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Monitoring Solr with Graphite and Carbon

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Monitoring Solr with Graphite and Carbon

· Big Data Zone ·
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This blog post requires graphite, carbon and python to be installed on your *ux. I'm running this on ubuntu.


To setup monitoring RAM usage of Solr instances (shards) with graphite, you will need two things:

1. backend: carbon
2. frontend: graphite

The data can be pushed to carbon using the following simple python script.

In my local cron I have:

1,6,11,16,21,26,31,36,41,46,51,56 * * * * \

The shell script is a wrapper for getting data from the remote server + pushing it to carbon with a python script:

scp -i /home/dmitry/keys/somekey.pem \
    user@remote_server:/path/memory.csv \ 
python \

An example entry in the MemoryStats.csv:


The command to produce a memory stat on ubuntu:

COMMAND="ssh user@remote_server pidstat -r -l -C java" | grep /path/to/shard

The python script is parsing the csv file (you may want to define your own format of the input file, I'm giving this as an example):

import sys
import time
import os
import platform
import subprocess
from socket import socket
import datetime, time


delay = 60
if len(sys.argv) > 1:
  delay = int( sys.argv[1] )

sock = socket()
  sock.connect( (CARBON_SERVER,CARBON_PORT) )
  print "Couldn't connect to %(server)s on port %(port)d, is carbon-agent.py running?" % { 'server':CARBON_SERVER, 'port':CARBON_PORT }

filename = '/home/dmitry/Downloads/MemoryStats.csv'

lines = []

with open(filename, 'r') as f:
  for line in f:

print lines
lines_to_send = []

for line in lines:
  if line.startswith("Time stamp"):
  shard = line.split(',')
  lines_to_send.append("system."+shard[1]+" %s %d" %(shard[5].replace("%", ""),int(time.mktime(datetime.datetime.strptime(shard[0], "%Y-%m-%dT%H:%M:%S.%fZ").timetuple()))))

#all lines must end in a newline
message = '\n'.join(lines_to_send) + '\n'
print "sending message\n"
print '-' * 80
print message

After the data has been pushed you can view it in graphite GWT based UI. The good thing about graphite vs jconsole or jvisualvm is that it persists data points so you can view and analyze them later.

For Amazon users, an alternative way of viewing the RAM usage graphs is with CloudWatch, although at the moment of this writing it allows storing 2 weeks worth of data only.


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