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From Radim Kolar <>
Subject cassandra read performance on large dataset
Date Mon, 28 Nov 2011 13:44:21 GMT

> I understand that my computer may be not as powerful as those used in 
> the other benchmarks,
> but it shouldn't be that far off (1:30), right?
cassandra has very fast writes. you can have read:write ratios like 1:1000

pure read workload on 1 billion rows without key/row cache on 2 node cluster
Running workload in 10 threads 1000 ops each.
Workload took 88.59 seconds, thruput 112.88 ops/sec

each node can do about 240 IOPS. Which means average 4 iops per read in 
cassandra on cold system.
After OS cache warms enough to cache indirect seek blocks it gets faster 
to almost ideal:
Workload took 79.76 seconds, thruput 200.59 ops/sec
Ideal cassandra read performance is (without caches) is 2 IOPS per read 
-> one io to read index, second to data.

pure write workload:
Running workload in 40 threads 100000 ops each.
Workload took 302.51 seconds, thruput 13222.62 ops/sec
write is slow here because nodes are running out of memory most likely 
due to memory leaks in 1.0 branch. Also writes in this test are not batched.

Cassandra is really awesome for its price tag. Getting similar numbers 
from Oracle will cost you way too much. For one 2 core Oracle licence 
suitable for processing large data you can get about  8 cassandra nodes 
- and dont forget that oracle needs some hardware too.  Transactions are 
not always needed for data warehousing - if you are importing chunks of 
data, you do not need to do rollbacks, just schedule  failed chunks for 
later processing. If you are able to code your app to work without 
transactions, cassandra is way to go.

Hadoop and cassandra are very good products for working with large data 
basically for just price of learning new technology. Usually cassandra 
is deployed first, its easy to get it running and day-to-day operations 
are simple. Hadoop follows later after discovering that cassandra is not 
really suitable for large batch jobs because it needs random access for 
data reading.

We finished processing migration from Commercional SQL to Hadoop/Cassa 
in 3 months, not only that it costs 10x less, we are able to process 
about 100 times larger datasets. Our largest dataset has 1200 billion rows.

Problems with this setup are:
    bloom filters are using too much memory. they should be configurable 
for applications where read performance is unimportant
    node startup is really slow
    data loaded into cassandra are about 2 times bigger then CSV export. 
(not really problem, diskspace is cheap, but there is kinda high per row 
    writing applications is harder then coding for SQL backend. Hadoop 
is way harder to use then cassandra.
    lack of good import/export tools for cassandra. especially lack of 
    must have knowledge of workarounds for hadoop bugs. Hadoop is not 
easy to use efficiently.
    index overhead is too big (about 100% slower) compared to index 
overhead in SQL databases (about 20% slower)
    no delete over index
    repair is slow

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