Introduction to InfluxDB 2.0 & Your First Flux Query by Sonia Gupta, Developer Advocate | InfluxData

Sonia Gupta / Developer Advocate
Introduction to InﬂuxDB
2.0 and Your First Flux
Query

© 2018 InfluxData. All rights reserved.2
Agenda
Basic Concepts
● Brief Overview of New Features in InfluxDB 2.0
● What is Flux?
Usage
● Running InfluxDB 2.0
● Building a basic flux query
● Using Flux in the 2.0 UI and in Chronograf with 1.7

InfluxDB: The Modern Time Series Database
• Time-series database built from
the ground up to handle high write
& query loads
• Custom high performance data
store written specifically for
time-stamped data, (DevOps
monitoring, application metrics,
IoT sensor data, & real-time
analytics)
• Offers a SQL-like query language
for interacting with data (InfluxQL)
• Now offers Flux for interacting with
data!

● It was hard to add feature requests to InfluxQL, Flux now has more
functionality than InfluxQL.
● TICK Script difficult to learn and debug.
● Creating a single binary means a better user experience when the UI
is part of the database.
Why develop Flux and InfluxDB 2.0?

● The UI is very diﬀerent. You have a wizard to build out scripts in 1.x,
whereas you have a visual query builder in 2.0.
● Changed visualizations, can switch between them using dropdown
instead of going to a separate page.
● Dedicated API layer within the system so the UI works against that
layer, and it’s the same between OSS, Cloud, and Enterprise.
What’s New in InﬂuxDB 2.0

● Single Binary - don’t need to deploy external software if you don’t want to.
● Native Prometheus endpoint that exposes our metrics in Prometheus data format -
localhost:9999/metrics
● Tasks are a brand new concept - Flux queries that can be scheduled, can be used to
analyze data over period of time similar to CQs.
● Tokens - you can give access to third party systems to write or read data from the system.

● Organizations are a workspace for your team to share dashboards
and queries, as well as a bucket.
● Telegraf agent conﬁguration in the UI itself.
● Can upload data in Line Protocol from the browser.

Flux is a functional data scripting and query language
• Written to be:
– Useable
• easy to learn
– Readable
• developers read more code than we write
– Composeable
• developers can build onto the language
– Testable
• queries are code
– Contributable
• open source contributions matter
– Shareable
–
– Composeable
– Testable
– Contributable
What Is Flux?Flux?

■ Flux is an alternative to InfluxQL with additional
functionality like:
● Joins
● Math across measurements
● Sort by any column
● Pivot
● Histograms
● Covariance
• Reference: https://docs.influxdata.com/flux/v0.7/introduction/flux-vs-influxql/#influxql-and-flux-parity
■ developers read more code than we write
○ Composeable
■ developers can build onto the language
○ Testable
■ queries are code
○ Contributable
■ open source contributions matter
Flux and InfluxQL

• Key terms and terminology
– Bucket: Named data source with retention policy
– Pipe-forward operator: chain operations together, |>
– Table: data is returned in annotated CSVs, represented as tables
– Group Key: list of columns for which every row in the table has
the same value
• Reference:https://docs.inﬂuxdata.com/ﬂux/v0.7/introduction/getting-started/
Concepts

• Every Flux query needs:
– data source
– time range
– data ﬁlters
Basic Flux Queries

Basic Flux Queries
Step 1:
Data Source
from(bucket:"telegraf/autogen")
Reference:
data source
database retention policy

Basic Flux Queries
Step 2:
Time Range
|> range(start: -1h)
relative time range

Basic Flux Queries
Step 3:
Data Filters
|> ﬁlter(fn: (r) =>
r.measurement == “cpu”
relative time range
Data Filters

Basic Flux Queries
• Every Flux query needs:
– data source
– time range
– data ﬁlters
r.measurement == “cpu” AND
r.ﬁeld == “usage_system” AND
r.cpu == “cpu_total”

Basic Flux Queries - Note
• Use Flux’s yield() function to output the filtered tables as the result of the
query
• Chronograf and Influx CLI automatically assume a yield()
r.measurement == “cpu” AND
r.field == “usage_system” AND
r.cpu == “cpu_total”
|> yield()

Flux functions can be split across four major boundaries:
1) Input functions (read from a source like InﬂuxDB)
2) Functions that combine or split apart tables in the resulting
stream (group, join, window)
3) Functions that get applied to each table in a stream (aggregates,
selectors, sorting)
4) Output functions that send the result to a data sync
– Composeable
– Testable
– Contributable
Four Major Boundaries

• Input
– from()
• Output
– yield()
• Transformation
– aggregates
• mean(), count(), etc
– selectors
• ﬁrst(), last(), max(), min(), etc
– many more
• Miscellaneous
– systemTime()
• Custom
– Composeable
– Testable
– Contributable
Flux Functions

CLI in “Flux” mode
Any Flux query can be executed in the REPL
$ ./inﬂux repl -o <organization name>

CLI in “Flux” mode
Any Flux query can be executed in the REPL
$> from(bucket:"telegraf/autogen")|> range(start: -15m)|> ﬁlter(fn: (r) => r._measurement == "cpu")

Start with Flux
Installation
● Flux is packaged with InfluxDB v1.7+ without further installation needed
● InfluxData Sandbox (Docker container)
● Flux is in technical preview (no prod)
InfluxDB Configuration
● Set `flux-enabled = true`
Help
● https://community.influxdata.com/c/fluxlang

Introduction to InfluxDB 2.0 & Your First Flux Query by Sonia Gupta, Developer Advocate | InfluxData

Introduction to InfluxDB 2.0 & Your First Flux Query by Sonia Gupta, Developer Advocate | InfluxData

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Introduction to InfluxDB 2.0 & Your First Flux Query by Sonia Gupta, Developer Advocate | InfluxData