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Flux and InfluxDB 2.0 Paul Dix @pauldix paul@influxdata.com

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• Data-scripting language • Functional • MIT Licensed • Language & Runtime/Engine

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Language + Query Engine

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2.0

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Biggest Change Since 0.9

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Clean Migration Path

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Compatibility Layer

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• MIT Licensed • Multi-tenanted • Telegraf, InfluxDB, Chronograf, Kapacitor rolled into 1 • OSS single server • Cloud usage based pricing • Dedicated Cloud • Enterprise on-premise

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• MIT Licensed • Multi-tenanted • Telegraf, InfluxDB, Chronograf, Kapacitor rolled into 1 • OSS single server • Cloud usage based pricing • Dedicated Cloud • Enterprise on-premise

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TICK is dead

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Long Live InfluxDB 2.0 (and Telegraf)

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Consistent Documented API Collection, Write/Query, Streaming & Batch Processing, Dashboards

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Officially Supported Client Libraries Go, Node.js, Ruby, Python, PHP, Java, C#, C, Kotlin

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Visualization Libraries

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Ways to run Flux - (interpreter, InfluxDB 1.7 & 2.0)

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Flux Language Elements

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// get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system")

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// get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Comments

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// get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Named Arguments

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// get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") String Literals

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// get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Buckets, not DBs

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// get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Duration Literal

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// get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:2018-11-07T00:00:00Z) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Time Literal

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// get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Pipe forward operator

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// get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system") Anonymous Function

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// get all data from the telegraf db from(bucket:”telegraf/autogen”) // filter that by the last hour |> range(start:-1h) // filter further by series with a specific measurement and field |> filter(fn: (r) => (r._measurement == "cpu" or r._measurement == “cpu") and r.host == “serverA") Predicate Function

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// variables some_int = 23

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// variables some_int = 23 some_float = 23.2

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// variables some_int = 23 some_float = 23.2 some_string = “cpu"

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// variables some_int = 23 some_float = 23.2 some_string = “cpu" some_duration = 1h

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// variables some_int = 23 some_float = 23.2 some_string = “cpu" some_duration = 1h some_time = 2018-10-10T19:00:00

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// variables some_int = 23 some_float = 23.2 some_string = “cpu" some_duration = 1h some_time = 2018-10-10T19:00:00 some_array = [1, 6, 20, 22]

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// variables some_int = 23 some_float = 23.2 some_string = “cpu" some_duration = 1h some_time = 2018-10-10T19:00:00 some_array = [1, 6, 20, 22] some_object = {foo: "hello" bar: 22}

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Data Model & Working with Tables

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Example Series _measurement=mem,host=A,region=west,_field=free _measurement=mem,host=B,region=west,_field=free _measurement=cpu,host=A,region=west,_field=usage_system _measurement=cpu,host=A,region=west,_field=usage_user

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Example Series _measurement=mem,host=A,region=west,_field=free _measurement=mem,host=B,region=west,_field=free _measurement=cpu,host=A,region=west,_field=usage_system _measurement=cpu,host=A,region=west,_field=usage_user Measurement

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Example Series _measurement=mem,host=A,region=west,_field=free _measurement=mem,host=B,region=west,_field=free _measurement=cpu,host=A,region=west,_field=usage_system _measurement=cpu,host=A,region=west,_field=usage_user Field

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Table _measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 10

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_measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 10 Column

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_measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 10 Record

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_measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 10 Group Key _measurement=mem,host=A,region=west,_field=free

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_measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 10 Every record has the same value! _measurement=mem,host=A,region=west,_field=free

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Table Per Series _measurement host region _field _time _value mem A west free 2018-06-14T09:15:00 10 mem A west free 2018-06-14T09:14:50 11 _measurement host region _field _time _value mem B west free 2018-06-14T09:15:00 20 mem B west free 2018-06-14T09:14:50 22 _measurement host region _field _time _value cpu A west usage_user 2018-06-14T09:15:00 45 cpu A west usage_user 2018-06-14T09:14:50 49 _measurement host region _field _time _value cpu A west usage_system 2018-06-14T09:15:00 35 cpu A west usage_system 2018-06-14T09:14:50 38

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input tables -> function -> output tables

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input tables -> function -> output tables // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum()

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input tables -> function -> output tables What to sum on? // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum()

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input tables -> function -> output tables Default columns argument // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum(columns: [“_value”])

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input tables -> function -> output tables _meas ureme host region _field _time _valu e mem A west free 2018-06- 14T09:1 10 mem A west free 2018-06- 14T09:1 11 _meas ureme host region _field _time _valu e mem B west free 2018-06- 14T09:15 20 mem B west free 2018-06- 14T09:14 22 Input in table form // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum()

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input tables -> function -> output tables _meas ureme host region _field _time _valu e mem A west free 2018-06- 14T09:1 10 mem A west free 2018-06- 14T09:1 11 _meas ureme host region _field _time _valu e mem B west free 2018-06- 14T09:15 20 mem B west free 2018-06- 14T09:14 22 sum() // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum()

