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From Cheng Lian <lian.cs....@gmail.com>
Subject Re: SparkSQL schemaRDD & MapPartitions calls - performance issues - columnar formats?
Date Sun, 11 Jan 2015 14:21:10 GMT

On 1/11/15 1:40 PM, Nathan McCarthy wrote:
> Thanks Cheng & Michael! Makes sense. Appreciate the tips!
>
> Idiomatic scala isn't performant. I’ll definitely start using while 
> loops or tail recursive methods. I have noticed this in the spark code 
> base.
>
> I might try turning off columnar compression (via 
> /spark.sql.inMemoryColumnarStorage.compressed=false /correct?) and see 
> how performance compares to the primitive objects. Would you expect to 
> see similar runtimes vs the primitive objects? We do have the luxury 
> of lots of memory at the moment so this might give us an additional 
> performance boost.
Turning off compression should be faster, but still slower than directly 
using primitive objects. Because Spark SQL also serializes all objects 
within a column into byte buffers in a compact format. However, this 
radically reduces number of Java objects in the heap and is more GC 
friendly. When running large queries, cost introduced by GC can be 
significant.
>
> Regarding the defensive copying of row objects. Can we switch this off 
> and just be aware of the risks? Is MapPartitions on SchemaRDDs and 
> operating on the Row object the most performant way to be flipping 
> between SQL & Scala user code? Is there anything else I could be doing?
This can be very dangerous and error prone. Whenever an operator tries 
to cache row objects, turning off defensive copying can introduce wrong 
query result. For example, sort-based shuffle caches rows to do sorting. 
In some cases, sample operator may also cache row objects. This is very 
implementation specific and may change between versions.
>
> Cheers,
> ~N
>
> From: Michael Armbrust <michael@databricks.com 
> <mailto:michael@databricks.com>>
> Date: Saturday, 10 January 2015 3:41 am
> To: Cheng Lian <lian.cs.zju@gmail.com <mailto:lian.cs.zju@gmail.com>>
> Cc: Nathan <nathan.mccarthy@quantium.com.au 
> <mailto:nathan.mccarthy@quantium.com.au>>, "user@spark.apache.org 
> <mailto:user@spark.apache.org>" <user@spark.apache.org 
> <mailto:user@spark.apache.org>>
> Subject: Re: SparkSQL schemaRDD & MapPartitions calls - performance 
> issues - columnar formats?
>
> The other thing to note here is that Spark SQL defensively copies rows 
> when we switch into user code.  This probably explains the difference 
> between 1 & 2.
>
> The difference between 1 & 3 is likely the cost of decompressing the 
> column buffers vs. accessing a bunch of uncompressed primitive objects.
>
> On Fri, Jan 9, 2015 at 6:59 AM, Cheng Lian <lian.cs.zju@gmail.com 
> <mailto:lian.cs.zju@gmail.com>> wrote:
>
>     Hey Nathan,
>
>     Thanks for sharing, this is a very interesting post :) My comments
>     are inlined below.
>
>     Cheng
>
>     On 1/7/15 11:53 AM, Nathan McCarthy wrote:
>>     Hi,
>>
>>     I’m trying to use a combination of SparkSQL and ‘normal'
>>     Spark/Scala via rdd.mapPartitions(…). Using the latest release
>>     1.2.0.
>>
>>     Simple example; load up some sample data from parquet on HDFS
>>     (about 380m rows, 10 columns) on a 7 node cluster.
>>
>>       val t = sqlC.parquetFile("/user/n/sales-tran12m.parquet”)
>>     t.registerTempTable("test1”)
>>       sqlC.cacheTable("test1”)
>>
>>     Now lets do some operations on it; I want the total sales &
>>     quantities sold for each hour in the day so I choose 3 out of the
>>     10 possible columns...
>>
>>       sqlC.sql("select Hour, sum(ItemQty), sum(Sales) from test1
>>     group by Hour").collect().foreach(println)
>>
>>     After the table has been 100% cached in memory, this takes around
>>     11 seconds.
>>
>>     Lets do the same thing but via a MapPartitions call (this isn’t
>>     production ready code but gets the job done).
