Hi Rajesh,
By looking at your code I see that the memory would definitely grow because
you write big batches async and you will end up large number of batch
statements and the all end up slowing down. We recently migrated some data
to C* and what we did was we created a data stream and wrote in batches and
used a library which is sensitive to back-pressure of the stream. In your
implementation there's is no back-pressure to control it. We migrated data
pretty fast by keeping the CPU 100% constantly and achieve the highest
performance (used Scala with akka-streams and phantom-websudo).
I would consider using some streaming API to implement this. When you do
batching make sure you don't exceed the max match size, then things will
slow down anyways.
Lahiru
On Mon, Nov 7, 2016 at 8:51 AM, Rajesh Radhakrishnan <
rajesh.radhakrish...@phe.gov.uk> wrote:
> Hi
>
> We are trying to inject millions to data into a table by executing Batches
> of PreparedStatments.
>
> We found that when we use 'session.execute(batch)', it write more data but
> very very slow.
> However if we use 'session.execute_async(batch)' then its relatively fast
> but when it reaches certain limit, its fillup the memory (python process)
>
> Our implementation:
> Cassandra 3.7.0 cluster ring with 3 nodes (RedHat, 150GB Disk, 8GB of RAM
> each)
>
> Python 2.7.12
>
> Anybody know how to reduce the memory use of Cassandra-python driver API
> specifically for execute_async? Thank you!
>
>
>
> ===CODE ======================================
> sqlQuery = "INSERT INTO tableV (id, sample_name, pos, ref_base,
> var_base) values (?,?,?,?,?)"
> random_numbers_for_strains = random.sample(xrange(1,300), 200)
> random_numbers = random.sample(xrange(1,2000000), 200000)
>
> totalCounter = 0
> c = 0
> time_init = time.time()
> for random_number_strain in random_numbers_for_strains:
>
> sample_name = None
> sample_name = 'sample'+str(random_number_strain)
>
> cassandraCluster = CassandraCluster.CassandraCluster()
> cluster = cassandraCluster.create_cluster_with_protocol2()
> session = cluster.connect();
> #session.default_timeout = 1800
> session.set_keyspace(self.KEYSPACE_NAME)
>
> preparedStatement = session.prepare(sqlQuery)
>
> counter = 0
> c = c + 1
>
> for random_number in random_numbers:
>
> totalCounter += 1
> if counter == 0 :
> batch = BatchStatement()
>
> counter += 1
> if totalCounter % 10000 == 0 :
> print "Total Count "+ str(totalCounter)
>
> batch.add(preparedStatement.bind([ uuid.uuid1(),
> sample_name, random_number, random.choice('GT'), random.choice('AC')]))
> if counter % 50 == 0:
> session.execute_async(batch)
> #session.execute(batch)
> batch = None
> del batch
> counter = 0
>
> time.sleep(2);
> session.cluster.shutdown()
> random_number= None
> del random_number
> preparedStatement = None
> session = None
> del session
> cluster = None
> del cluster
> cassandraCluster = None
> del cassandraCluster
> gc.collect()
>
> ===CODE ======================================
>
>
>
> Kind regards,
> Rajesh Radhakrishnan
>
>
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