Leftouterjoin and join apis are super slow in spark. 100x slower than hadoop 

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> On 14-Jul-2015, at 10:59 PM, Wush Wu <wush...@gmail.com> wrote:
> 
> I don't understand.
> 
> By the way, the `joinWithCassandraTable` does improve my query time
> from 40 mins to 3 mins.
> 
> 
> 2015-07-15 13:19 GMT+08:00 ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com>:
>> I have explored spark joins for last few months (you can search my posts)
>> and its frustrating useless.
>> 
>>> On Tue, Jul 14, 2015 at 9:35 PM, Wush Wu <wush...@gmail.com> wrote:
>>> 
>>> Dear all,
>>> 
>>> I have found a post discussing the same thing:
>>> 
>>> https://groups.google.com/a/lists.datastax.com/forum/#!searchin/spark-connector-user/join/spark-connector-user/q3GotS-n0Wk/g-LPTteCEg0J
>>> 
>>> The solution is using "joinWithCassandraTable" and the documentation
>>> is here:
>>> https://github.com/datastax/spark-cassandra-connector/blob/v1.3.0-M2/doc/2_loading.md
>>> 
>>> Wush
>>> 
>>> 2015-07-15 12:15 GMT+08:00 Wush Wu <wush...@gmail.com>:
>>>> Dear all,
>>>> 
>>>> I am trying to join two RDDs, named rdd1 and rdd2.
>>>> 
>>>> rdd1 is loaded from a textfile with about 33000 records.
>>>> 
>>>> rdd2 is loaded from a table in cassandra which has about 3 billions
>>>> records.
>>>> 
>>>> I tried the following code:
>>>> 
>>>> ```scala
>>>> 
>>>> val rdd1 : (String, XXX) = sc.textFile(...).map(...)
>>>> import org.apache.spark.sql.cassandra.CassandraSQLContext
>>>> cc.setKeyspace("xxx")
>>>> val rdd2 : (String, String) = cc.sql("SELECT x, y FROM xxx").map(r =>
>>>> ...)
>>>> 
>>>> val result = rdd1.leftOuterJoin(rdd2)
>>>> result.take(20)
>>>> 
>>>> ```
>>>> 
>>>> However, the log shows that the spark loaded 3 billions records from
>>>> cassandra and only 33000 records left at the end.
>>>> 
>>>> Is there a way to query the cassandra based on the key in rdd1?
>>>> 
>>>> Here is some information of our system:
>>>> 
>>>> - The spark version is 1.3.1
>>>> - The cassandra version is 2.0.14
>>>> - The key of joining is the primary key of the cassandra table.
>>>> 
>>>> Best,
>>>> Wush
>>> 
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>> 
>> 
>> 
>> --
>> Deepak
>> 

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