InfluxDB intro for Data Driven NYC

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InﬂuxDB - an open source time series database Paul Dix CEO @pauldix paul@inﬂuxdb.com

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What it’s for…

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Metrics

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Time Series

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Analytics

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Events

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Use Cases

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DevOps

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Real-time analytics (user & business)

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Sensor Data

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Can’t you just use a regular DB?

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order by time?

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Doesn’t Scale

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Example from metrics: 100 measurements per host * 10 hosts * 8640 per day (once every 10s) * 365 days = 3,153,600,000 records per year

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Have fun with that table…

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But wait, we’ll just keep the summaries!

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1h averages = 8,760,000 per year

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Lose Detail and AdHoc Queryability

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So let’s use Cassandra, HBase, or Scaleasaurus!

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Too much application code and complexity

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Application logic and scripts to compute summaries

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Application level logic for balancing

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No data locality for AdHoc queries

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How to handle data retention?

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And then there’s more…

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Web services

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Libraries for web services

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Data collection

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Visualization

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–Paul Dix “Building an application with an analytics component today is like building a web application in 1998. You spend months building infrastructure before getting to the actual thing you want to build.”

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Analytics and monitoring should be about analyzing and interpreting data, not the infrastructure to store and process it.

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No content

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A time series database with no external dependencies

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Features Upcoming 0.9.0 release

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Data model • Databases

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Data model • Databases • Measurements • cpu_load, temperature, log_lines, click, etc.

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Data model • Databases • Measurements • cpu_load, temperature, log_lines, click, etc. • Tags • region=uswest, host=serverA, building=23, service=redis, etc.

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Data model • Databases • Measurements • cpu_load, temperature, log, click, etc. • Tags • region=uswest, host=serverA, building=23, service=redis, etc. • Series - measurement + unique tagset • Points • Fields - bool, int64, ﬂoat64, string, []byte • Timestamp - nano epoch

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Writing Data curl -XPOST 'http://localhost:8086/write' -d '...'

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Writing Data { "database": "mydb", "retentionPolicy": "30d", "points": [ { "name": "cpu_load", "tags": { "host": "server01", "region": "us-west" }, "timestamp": "2009-11-10T23:00:00Z", "fields": { "value": 0.64 } } ] } Measurement Tags Fields

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Querying curl -G 'http://localhost:8086/query' --data-urlencode "q=..."

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SQL-ish query language

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SELECT value FROM cpu WHERE host = 'serverA' { "results":[ { "query": "SELECT value FROM cpu WHERE host='serverA'", "series": [ { "name": "cpu", "tags": { "host": "serverA" }, "columns": ["time", "value"], "values": [ ["2009-11-10T23:00:00Z", 22.1], ["2009-11-10T23:00:10Z", 25.2] ] } ] } ] } QUERY: RESULTS:

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SELECT value FROM cpu WHERE host = ‘serverA'OR host = 'serverB' QUERY: { "series": [ { "name": "cpu", "tags": { "host": "serverA" }, "columns": ["time", "value"], "values": [] }, { "name": "cpu", "tags": { "host": "serverB" }, "columns": ["time", "value"], "values": [] } ] } SERIES IN RESULT:

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SELECT percentile(90, value) FROM cpu WHERE time > now() - 4h GROUP BY time(10m), region QUERY: [ { "name": "cpu", "tags": { "region": "us-west" }, "columns": ["time", "percentile"], "values": [] }, { "name": "cpu", "tags": { "region": "us-east" }, "columns": ["time", "percentile"], "values": [] } ] SERIES IN RESULT:

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Multiple aggregates SELECT mean(value), percentile(90, value), min(value), max(value) FROM cpu WHERE host='serverA' AND time > now() - 48h GROUP BY time(1h)

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Return every series in CPU SELECT mean(value) FROM cpu WHERE time > now() - 48h GROUP BY time(1h), *

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Discovery based on tags

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{ "results":[ { "query": "SHOW MEASUREMENTS", "series": [ { "name": "measurements", "columns": ["name"], "values": [ ["cpu"], ["memory"], ["network"] ] } ] } ] }

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{ "results":[ { "query": "SHOW SERIES", "series": [ { "name": "cpu", "columns": ["id", "region", "host"], "values": [ [1, "us-west", "serverA"], [2, "us-east", "serverB"] ] } ] } ] }

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{ "query": "SHOW MEASUREMENTS WHERE service='redis'", "series": [ { "name": "measurements", "name": "series", "columns": ["measurement"], "values": [ ["key_count"], ["connections"] ] } ] }

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{ "query": "SHOW TAG KEYS from cpu", "series": [ { "name": "keys", "columns": ["key"], "values": [ ["region"], ["host"] ] } ] }

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{ "query": "SHOW TAG VALUES WITH KEY = service", "series": [ { "name": "series", "columns": ["service"], "values": [ ["redis"], ["apache"] ] } ] }

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{ "query": "SHOW TAG VALUES FROM cpu WITH KEY = service", "series": [ { "name": "series", "columns": ["service"], "values": [ ["redis"], ["apache"] ] } ] }

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Much more • Retention policies • Automatic downsampling and aggregation • Clustering