes
Pie in the sky: Scaling Riemann
This sounds like how you would scale Graphite, but because we have InfluxDB, each Riemann instance can write to the same InfluxDB instance, or one of many nodes in a cluster
Because InfluxDB can take data in over a network connection, can cluster and is not plain-file-based like Whisper, multiple
Riemanns writing to 1 or more InfluxDBs in a cluster shouldn't be an issue
Some of the monitoring uses of Riemann (calculating moving averages or derivatives) will break because the in-memory
indexes can’t be shared, though
Pie in the sky: Scaling InfluxDBInfluxDB has out-of-the-box support for clustering
Uses Raft for a consensus protocol
Sharding is done by blocks of time (time periods are configurable
Metadata is shared via Raft that lets each node know which shards covering what time periods and series/DBs are on
each node
http://sssslide.com/speakerdeck.com/pauldix/the-internals-of-influxdb
Also has a WAL (write ahead log)
Databases and series can be broken up into shards
Tool integrationsRiemann and Logstash can output events to each other
Both can output events to Graphite
This can get really confusing, really quickly
One possible use: Logstash takes in web server logs, counts response codes, and outputs metrics in Graphite format to
Riemann
Pies in the skyWhat I want to experiment with and get working:
Email alerts from Riemann
Riemann telling Nagios/Icinga to send alerts based on thresholds for averages or derivatives of metric values
Send web logs to Logstash and make Logstash output metrics from them to Riemann (how many HTTP 404, 503, etc. responses is my web server sending out?)
Links
My super basic Riemann Puppet module: https://github.com/nickchappell/puppet-riemann/
Linkshttp://riemann.io/
http://influxdb.com/
http://grafana.org/
LinksMonitorama PDX 2014 Grafana workshop:
http://vimeo.com/95316672
Monitorama PDX 2014 InfluxDB talk: http://vimeo.com/95311877
Monitorama Boston 2013 Riemann talk: http://vimeo.com/67181466