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From "Petter. Andreas" <a.pet...@seeburger.de>
Subject Slow performance because of used-up "Waste" in AtomicBTreeColumns
Date Thu, 23 Jul 2015 10:55:14 GMT
Hello everyone,

we are experiencing performance issues with Cassandra overloading effects (dropped mutations
and node drop-outs) with the following workload:

create table test (year bigint, spread bigint, time bigint, batchid bigint, value set<text>,
primary key ((year, spread), time, batchid))
inserting data using an update statement ("+" operator to merge the sets). Data _is_being_ordered_
before the mutation is executed on the session. Number of inserts range from 400k to a few
millions.

Originally we were using scalding/summingbird and thought the problem to be in our Cassandra-storage-code.
To test that i wrote a simple cascading-hadoop job (not using BulkOutputFormat, but the Datastax
driver). I was a little bit surprised to still see Cassandra _overload_ (3 reducers/Hadoop-writers
and 3 co-located Cassandra nodes, as well as a setup with 4/4 nodes). The internal reason
seems to be that many worker threads go into state BLOCKED in AtomicBTreeColumns.addAllWithSizeDelta,
because s.th. called "waste" is used up and Cassandra switches to pessimistic locking.

However, i re-wrote the job using plain Hadoop-mapred (without cascading) but using the same
storage abstraction for writing and Cassandra _did_not_overload_ and the job has the great
write-performance i'm used to (and threads are not going into state BLOCKED).  We're totally
lost and puzzled.

So i have a few questions:
1. What is this "waste" used for? Is it a way of braking or load shedding? Why is locking
being used in AtomicBTreeColumns?
2. Is it o.k. to order columns before inserts are being performed?
3. What could be the reason that "waste" is being used-up in the cascading job and not  in
the plain Hadoop-job (sorting order?)?
4. Is there any way to circumvent using up "waste" (except for scaling nodes, which does not
seem to be the answer, as the plain Hadoop job runs Cassandra-"friendly")?

thanks in advance,
regards,
Andi








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