Join data with Flux
The join() function merges two or more
input streams, whose values are equal on a set of common columns, into a single output stream.
Flux allows you to join on any columns common between two data streams and opens the door
for operations such as cross-measurement joins and math across measurements.
To illustrate a join operation, use data captured by Telegraf and stored in InfluxDB - memory usage and processes.
In this guide, we’ll join two data streams, one representing memory usage and the other representing the total number of running processes, then calculate the average memory usage per running process.
If you’re just getting started with Flux queries, check out the following:
- Get started with Flux for a conceptual overview of Flux and parts of a Flux query.
- Execute queries to discover a variety of ways to run your queries.
Define stream variables
In order to perform a join, you must have two streams of data. Assign a variable to each data stream.
Memory used variable
Define a memUsed variable that filters on the mem measurement and the used field.
This returns the amount of memory (in bytes) used.
memUsed stream definition
memUsed = from(bucket: "example-bucket")
|> range(start: -5m)
|> filter(fn: (r) => r._measurement == "mem" and r._field == "used")
memUsed data output
Table: keys: [_start, _stop, _field, _measurement, host]
_start:time _stop:time _field:string _measurement:string host:string _time:time _value:int
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------ ------------------------------ --------------------------
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:00.000000000Z 10956333056
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:10.000000000Z 11014008832
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:20.000000000Z 11373428736
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:30.000000000Z 11001421824
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:40.000000000Z 10985852928
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:50.000000000Z 10992279552
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:00.000000000Z 11053568000
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:10.000000000Z 11092242432
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:20.000000000Z 11612774400
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:30.000000000Z 11131961344
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:40.000000000Z 11124805632
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:50.000000000Z 11332464640
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:00.000000000Z 11176923136
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:10.000000000Z 11181068288
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:20.000000000Z 11182579712
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:30.000000000Z 11238862848
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:40.000000000Z 11275296768
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:50.000000000Z 11225411584
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:00.000000000Z 11252690944
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:10.000000000Z 11227029504
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:20.000000000Z 11201646592
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:30.000000000Z 11227897856
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:40.000000000Z 11330428928
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:50.000000000Z 11347976192
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:00.000000000Z 11368271872
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:10.000000000Z 11269623808
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:20.000000000Z 11295637504
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:30.000000000Z 11354423296
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:40.000000000Z 11379687424
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:50.000000000Z 11248926720
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:55:00.000000000Z 11292524544
Total processes variable
Define a procTotal variable that filters on the processes measurement and the total field.
This returns the number of running processes.
procTotal stream definition
procTotal = from(bucket: "example-bucket")
|> range(start: -5m)
|> filter(fn: (r) => r._measurement == "processes" and r._field == "total")
procTotal data output
Table: keys: [_start, _stop, _field, _measurement, host]
_start:time _stop:time _field:string _measurement:string host:string _time:time _value:int
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------ ------------------------------ --------------------------
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:00.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:10.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:20.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:30.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:40.000000000Z 469
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:50.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:00.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:10.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:20.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:30.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:40.000000000Z 469
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:50.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:00.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:10.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:20.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:30.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:40.000000000Z 472
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:50.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:00.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:10.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:20.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:30.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:40.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:50.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:00.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:10.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:20.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:30.000000000Z 473
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:40.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:50.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:55:00.000000000Z 471
Join the two data streams
With the two data streams defined, use the join() function to join them together.
join() requires two parameters:
tables
A map of tables to join with keys by which they will be aliased.
In the example below, mem is the alias for memUsed and proc is the alias for procTotal.
on
An array of strings defining the columns on which the tables will be joined. Both tables must have all columns specified in this list.
