Querying Prometheus with Flux (#fluxlang) Paul Dix @pauldix - - PowerPoint PPT Presentation

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Querying Prometheus with Flux (#fluxlang) Paul Dix @pauldix - - PowerPoint PPT Presentation

Jul 03, 2023 225 likes •1.37k views

Querying Prometheus with Flux (#fluxlang) Paul Dix @pauldix paul@influxdata.com Data-scripting language Functional MIT Licensed Language & Runtime/Engine Prometheus users: so what? High availability? Sharded Data?

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Querying Prometheus with Flux (#fluxlang)

Paul Dix @pauldix paul@influxdata.com

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  • Data-scripting language
  • Functional
  • MIT Licensed
  • Language & Runtime/Engine
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Prometheus users: so what?

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High availability?

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Sharded Data?

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Federation?

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subqueries

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subqueries recording rules

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Ad hoc exporation

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Focus is Strength

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Saying No is an Asset

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Liberate the silo!

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Language Elements

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// get all data from the telegraf db from(db:"telegraf") // 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(db:"telegraf") // 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(db:"telegraf") // 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")

Functions

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// get all data from the telegraf db from(db:"telegraf") // 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(db:"telegraf") // 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(db:"telegraf") // 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 Literal

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// get all data from the telegraf db from(db:"telegraf") // 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 (relative time)

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// get all data from the telegraf db from(db:"telegraf") // filter that by the last hour |> range(start:”2018-08-09T14:00:00Z“) // 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(db:"telegraf") // 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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Operators

+ == != ( )

  • < !~ [ ]

* > =~ { } / <= = , : % >= <- . |>

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Types

  • int
  • uint
  • float64
  • string
  • duration
  • time
  • regex
  • array
  • object
  • function
  • namespace
  • table
  • table stream
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Ways to run Flux - (interpreter, fluxd api server, InfluxDB 1.7 & 2.0)

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Flux builder in Chronograf

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Flux builder in Grafana

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Flux is about:

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Time series in Prometheus

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// get data from Prometheus on http://localhost:9090 fromProm(query:`node_cpu_seconds_total{cpu=“0”,mode=“idle”}`) // filter that by the last minute |> range(start:-1m)

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Multiple time series in Prometheus

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fromProm(query: `node_cpu_seconds_total{cpu=“0”,mode=~”idle|user”}`) |> range(start:-1m) |> keep(columns: [“name”, “cpu”, “host”, “mode”, “_value”, “_time”])

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Tables are the base unit

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Not tied to a specific data model/schema

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Filter function

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fromProm() |> range(start:-1m) |> filter(fn: (r) => r.__name__ == “node_cpu_seconds_total” and r.mode == “idle” and r.cpu == “0”) |> keep(columns: [“name”, “cpu”, “host”, “mode”, “_value”, “_time”])

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fromProm() |> range(start:-1m) |> filter(fn: (r) => r.__name__ == “node_cpu_seconds_total” and r.mode in [“idle”, “user”] and r.cpu == “0”) |> keep(columns: [“name”, “cpu”, “host”, “mode”, “_value”, “_time”])

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Aggregate functions

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fromProm() |> range(start:-30s) |> filter(fn: (r) => r.__name__ == “node_cpu_seconds_total” and r.mode == “idle” and r.cpu =~ /0|1/) |> count() |> keep(columns: [“name”, “cpu”, “host”, “mode”, “_value”, “_time”])

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_start and _stop are about windows of data

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fromProm(query: `node_cpu_seconds_total{cpu=“0”,mode=“idle”}` |> range(start: -1m)

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fromProm(query: `node_cpu_seconds_total{cpu=“0”,mode=“idle”}` |> range(start: -1m) |> window(every: 20s)

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fromProm(query: `node_cpu_seconds_total{cpu=“0”,mode=“idle”}` |> range(start: -1m) |> window(every: 20s)j |> min()

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fromProm(query: `node_cpu_seconds_total{cpu=“0”,mode=“idle”}` |> range(start: -1m) |> window(every: 20s)j |> min() |> window(every:inf)

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Window converts N tables to M tables based on time boundaries

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Group converts N tables to M tables based on values

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fromProm(query: `node_cpu_seconds_total{cpu=~“0|1”,mode=“idle”}`) |> range(start: -1m)

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fromProm(query: `node_cpu_seconds_total{cpu=~“0|1”,mode=“idle”}`) |> range(start: -1m) |> group(columns: [“__name__”, “mode”])

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Nested range vectors

fromProm(host:”http://localhost:9090") |> filter(fn: (r) => r.__name__ == "node_disk_written_bytes_total") |> range(start:-1h) // transform into non-negative derivative values |> derivative() // break those out into tables for each 10 minute block of time |> window(every:10m) // get the max rate of change in each 10 minute window |> max() // and put everything back into a single table |> window(every:inf) // and now let’s convert to KB |> map(fn: (r) => r._value / 1024.0)

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Multiple Servers

dc1 = fromProm(host:”http://prom.dc1.local:9090") |> filter(fn: (r) => r.__name__ == “node_network_receive_bytes_total”) |> range(start:-1h) |> insertGroupKey(key: “dc”, value: “1”) dc2 = fromProm(host:”http://prom.dc2.local:9090") |> filter(fn: (r) => r.__name__ == “node_network_receive_bytes_total”) |> range(start:-1h) |> insertGroupKey(key: “dc”, value: “2”) dc1 |> union(streams: [dc2]) |> limit(n: 2) |> derivative() |> group(columns: [“dc”]) |> sum()

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Work with data from many sources

  • from() // influx
  • fromProm()
  • fromMySQL()
  • fromCSV()
  • fromS3()
  • …
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Defining Functions

fromProm(query: `node_cpu_seconds_total{cpu=“0”,mode=“idle”}` |> range(start: -1m) |> window(every: 20s)j |> min() |> window(every:inf)

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Defining Functions

windowAgg = (every, fn, <-stream) => { return stream |> window(every: every) |> fn() |> window(every:inf) } fromProm(query: `node_cpu_seconds_total{cpu=“0”,mode=“idle”}` |> range(start: -1m) |> windowAgg(every:20s, fn: min)

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Packages & Namespaces

package “flux-helpers” windowAgg = (every, fn, <-stream) => { return stream |> window(every: every) |> fn() |> window(every:inf) } // in a new script import helpers “github.com/pauldix/flux-helpers" fromProm(query: `node_cpu_seconds_total{cpu=“0”,mode=“idle”}` |> range(start: -1m) |> helpers.windowAgg(every:20s, fn: min)

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Project Status

  • Everything in this talk is prototype (as of 2018-08-09)
  • Proposed Final Language Spec
  • Release flux, fluxd, InfluxDB 1.7, InfluxDB 2.0 alpha
  • Iterate with community to finalize spec
  • Optimizations!
  • https://github.com/influxdata/flux
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Future work

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More complex Flux compilations to PromQL?

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PromQL parser for Flux engine?

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Add Flux into Prometheus?

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Arrow API for Prometheus

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Apache Arrow

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Stream from Prometheus

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Pushdown matcher and range

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Later pushdown more?

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Standardized Remote Read API?

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Arrow is becoming the lingua franca in data science and big data

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fromProm(query: `{__name__=~/node_.*/}`) |> range(start:-1h) |> toCSV(file: “node-data.csv”) |> toFeather(file: “node-data.feather”)

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Much more work to be done…

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Prometheus + Flux = Possibilities

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Thank you

Paul Dix @pauldix paul@influxdata.com