You will also have to read/resolve multiple row instances (if you update 
records) and tombstones (if you delete records) yourself.

From: platon.tema [mailto:platon.t...@yandex.ru]
Sent: Tuesday, September 16, 2014 1:51 PM
To: user@cassandra.apache.org
Subject: Re: Direct IO with Spark and Hadoop over Cassandra

Thanks.

But 1) overcomes with C* API for commitlog and memtables or with mixed access 
(direct IO + traditional connectors or pure CQL if data model allows, we 
experimented with it).

2) is more complex for universal solution. In our case C* uses without 
replication (RF=1) because of huge data size (replication too expensive).
On 09/16/2014 03:40 PM, DuyHai Doan wrote:

If you access directly the C* sstables from those frameworks, you will:

1) miss live data which are in memory and not dumped yet to disk

2) skip the Dynamo layer of C* responsible for data consistency
Le 16 sept. 2014 10:58, "platon.tema" 
<platon.t...@yandex.ru<mailto:platon.t...@yandex.ru>> a écrit :
Hi.

As I see massive data processing tools (map\reduce) with C* data include

connectors
- Calliope http://tuplejump.github.io/calliope/
- Datastax spark cassandra connector 
https://github.com/datastax/spark-cassandra-connector
- Startio Deep https://github.com/Stratio/stratio-deep
- other free\commercial

runtime (job management and infrastructure)
- Spark
- Hadoop

But if I'm not mistaken all these solutions use network for data loading. In 
best case logic instance (some "job") run on the same node (wherethe 
corresponding range was found).

Why this logic can`t use direct C* IO (sstable reading from disk)? Any cons ?

Some time ago i read article (still can't find it) about academical research 
within Hadoop was modified to support this direct IO mode. According to that 
benchmarks direct IOgave a significant performance increase.


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