I have a column node which have many mac addresses. I want to map the mac-address to location and site. Eg - mac-address = 0005BCA, site = mumbai, location = 123. I am using processor.enum.mapping to map mac-address. But i am facing the issue that i am to define each mac-address manually in “[processors.enum.mapping.value_mappings]”, is there a way to do dynamically. Like for some mac-address we can have site and location without a way to manually define it. Will storing a key-value pair in redis or some other db will work, so that i don’t need to manually define keys and i should get all key-value pairs from redis db.
Can it be that we have the same problem but in a different context? I want to add the tags from one table to another. Maybe something similar can help you or we can help each other.
Here is the link to my topic: https://community.influxdata.com/t/join-two-different-sized-tables-by-a-tag-and-not-by-time/19120?u=patse
As far as I know not, that would be a very special use case for the enum processor.
But you could write your own processor plugin that does exactly this dynamic mapping with a processors.execd plugin.
I am not sure.
@Pratik_Das_Baghel wants to solve this on Telegraf level.
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ok thanks. i wasn’t sure about this.
Thanks. I will look for that.
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Hello @Pratik_Das_Baghel,
This might also be useful
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I will briefly outline here a working example that works like this.
It can serve as a starting point for you and must of course be adapted!
The processors plugin is not perfect - i would make it a bit more robust against errors.
You will need three external python 3 libraries:
pip install redis
pip install influxdb-client
pip install line-protocol-parser
First I start a local Redis instance with Docker:
docker pull redis
docker run -p 6379:6379 --name redis -d redis
Then we initially populate the Redis database with the dynamic mappings using this Python 3 script:
import json
import redis
macs = dict()
macs["0005BCA"] = {'site': 'mumbai', 'location': 123}
macs["0005BCB"] = {'site': 'delhi', 'location': 124}
macs["0005BCC"] = {'site': 'bangalore', 'location': 125}
r = redis.Redis() # default connection localhost:6379
print(r.ping()) # check connection
for key, value in macs.items():
r.set(key, json.dumps(value)) # write keys
for key in macs:
print(json.loads(r.get(key))) # read back keys
Then we have some input data (mac addresses) as an example in macs.log:
0005BCA
0005BCB
0005BCC
This is my telegraf.conf snippet:
[[inputs.tail]] # only for testing
files = ["macs.log"]
from_beginning = true
data_format = "value"
data_type = "string"
name_override = "macs"
[[processors.execd]]
namepass = ["macs"]
command = ["python", "execd-redis.py"]
[[outputs.file]] # only for testing
files = ["macs.out"]
influx_sort_fields = true
This is the Python 3 processors.execd plugin in the file execd-redis.py:
import json
import redis
from influxdb_client import Point
from line_protocol_parser import parse_line
r = redis.Redis() # default connection localhost:6379
r.ping() # check connection
while True:
try:
input_line = input() # read from stdin
except EOFError: # catch EOF error
break
except KeyboardInterrupt: # catch KeyboardInterrupt
break
else:
data = parse_line(input_line) # parse input line
mac = data['fields'].get('value')
if mac:
data['tags'].update({'mac': mac}) # make tag
data['fields'].update({'mac': mac}) # make field
data['fields'].pop('value') # remove field
if r.exists(mac):
details = json.loads(r.get(mac))
data['fields'].update(details)
point = Point.from_dict(data) # metric from dict
print(point.to_line_protocol()) # write to stdout
And voila, here is the output in macs.out after I ran Telegraf:
macs,mac=0005BCA,path=macs.log location=123i,mac="0005BCA",site="mumbai" 1617384804599588100
macs,mac=0005BCB,path=macs.log location=124i,mac="0005BCB",site="delhi" 1617384804599588100
macs,mac=0005BCC,path=macs.log location=125i,mac="0005BCC",site="bangalore" 1617384804599588100
Happy telegrafing 
Hello @Franky1,
Thank you so much for this detailed answer. You rock!
Great work. Thanks man
