An InfluxDB task is a scheduled Flux script that takes a stream of input data, modifies, or analyzes it in some way. This session breaks down how to use tasks, introduces invokable scripts, and looks at the future of tasks.

2. InfluxDB Tasks
Overview
Balaji Palani - Senior Director of Product Management,
InfluxData

3. Connect Learn Build
Hear from and meet developers
from the InfluxDB Community
Be inspired by use cases from
our partners and InfluxDB engineers
Learn best practices that will
help you build great experiences
for your projects

4. An InfluxDB task is a scheduled Flux script that takes a
stream of input data, modifies or analyzes it in some way.
This session breaks down how to use tasks, introduces
invokable scripts, and looks at the future of tasks.
Balaji Palani
Senior Director of Product
Management, InfluxData
Balaji Palani is the Director of Product Management focused on
InfluxDB Cloud. He is passionate about building powerful cloud
products that help Developers achieve the fastest time to awesome.
And with InfluxDB Cloud, his customers are able to collect and utilize
time series data to hit even the toughest SLAs. Previous to InfluxData,
Balaji has held several Product Management and Engineering
positions at companies like BMC, HP, and Mercury. Balaji holds an MS
degree in Computer Science from West Virginia University and a BS in
Electrical Engineering from Annamalai University.
InfluxDB Tasks Overview

5. Agenda
1. What Are Tasks?
2. Invokable Scripts
3. The Future of Tasks

6. What Are Tasks?

7. Tasks
(Automate your transformations)
Raw Data Transformed
or
Downsampled
Data

8. Tasks
(Automate your transformations)
Raw Data Transformed
or
Downsampled
Data

9. Flux
1. Query your raw data:
○ Bucket
○ Time range
○ Filters
Task structure
from(bucket: "raw_data")
|> range(start: -15m, stop: now())
|> filter(fn: (r) => (r._measurement
== "airSensors"))
|> filter(fn: (r) => (r._field ==
"temperature"))

10. Flux
1. Query your raw data:
○ Bucket
○ Time range
○ Filters
Task structure
from(bucket: "raw_data")
|> range(start: -15m, stop: now())
|> filter(fn: (r) => (r._measurement
== "airSensors"))
|> filter(fn: (r) => (r._field ==
"temperature"))

11. 2. Aggregate the data
○ Use Flux math functions
○ Build your custom logic
Task structure
from(bucket: "raw_data")
|> range(start: -15m, stop: now())
|> filter(fn: (r) => (r._measurement
== "airSensors"))
|> filter(fn: (r) => (r._field ==
"temperature"))
|> mean(column: "_value")
Flux

12. 3. Write the results to
downsampled bucket
Task structure
from(bucket: "raw_data")
|> range(start: -15m, stop: now())
|> filter(fn: (r) => (r._measurement
== "airSensors"))
|> filter(fn: (r) => (r._field ==
"temperature"))
|> mean(column: "_value")
|>rename (columns: {_stop:”_time”})
|> to(bucket: "downsampled_data")
Flux

13. 4. Setup your task options:
■ Name of the task
■ Frequency
Task structure
option task = {name: “agg_1h_sum”,
every: 15m, offset: 30s}
from(bucket: "raw_data")
|> range(start: -15m, stop: now())
|> filter(fn: (r) => (r._measurement
== "airSensors"))
|> filter(fn: (r) => (r._field ==
"temperature"))
|> mean(column: "_value")
|>rename (columns: {_stop:”_time”})
|> to(bucket: "downsampled_data")
Flux

14. Flux has a vast array of
transformation functions to
build any custom task
An example of a custom downsampling task
option task = {name: "transform_10s_power",
every: 10s}
offset = 1h
step = 10s
ta1 = from(bucket: "raw_bucket_1")
|> range(start: -offset)
|> filter(fn: (r) => (r["_field"] == "power"))
|> aggregateWindow(every: step, fn: mean)
|> pivot(rowKey: ["_time"], columnKey:
["_field"], valueColumn: "_value")
ta2 = from(bucket: "raw_bucket_2)
|> range(start: -offset)
|> filter(fn: (r) => (r["_field"] ==
"parsed_value”))
|> aggregateWindow(every: step, fn: mean)
|> pivot(rowKey: ["_time"], columnKey:
["_field"], valueColumn: "_value")
join(tables: {t1: ta1, t2: ta2}, on: ["_time"])
|> to(bucket: "agg_bucket")
Flux

15. Flux has a vast array of
transformation functions to
build any custom task
An example of a custom downsampling task
option task = {name: "transform_10s_power",
every: 10s}
offset = 1h
step = 10s
ta1 = from(bucket: "raw_bucket_1")
|> range(start: -offset)
|> filter(fn: (r) => (r["_field"] == "power"))
|> aggregateWindow(every: step, fn: mean)
|> pivot(rowKey: ["_time"], columnKey:
["_field"], valueColumn: "_value")
ta2 = from(bucket: "raw_bucket_2)
|> range(start: -offset)
|> filter(fn: (r) => (r["_field"] ==
"parsed_value”))
|> aggregateWindow(every: step, fn: mean)
|> pivot(rowKey: ["_time"], columnKey:
["_field"], valueColumn: "_value")
join(tables: {t1: ta1, t2: ta2}, on: ["_time"])
|> to(bucket: "agg_bucket")
Flux