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input tables -> function -> output tables // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> sum() _meas ureme host region _field _time _valu e mem A west free 2018-06- 14T09:1 10 mem A west free 2018-06- 14T09:1 11 _meas ureme host region _field _time _valu e mem B west free 2018-06- 14T09:15 20 mem B west free 2018-06- 14T09:14 22 sum() _meas ureme host region _field _time _valu e mem A west free 2018-06- 14T09:1 21 _meas ureme host region _field _time _valu e mem B west free 2018-06- 14T09:15 42

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N to N table mapping (1 to 1 mapping)

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N to M table mapping

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window // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s) 30s of data (4 samples)

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window // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s) split into 20s windows

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window _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s) Input

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window _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 window( every:20s) // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s)

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window _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 window( every:20s) // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s) _meas ureme host region _field _time _valu e mem A west free …14:30 10 mem A west free …14:40 11 _meas ureme host region _field _time _valu e mem B west free …14:50 23 mem B west free …15:00 24 _meas ureme host region _field _time _valu e mem B west free …14:30 20 mem B west free …14:40 22 _meas ureme host region _field _time _valu e mem A west free …14:50 12 mem A west free …15:00 13

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window _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 window( every:20s) // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> window(every:20s) _meas ureme host region _field _time _valu e mem A west free …14:30 10 mem A west free …14:40 11 _meas ureme host region _field _time _valu e mem B west free …14:50 23 mem B west free …15:00 24 _meas ureme host region _field _time _valu e mem B west free …14:30 20 mem B west free …14:40 22 _meas ureme host region _field _time _valu e mem A west free …14:50 12 mem A west free …15:00 13 N to M tables

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Window based on time _start and _stop columns

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group // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> group(keys:[“region"])

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group // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> group(keys:[“region"]) new group key

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group _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> group(keys:[“region"])

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group _meas host region _field _time _valu mem A west free …14:30 10 mem A west free …14:40 11 mem A west free …14:50 12 mem A west free …15:00 13 _meas host region _field _time _valu mem B west free …14:30 20 mem B west free …14:40 22 mem B west free …14:50 23 mem B west free …15:00 24 group( keys: [“region”]) // example query from(db:"telegraf") |> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01) |> filter(fn: r => r._measurement == “mem" and r._field == “free”) |> group(keys:[“region"]) _meas ureme host region _field _time _valu e mem A west free …14:30 10 mem B west free …14:30 20 mem A west free …14:40 11 mem B west free …14:40 21 mem A west free …14:50 12 mem B west free …14:50 22 mem B west free …15:00 13 mem B west free …15:00 23 N to M tables M == cardinality(group keys)

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Group based on columns

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Flux Design Principles

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Useable

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Make Everyone a Data Programmer!

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Readable

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Flexible

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Composable

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Testable

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Contributable

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Shareable

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Functions Overview

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Inputs from, fromKafka, fromFile, fromS3, fromPrometheus, fromMySQL, etc.

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Flux != InfluxDB

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Follow Telegraf Model

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import "mysql" customers = mysql.from(connect: loadSecret(name:”mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results")

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import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Imports for sharing code!

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import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Pulling data from a non-InfluxDB source

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import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Raw query (for now)

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import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Loading Secret

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import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Renaming & Shaping Data

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import "mysql" customers = mysql.from(connect: loadSecret(name:"mysql_prod"), query: "select id, name from customers") data = from(bucket: "my_data") |> range(start: -4h) |> filter(fn: (r) => r._measurement == “write_requests") |> rename(columns: {customer_id: “id"}) join(tables: {customers, data}, on: ["id"]) |> yield(name: "results") Join on any column

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Outputs to, toKafka, toFile, toS3, toPrometheus, toMySQL, etc.

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Outputs are for Tasks

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option task = { name: “Alert on disk", every: 5m, } crit = 90 // alert at this percentage warn = 80 // warn at this percentage data = from(bucket: "telegraf/autogen") |> filter(fn: (r) => r._measurement == "disk" and r._field == "used_percent") |> last() data |> filter(fn: (r) => r._value > crit) |> addColumn(key: "level", value: "critical") |> addColumn(key: "alert", value: task.name) |> to(bucket: "alerts") data |> filter(fn: (r) => r._value > warn && r._value < crit) |> addColumn(key: "level", value: "warn") |> to(bucket: "alerts")

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option task = { name: “Alert on disk", every: 5m, } crit = 90 // alert at this percentage warn = 80 // warn at this percentage data = from(bucket: "telegraf/autogen") |> filter(fn: (r) => r._measurement == "disk" and r._field == "used_percent") |> last() data |> filter(fn: (r) => r._value > crit) |> addColumn(key: "level", value: "critical") |> addColumn(key: "alert", value: task.name) |> to(bucket: "alerts") data |> filter(fn: (r) => r._value > warn && r._value < crit) |> addColumn(key: "level", value: "warn") |> to(bucket: "alerts") Option syntax for tasks

