@influxdb - usenix...influxdb u ser v a l u e time? 1 9 / 6 influxdb 2 1 / 6 9. influxdb time series...
Post on 03-Jun-2020
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@InfluxDBDavid Norton (@dgnorton)
david@influxdb.com
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Instrumenting a Data Center
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The problem:Efficiently monitor hundreds or
thousands of servers
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The solution:Automate it!
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Automate what?data collection, storage, analysis, &
alerting
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Easy!Write a few scripts & store the data in
SQL.
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1 server
100 measurements
8,640 per day (once every 10s)
365 days
= 315 million records (points) per year9 / 69
10 servers
100 measurements per server
8,640 per day (once every 10s)
365 days
= 3.2 billion records (points) per year10 / 69
2,000 servers
200 measurements per server
17,280 per day (once every 5s)
365 days
= 2.5 trillion records (points) per year
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Time series database is ideal for thistype of data & workload
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"time series"
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Values and time stampsValue
Time?14 / 69
Values and time stamps
Value
Time?
time value20150422 5:00 PM20150422 6:00 PM
10,020,0
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CPU usage every 10 seconds...
Value
Time
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Cumulative steps taken...
Step
Value
Step
Count Value
Time
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Happy InfluxDB users...
Value
TimeInfluxDB
Users
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Time series database
Value
Time
Step
Count Value
Time
Value
TimeInfluxDB
Users
Value
Time?19 / 69
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InfluxDB
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InfluxDB time series database
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InfluxDB time series database
no external dependencies
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InfluxDB time series database
no external dependencies
distributed & scalable
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InfluxDB time series database
no external dependencies
distributed & scalable
easy to install, use, & maintain
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InfluxDB time series database
no external dependencies
distributed & scalable
easy to install, use, & maintain
open source (MIT license)
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InfluxDB time series database
no external dependencies
distributed & scalable
easy to install, use, & maintain
open source (MIT license)
written in Go27 / 69
Features SQLlike query language
HTTP(S) API for writes & queries
Supports other protocols (collectd, graphite,opentsdb)
Automated data retention policies
Aggregate data onthefly
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Data model...
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influx d
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influx d
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influx d
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influx d
measurements
DISK
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influx d
measurements
DISK
tags
country ='DEU'
region ='neast'
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influx d
country ='DEU'
region ='west'
+
series = measurement + unique tag set
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influx d
country ='DEU'
region ='west'
+
series = measurement + unique tag set
OR
country ='DEU'
region ='east'
+
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influx d
Points: values in a series
country ='DEU'
region ='east'
+time value20150422 5:00 PM20150422 5:01 PM
10,020,0
country ='USA'
region ='east'
+time value20150422 5:00 PM20150422 5:01 PM
14,038,0
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What's indexed?
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What's indexed?Metadata:
Measurement names
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What's indexed?Metadata:
Measurement names
Tag keys & values
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What's indexed?Metadata:
Measurement names
Tag keys & values
Field keys
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What's indexed?Metadata:
Measurement names
Tag keys & values
Field keys
Field values by time*
WHERE time > now() 1m
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Metadata index is held inmemory atrun time
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What's not indexed?
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What's not indexed? Field values by value
WHERE value > 2.718
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What's not indexed? Field values by value
WHERE value > 2.718
Metadata by time
SHOW MEASUREMENTS WHERE time > now() - 1h
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Data retention
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InfluxDB hasRetention Policies
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How do they work?
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All data is written to aretention policy
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Each retention policy has aduration specified(between 1 hour and infinite)
(between 1 hour and infinite)
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raw(1 day)
cpu_1h(1 week)
shards shards
SELECT mean(value) INTO cpu_1h.cpu FROM raw.cpu WHERE time > now() 1w GROUP BY time(1h)
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How to automatedownsampling?
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Continuous QueriesStored in the database and run periodically in
the background
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raw(1 day)
cpu_1h(1 week)
shards shards
CREATE CONTINUOUS QUERY mycq ON mydbCREATE CONTINUOUS QUERY mycq ON mydb BEGIN BEGIN SELECT mean(value) SELECT mean(value) INTO cpu_1h.cpu INTO cpu_1h.cpu FROM raw.cpu FROM raw.cpu GROUP BY time(1h) GROUP BY time(1h) END END
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Replication is handled throughRetention Policies
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FunctionsAggregations:
count, distinct, mean, median, sum
count, distinct, mean, median, sum
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FunctionsAggregations:
count, distinct, mean, median, sum
Selectors:
bottom, first, last, max, min, percentile, top
bottom, first, last, max, min, percentile, top
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FunctionsAggregations:
count, distinct, mean, median, sum
Selectors:
bottom, first, last, max, min, percentile, top
Transformations:
ceiling, derivative, difference, floor, histogram,stddev
stddev60 / 69
Data ingestion
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InfluxDB supports: collectd
openTSDB
graphite
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Telegraf http://github.com/influxdb/telegraf
Open Source (MIT License)
Also written in Go
Plugin based (~30 currently and growing)
Cross platform
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Client libraries
Client libraries Go
Ruby
Java
Python
etc. (http://github.com/influxdb)
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HTTP(S) APIWrite data via HTTP using a simpletextbased protocol
cpu,host=serverA value=10.0 1234567890
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Visualization
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Grafana
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Chronograf
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Thank You!@InfluxDB
David Norton (@dgnorton)
david@influxdb.com
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