How to avoid high CPU usage with pysnmp

Question

I am using pysnmp and have encountered high CPU usage. I know netsnmp is written in C and pysnmp in Python, so I would expect the CPU usage times to be about 20-100% higher because of that. Instead I am seeing 20 times higher CPU usage times.

Am I using pysnmp correctly or could I do something to make it use less resources?

Test case 1 - PySNMP:

from pysnmp.entity.rfc3413.oneliner import cmdgen
import config
import yappi

yappi.start()
cmdGen = cmdgen.CommandGenerator()
errorIndication, errorStatus, errorIndex, varBindTable = cmdGen.nextCmd(
    cmdgen.CommunityData(config.COMMUNITY),
    cmdgen.UdpTransportTarget((config.HOST, config.PORT)),
    config.OID,
    lexicographicMode=False,
    ignoreNonIncreasingOid=True,
    lookupValue=False, lookupNames=False
)
for varBindTableRow in varBindTable:
    for name, val in varBindTableRow:
        print('%s' % (val,))
yappi.get_func_stats().print_all()

Test case 2 - NetSNMP:

import argparse
import netsnmp
import config
import yappi

yappi.start()
oid = netsnmp.VarList(netsnmp.Varbind('.'+config.OID))
res = netsnmp.snmpwalk(oid, Version = 2, DestHost=config.HOST, Community=config.COMMUNITY)
print(res)
yappi.get_func_stats().print_all()

If someone wants to test for himself, both test cases need a small file with settings, config.py:

HOST = '192.168.1.111'
COMMUNITY = 'public'
PORT = 161
OID = '1.3.6.1.2.1.2.2.1.8'

I have compared the returned values and they are the same - so both examples function correctly. The difference is in timings:

PySNMP:

Clock type: cpu
Ordered by: totaltime, desc

name                                    #n         tsub      ttot      tavg
..dgen.py:408 CommandGenerator.nextCmd  1          0.000108  1.890072  1.890072
..:31 AsynsockDispatcher.runDispatcher  1          0.005068  1.718650  1.718650
..r/lib/python2.7/asyncore.py:125 poll  144        0.010087  1.707852  0.011860
/usr/lib/python2.7/asyncore.py:81 read  72         0.001191  1.665637  0.023134
..UdpSocketTransport.handle_read_event  72         0.001301  1.664446  0.023117
..py:75 UdpSocketTransport.handle_read  72         0.001888  1.663145  0.023099
..base.py:32 AsynsockDispatcher._cbFun  72         0.001766  1.658938  0.023041
..:55 SnmpEngine.__receiveMessageCbFun  72         0.002194  1.656747  0.023010
..4 MsgAndPduDispatcher.receiveMessage  72         0.008587  1.654553  0.022980
..eProcessingModel.prepareDataElements  72         0.014170  0.831581  0.011550
../ber/decoder.py:585 Decoder.__call__  1224/216   0.111002  0.801783  0.000655
...py:312 SequenceDecoder.valueDecoder  288/144    0.034554  0.757069  0.002629
..tCommandGenerator.processResponsePdu  72         0.008425  0.730610  0.010147
..NextCommandGenerator._handleResponse  72         0.008692  0.712964  0.009902
...

NetSNMP:

Clock type: cpu
Ordered by: totaltime, desc

name                                    #n         tsub      ttot      tavg
..kages/netsnmp/client.py:227 snmpwalk  1          0.000076  0.103274  0.103274
..s/netsnmp/client.py:173 Session.walk  1          0.000024  0.077640  0.077640
..etsnmp/client.py:48 Varbind.__init__  72         0.008860  0.035225  0.000489
..tsnmp/client.py:111 Session.__init__  1          0.000055  0.025551  0.025551
...

So, netsnmp uses 0.103 s of CPU time and pysnmp uses 1.890 s of CPU time for the same operation. I find the results surprising... I have also tested the asynchronous mode, but the results were even a bit worse.

Am I doing something wrong (with pysnmp)?

UPDATE:

As per Ilya's suggestion, I have tryed using BULK instead of WALK. BULK is indeed much faster overall, but PySNMP still uses cca. 20x CPU time in comparison to netsnmp:

..dgen.py:496 CommandGenerator.bulkCmd  1          0.000105  0.726187  0.726187

Netsnmp:

..es/netsnmp/client.py:216 snmpgetbulk  1          0.000109  0.044421  0.044421

So the question still stands - can I make pySNMP less CPU intensive? Am I using it incorrectly?

Answer 1

Try using GETBULK instead of GETNEXT. With your code and Max-Repetitions=25 setting it gives 5x times performance improvement on my synthetic test.