python cassandra driver same insert performance as

Question

I'm trying to use Python async with Cassandra to see if I can write records to Cassandra faster than the CQL COPY command.

My python code looks like this:

from cassandra.cluster import Cluster
from cassandra import ConsistencyLevel
from cassandra.query import SimpleStatement
cluster = Cluster(['1.2.1.4'])

session = cluster.connect('test')

with open('dataImport.txt') as f:
    for line in f:
        query = SimpleStatement (
            "INSERT INTO tstTable (id, accts, info) VALUES (%s) " %(line),
            consistency_level=ConsistencyLevel.ONE)
        session.execute_async (query)

but its giving me the same performance as the COPY command...around 2,700 rows/sec....should it be faster with async?

Do I need to use multithreading in python? Just reading about it but not sure how it fits into this...

EDIT:

so I found something online that i'm trying to modify but can't get to quite work...I have this so far..also I split the file into 3 file into /Data/toImport/ dir:

import multiprocessing
import time
import os
from cassandra.cluster import Cluster
from cassandra import ConsistencyLevel
from cassandra.query import SimpleStatement


cluster = Cluster(['1.2.1.4'])

session = cluster.connect('test')

def mp_worker(inputArg):
        with open(inputArg[0]) as f:
            for line in f:
                query = SimpleStatement (
                    "INSERT INTO CustInfo (cust_id, accts, offers) values (%s)" %(line),
                    consistency_level=ConsistencyLevel.ONE)
                session.execute_async (query)


def mp_handler(inputData, nThreads = 8):
    p = multiprocessing.Pool(nThreads)
    p.map(mp_worker, inputData, chunksize=1)
    p.close()
    p.join()

if __name__ == '__main__':
    temp_in_data = file_list
    start = time.time()
    in_dir = '/Data/toImport/'
    N_Proc = 8
    file_data = [(in_dir) for i in temp_in_data]

    print '----------------------------------Start Working!!!!-----------------------------'
    print 'Number of Processes using: %d' %N_Proc
    mp_handler(file_data, N_Proc)
    end = time.time()
    time_elapsed = end - start
    print '----------------------------------All Done!!!!-----------------------------'
    print "Time elapsed: {} seconds".format(time_elapsed)

but get this error:

Traceback (most recent call last):
  File "multiCass.py", line 27, in <module>
    temp_in_data = file_list
NameError: name 'file_list' is not defined

Answer 1

This post A Multiprocessing Example for Improved Bulk Data Throughput provides all the details needed to improve the performance of bulk data ingestion. Basically there are 3 mechanisms and additional tuning can be done based on your use-case & hw:

1. single process (that's the case in your example)
2. multi-processing single queries
3. multi-processing concurrent queries

Size of batches and concurrency are the variables you'll have to play with yourself.

Answer 2

got it working like this:

import multiprocessing
import time
import os
from cassandra.cluster import Cluster
from cassandra import ConsistencyLevel
from cassandra.query import SimpleStatement



def mp_worker(inputArg):
        cluster = Cluster(['1.2.1.4'])
        session = cluster.connect('poc')


        with open(inputArg[0]) as f:
            for line in f:
                query = SimpleStatement (
                    "INSERT INTO testTable (cust_id, accts, offers) values (%s)" %(line),
                    consistency_level=ConsistencyLevel.ONE)
                session.execute_async (query)


def mp_handler(inputData, nThreads = 8):
    p = multiprocessing.Pool(nThreads)
    p.map(mp_worker, inputData, chunksize=1)
    p.close()
    p.join()

if __name__ == '__main__':
    temp_in_data = ['/toImport/part-00000', '/toImport/part-00001', '/toImport/part-00002']
    start = time.time()
    N_Proc = 3
    file_data = [(i,) for i in temp_in_data]

    print '----------------------------------Start Working!!!!-----------------------------'
    print 'Number of Processes using: %d' %N_Proc
    mp_handler(file_data, N_Proc)
    end = time.time()
    time_elapsed = end - start
    print '----------------------------------All Done!!!!-----------------------------'
    print "Time elapsed: {} seconds".format(time_elapsed)