Thanks Kathleen, 1. So if i've got 4 df's and i want "dfv1 union dfv2 union
dfv3 union dfv4", would it first compute "dfv1 union dfv2" and "dfv3 union
dfv4" independently and simultaneously? then union their results? 2. Its going
to be hundreds of partitions to union, creating a temp view for each of them
might be slow? Sent using Zoho Mail ============ Forwarded message ============
From : kathleen li <kathleenli...@gmail.com> To : <onmstes...@zoho.com.invalid>
Cc : <user@spark.apache.org> Date : Wed, 21 Nov 2018 10:16:21 +0330 Subject :
Re: spark-sql force parallel union ============ Forwarded message ============
you might first write the code to construct query statement with "union all"
like below: scala> val query="select * from dfv1 union all select * from dfv2
union all select * from dfv3" query: String = select * from dfv1 union all
select * from dfv2 union all select * from dfv3 then write loop to register
each partition to a view like below: for (i <- 1 to 3){
df.createOrReplaceTempView("dfv"+i) } scala> spark.sql(query).explain ==
Physical Plan == Union :- LocalTableScan [_1#0, _2#1, _3#2] :- LocalTableScan
[_1#0, _2#1, _3#2] +- LocalTableScan [_1#0, _2#1, _3#2] You can use " roll up"
or "group set" for multiple dimension to replace "union" or "union all" On
Tue, Nov 20, 2018 at 8:34 PM onmstester onmstester
<onmstes...@zoho.com.invalid> wrote: I'm using Spark-Sql to query Cassandra
tables. In Cassandra, i've partitioned my data with time bucket and one id, so
based on queries i need to union multiple partitions with spark-sql and do the
aggregations/group-by on union-result, something like this: for(all cassandra
partitions){
DataSet<Row> currentPartition = sqlContext.sql(....);
unionResult = unionResult.union(currentPartition);
}
Increasing input (number of loaded partitions), increases response time more
than linearly because unions would be done sequentialy. Because there is no
harm in doing unions in parallel, and i dont know how to force spark to do them
in parallel, Right now i'm using a ThreadPool to Asyncronosly load all
partitions in my application (which may cause OOM), and somehow do the sort or
simple group by in java (Which make me think why even i'm using spark at all?)
The short question is: How to force spark-sql to load cassandra partitions in
parallel while doing union on them? Also I don't want too many tasks in spark,
with my Home-Made Async solution, i use coalesece(1) so one task is so fast
(only wait time on casandra). Sent using Zoho Mail