16. Alert Checks
(Built on the Tasks subsystem)
Monitored
Data
_monitoring
(System
Bucket)
monitoring()

17. An example of a deadman check task
Flux
import "influxdata/influxdb/monitor"
import "experimental"
import "influxdata/influxdb/v1"
data = from(bucket: "idping"
|> range(start: -10m)
|> filter(fn: (r) => (r["_measurement"] == "ctr"))
|> filter(fn: (r) => (r["_field"] == "n")))
option task = {name: "idping Deadman", every: 1m, offset: 0s}
check = {
_check_id: "CHECK_ID",
_check_name: "CHECK_NAME",
_type: "deadman",
tags: {idping: "deadman"}
}
crit = (r) => (r["dead"])
messageFn = (r) => ("Check: ${r._check_name} is: ${r._level}")
data
|> v1["fieldsAsCols"]()
|> monitor["deadman"](t: experimental["subDuration"](from: now(), d: 5m))
|> monitor["check"](data: check, messageFn: messageFn, crit: crit)

18. An example of a deadman check task
Flux
import "influxdata/influxdb/monitor"
import "experimental"
import "influxdata/influxdb/v1"
data = from(bucket: "idping"
|> range(start: -10m)
|> filter(fn: (r) => (r["_measurement"] == "ctr"))
|> filter(fn: (r) => (r["_field"] == "n")))
option task = {name: "idping Deadman", every: 1m, offset: 0s}
check = {
_check_id: "CHECK_ID",
_check_name: "CHECK_NAME",
_type: "deadman",
tags: {idping: "deadman"}
}
crit = (r) => (r["dead"])
messageFn = (r) => ("Check: ${r._check_name} is: ${r._level}")
data
|> v1["fieldsAsCols"]()
|> monitor["deadman"](t: experimental["subDuration"](from: now(), d: 5m))
|> monitor["check"](data: check, messageFn: messageFn, crit: crit)

19. Notifications
(Built on the Tasks subsystem)
Call
Notification
Endpoint
(e.g.
http.post)
_monitoring
(System
Bucket)
monitoring()

20. Notifications
(Built on the Tasks subsystem)
Call
Notification
Endpoint
(e.g.
http.post)
_monitoring
(System
Bucket)
monitoring()

21. An example of a notification task
import "influxdata/influxdb/monitor"
import "slack"
import "influxdata/influxdb/secrets"
import "experimental"
option task = {name: "Reads Deadman Notification", every: 1m, offset: 0s}
slack_endpoint = slack["endpoint"](url: "https://hooks.slack.com/services/SGSDFGER/HW36BWGDFEY/Slack_Token")
notification = {
_notification_rule_id: "NOTIF_RULE_ID",
_notification_rule_name: "NOTIF RULE NAME",
_notification_endpoint_id: "NOTIF_ENDPOINT",
_notification_endpoint_name: "ENDPOINT_NAME",
}
statuses = monitor["from"](start: -2m, fn: (r) => (r["reads"] == "deadman"))
crit = statuses
|> filter(fn: (r) => (r["_level"] == "crit"))
|> filter(fn: (r) => (r["_time"] >= experimental[
"subDuration"](from: now(), d: 1m)))
crit
|> monitor["notify"](data: notification, endpoint: slack_endpoint(mapFn: (r) =>
({channel: "", text: "Notification Rule: ${r._notification_rule_name} triggered by check:
${r._check_name}: ${r._message}",
color: if r["_level"] == "crit" then "danger" else "good"})))
Flux

22. Tasks and Invokable Scripts

23. API Invokable Script
App / Platform
/scripts/aggregate_1h_sum
Raw Data Transformed
Data
AWS Lambda
Node-Red
Web Connector
Azure Functions

24. API Invokable Script
App / Platform
/scripts/aggregate_1h_sum
Raw Data Transformed
Data
AWS Lambda
Node-Red
Web Connector
Azure Functions

25. Tasks leveraging Scripts
/scripts/aggregate_1h_sum
Raw Data Downsampled
Data
Developer value:
• Code reusability and
shareability
• Separation of roles
• Drastically improves
ease of use
Pass param
values

26. The Future of Tasks

27. Vision: Automate at scale
Schedule
Remote Invoke
Task Run
Flux
Script Language
Python
Javascript
Destination
Notification
Endpoint
Bucket
External
Datastore

28. Vision: Automate at scale
Schedule
Remote Invoke
Task Run
Flux
Script Language
Python
Javascript
Destination
Notification
Endpoint
Bucket
External
Datastore

29. Additional Resources
Free InfluxDB: OSS or Cloud - influxdata.com/cloud
Forums: community.influxdata.com
Slack: influxcommunity.slack.com
Reddit: r/InfluxData
Influx Community (GH): github.com/InfluxCommunity
Book: awesome.influxdata.com
Docs: docs.influxdata.com
Blogs: influxdata.com/blog
InfluxDB University: influxdata.com/university
How to guides: docs.influxdata.com/resources/how-to-guides/

30. T H A N K Y O U