join(
tables: {mem:memUsed, proc:procTotal},
on: ["_time", "_stop", "_start", "host"],
)
Joined output table
Table: keys: [_field_mem, _field_proc, _measurement_mem, _measurement_proc, _start, _stop, host]
_field_mem:string _field_proc:string _measurement_mem:string _measurement_proc:string _start:time _stop:time host:string _time:time _value_mem:int _value_proc:int
---------------------- ---------------------- ----------------------- ------------------------ ------------------------------ ------------------------------ ------------------------ ------------------------------ -------------------------- --------------------------
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:00.000000000Z 10956333056 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:10.000000000Z 11014008832 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:20.000000000Z 11373428736 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:30.000000000Z 11001421824 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:40.000000000Z 10985852928 469
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:50.000000000Z 10992279552 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:00.000000000Z 11053568000 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:10.000000000Z 11092242432 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:20.000000000Z 11612774400 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:30.000000000Z 11131961344 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:40.000000000Z 11124805632 469
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:50.000000000Z 11332464640 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:00.000000000Z 11176923136 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:10.000000000Z 11181068288 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:20.000000000Z 11182579712 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:30.000000000Z 11238862848 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:40.000000000Z 11275296768 472
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:50.000000000Z 11225411584 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:00.000000000Z 11252690944 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:10.000000000Z 11227029504 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:20.000000000Z 11201646592 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:30.000000000Z 11227897856 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:40.000000000Z 11330428928 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:50.000000000Z 11347976192 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:00.000000000Z 11368271872 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:10.000000000Z 11269623808 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:20.000000000Z 11295637504 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:30.000000000Z 11354423296 473
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:40.000000000Z 11379687424 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:50.000000000Z 11248926720 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:55:00.000000000Z 11292524544 471
Notice the output table includes the following columns:
_field_mem_field_proc_measurement_mem_measurement_proc_value_mem_value_proc
These represent the columns with values unique to the two input tables.
Calculate and create a new table
With the two streams of data joined into a single table, use the
map() function
to build a new table by mapping the existing _time column to a new _time
column and dividing _value_mem by _value_proc and mapping it to a
new _value column.
join(tables: {mem: memUsed, proc: procTotal}, on: ["_time", "_stop", "_start", "host"])
|> map(fn: (r) => ({_time: r._time, _value: r._value_mem / r._value_proc}))
Mapped table
Table: keys: [_field_mem, _field_proc, _measurement_mem, _measurement_proc, _start, _stop, host]
_field_mem:string _field_proc:string _measurement_mem:string _measurement_proc:string _start:time _stop:time host:string _time:time _value:int
---------------------- ---------------------- ----------------------- ------------------------ ------------------------------ ------------------------------ ------------------------ ------------------------------ --------------------------
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:00.000000000Z 23311346
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:10.000000000Z 23434061
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:20.000000000Z 24147407
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:30.000000000Z 23407280
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:40.000000000Z 23423993
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:50.000000000Z 23338173
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:00.000000000Z 23518229
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:10.000000000Z 23600515
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:20.000000000Z 24708030
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:30.000000000Z 23685024
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:40.000000000Z 23720267
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:50.000000000Z 24060434
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:00.000000000Z 23730197
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:10.000000000Z 23789506
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:20.000000000Z 23792722
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:30.000000000Z 23861704
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:40.000000000Z 23888340
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:50.000000000Z 23833145
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:00.000000000Z 23941895
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:10.000000000Z 23887296
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:20.000000000Z 23833290
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:30.000000000Z 23838424
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:40.000000000Z 24056112
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:50.000000000Z 24093367
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:00.000000000Z 24136458
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:10.000000000Z 23977922
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:20.000000000Z 23982245
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:30.000000000Z 24005123
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:40.000000000Z 24160695
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:50.000000000Z 23883071
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:55:00.000000000Z 23975635
This table represents the average amount of memory in bytes per running process.
Real world example
The following function calculates the batch sizes written to an InfluxDB cluster by joining
fields from httpd and write measurements in order to compare pointReq and writeReq.
The results are grouped by cluster ID so you can make comparisons across clusters.
batchSize = (cluster_id, start=-1m, interval=10s) => {
httpd = from(bucket: "telegraf")
|> range(start: start)
|> filter(fn: (r) => r._measurement == "influxdb_httpd" and r._field == "writeReq" and r.cluster_id == cluster_id)
|> aggregateWindow(every: interval, fn: mean)
|> derivative(nonNegative: true, unit: 60s)
write = from(bucket: "telegraf")
|> range(start: start)
|> filter(fn: (r) => r._measurement == "influxdb_write" and r._field == "pointReq" and r.cluster_id == cluster_id)
|> aggregateWindow(every: interval, fn: max)
|> derivative(nonNegative: true, unit: 60s)
return join(tables: {httpd: httpd, write: write}, on: ["_time", "_stop", "_start", "host"])
|> map(fn: (r) => ({_time: r._time, _value: r._value_httpd / r._value_write}))
|> group(columns: cluster_id)
}
batchSize(cluster_id: "enter cluster id here")
To see how join works in InfluxDB, watch the following video:
Support and feedback
Thank you for being part of our community! We welcome and encourage your feedback and bug reports for InfluxDB and this documentation. To find support, the following resources are available:
InfluxDB Cloud and InfluxDB Enterprise customers can contact InfluxData Support.