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option task = { name: “Alert on disk", every: 5m, } crit = 90 // alert at this percentage warn = 80 // warn at this percentage data = from(bucket: "telegraf/autogen") |> filter(fn: (r) => r._measurement == "disk" and r._field == "used_percent") |> last() data |> filter(fn: (r) => r._value > crit) |> addColumn(key: "level", value: "critical") |> addColumn(key: "alert", value: task.name) |> to(bucket: "alerts") data |> filter(fn: (r) => r._value > warn && r._value < crit) |> addColumn(key: "level", value: "warn") |> to(bucket: "alerts") Get at the last value without specifying time range

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option task = { name: “Alert on disk", every: 5m, } crit = 90 // alert at this percentage warn = 80 // warn at this percentage data = from(bucket: "telegraf/autogen") |> filter(fn: (r) => r._measurement == "disk" and r._field == "used_percent") |> last() data |> filter(fn: (r) => r._value > crit) |> addColumn(key: "level", value: “critical") |> addColumn(key: "alert", value: task.name) |> to(bucket: "alerts") data |> filter(fn: (r) => r._value > warn && r._value < crit) |> addColumn(key: "level", value: "warn") |> to(bucket: "alerts") Adding a column to decorate the data

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option task = { name: “Alert on disk", every: 5m, } crit = 90 // alert at this percentage warn = 80 // warn at this percentage data = from(bucket: "telegraf/autogen") |> filter(fn: (r) => r._measurement == "disk" and r._field == "used_percent") |> last() data |> filter(fn: (r) => r._value > crit) |> addColumn(key: "level", value: "critical") |> addColumn(key: "alert", value: task.name) |> to(bucket: "alerts") data |> filter(fn: (r) => r._value > warn && r._value < crit) |> addColumn(key: "level", value: "warn") |> to(bucket: "alerts") To writes to the local InfluxDB

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Separate Alerts From Notifications!

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option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: “slack_alert_config”), message: “_value”) |> to(bucket: “notifications")

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option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == “critical”) // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") We have state so we don’t resend

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option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") Use last time as argument to range

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option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") Now function for current time

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option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") Map function to iterate over values

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option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") String interpolation

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option task = {name: "slack critical alerts", every: 1m} import "slack" lastNotificationTime = from(bucket: "notificatons") |> filter(fn: (r) => r.level == "critical" and r._field == "alert_time") |> group(none:true) |> last() |> recordValue(column:"_value") from(bucket: "alerts") |> range(start: lastNotificationTime) |> filter(fn: (r) => r.level == "critical") // shape the alert data to what we care about in notifications |> renameColumn(from: "_time", to: "alert_time") |> renameColumn(from: "_value", to: "used_percent") // set the time the notification is being sent |> addColumn(key: "_time", value: now()) // get rid of unneeded columns |> drop(columns: ["_start", "_stop"]) // write the message |> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%") |> slack.to(config: loadSecret(name: "slack_alert")) |> to(bucket: “notifications") Send to Slack and record in InfluxDB

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option task = { name: "email alert digest", cron: "0 5 * * 0" } import "smtp" body = "" from(bucket: "alerts") |> range(start: -24h) |> filter(fn: (r) => (r.level == "warn" or r.level == "critical") and r._field == "message") |> group(by: ["alert"]) |> count() |> group(none: true) |> map(fn: (r) => body = body + "Alert {r.alert} triggered {r._value} times\n") smtp.to( config: loadSecret(name: "smtp_digest"), to: "[email protected]", title: "Alert digest for {now()}", body: message)

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option task = { name: "email alert digest", cron: "0 5 * * 0" } import "smtp" body = "" from(bucket: "alerts") |> range(start: -24h) |> filter(fn: (r) => (r.level == "warn" or r.level == "critical") and r._field == "message") |> group(by: ["alert"]) |> count() |> group(none: true) |> map(fn: (r) => body = body + "Alert {r.alert} triggered {r._value} times\n") smtp.to( config: loadSecret(name: "smtp_digest"), to: "[email protected]", title: "Alert digest for {now()}", body: message) Cron syntax

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option task = { name: "email alert digest", cron: "0 5 * * 0" } import "smtp" body = "" from(bucket: "alerts") |> range(start: -24h) |> filter(fn: (r) => (r.level == "warn" or r.level == "critical") and r._field == "message") |> group(by: ["alert"]) |> count() |> group(none: true) |> map(fn: (r) => body = body + "Alert {r.alert} triggered {r._value} times\n") smtp.to( config: loadSecret(name: "smtp_digest"), to: "[email protected]", title: "Alert digest for {now()}", body: message) Closures

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Tasks run logs (just another time series)

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UI will hide complexity

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Built on top of primitives

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API for Defining Dashboards

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Bulk Import & Export Specify bucket, range, predicate

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Same API in OSS, Cloud, and Enterprise

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CLI & UI

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2.0

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Thank you. Paul Dix @pauldix paul@influxdata.com