>>
>>     val try2 = sqlC.sql("select Hour, ItemQty, Sales from test1”)
>>     rddPC.mapPartitions { case hrs =>
>>       val qtySum = new Array[Double](24)
>>       val salesSum = new Array[Double](24)
>>
>>       for(r <- hrs) {
>>         val hr = r.getInt(0)
>>         qtySum(hr) += r.getDouble(1)
>>         salesSum(hr) += r.getDouble(2)
>>       }
>>       (salesSum zip qtySum).zipWithIndex.map(_.swap).iterator
>>     }.reduceByKey((a,b) => (a._1 + b._1, a._2 +
>>     b._2)).collect().foreach(println)
>     I believe the evil thing that makes this snippet much slower is
>     the for-loop. According to my early benchmark done with Scala 2.9,
>     for-loop can be orders of magnitude slower than a simple
>     while-loop, especially when the body of the loop only does
>     something as trivial as this case. The reason is that Scala
>     for-loop is translated into corresponding
>     foreach/map/flatMap/withFilter function calls. And that's exactly
>     why Spark SQL tries to avoid for-loop or any other functional
>     style code in critical paths (where every row is touched), we also
>     uses reusable mutable row objects instead of the immutable version
>     to improve performance. You may check HiveTableScan,
>     ParquetTableScan, InMemoryColumnarTableScan etc. for reference.
>     Also, the `sum` function calls in your SQL code are translated
>     into `o.a.s.s.execution.Aggregate` operators, which also use
>     imperative while-loop and reusable mutable rows.
>
>     Another thing to notice is that the `hrs` iterator physically
>     points to underlying in-memory columnar byte buffers, and the `for
>     (r <- hrs) { ... }` loop actually decompresses and extracts values
>     from required byte buffers (this is the "unwrapping" processes you
>     mentioned below).
>>
>>     Now this takes around ~49 seconds… Even though test1 table is
>>     100% cached. The number of partitions remains the same…
>>
>>     Now if I create a simple RDD of a case class HourSum(hour: Int,
>>     qty: Double, sales: Double)
>>
>>     Convert the SchemaRDD;
>>     val rdd = sqlC.sql("select * from test1").map{ r =>
>>     HourSum(r.getInt(1), r.getDouble(7), r.getDouble(8)) }.cache()
>>     //cache all the data
>>     rdd.count()
>>
>>     Then run basically the same MapPartitions query;
>>
>>     rdd.mapPartitions { case hrs =>
>>       val qtySum = new Array[Double](24)
>>       val salesSum = new Array[Double](24)
>>
>>       for(r <- hrs) {
>>         val hr = r.hour
>>         qtySum(hr) += r.qty
>>         salesSum(hr) += r.sales
>>       }
>>       (salesSum zip qtySum).zipWithIndex.map(_.swap).iterator
>>     }.reduceByKey((a,b) => (a._1 + b._1, a._2 +
>>     b._2)).collect().foreach(println)
>>
>>     This takes around 1.5 seconds! Albeit the memory footprint is
>>     much larger.
>     I guess this 1.5 seconds doesn't include the time spent on caching
>     the simple RDD? As I've explained above, in the first
>     `mapPartitions` style snippet, columnar byte buffer unwrapping
>     happens within the `mapPartitions` call. However, in this version,
>     the unwrapping process happens when the `rdd.count()` action is
>     performed. At that point, all values of all columns are extracted
>     from underlying byte buffers, and the portion of data you need are
>     then manually selected and transformed into the simple case class
>     RDD via the `map` call.
>
>     If you include time spent on caching the simple case class RDD, it
>     should be even slower than the first `mapPartitions` version.
>>
>>     My thinking is that because SparkSQL does store things in a
>>     columnar format, there is some unwrapping to be done out of the
>>     column array buffers which takes time and for some reason this
>>     just takes longer when I switch out to map partitions (maybe its
>>     unwrapping the entire row, even though I’m using just a subset of
>>     columns, or maybe there is some object creation/autoboxing going
>>     on when calling getInt or getDouble)…
>>
>>     I’ve tried simpler cases too, like just summing sales. Running
>>     sum via SQL is fast (4.7 seconds), running a mapPartition sum on
>>     a double RDD is even faster (2.6 seconds). But MapPartitions on
>>     the SchemaRDD;
>>
>>     /sqlC.sql("select SalesInclGST from test1").mapPartitions(iter =>
>>     Iterator(iter.foldLeft(0.0)((t,r) => t+r.getDouble(0)))).sum/
>>
>>      takes a long time (33 seconds). In all these examples everything
>>     is fully cached in memory. And yes for these kinds of operations
>>     I can use SQL, but for more complex queries I’d much rather be
>>     using a combo of SparkSQL to select the data (so I get nice
>>     things like Parquet pushdowns etc.) & functional Scala!
>     Again, unfortunately, functional style code like `Iterator.sum`
>     and `Iterator.foldLeft` can be really slow on critical paths.
>>
>>     I think I’m doing something dumb… Is there something I should be
>>     doing to get faster performance on MapPartitions on SchemaRDDs?
>>     Is there some unwrapping going on in the background that catalyst
>>     does in a smart way that I’m missing?
>     It makes sense that people use both Spark SQL and Spark core,
>     especially when Spark SQL lacks features users need (like window
>     function, for now). The suggestion here is, if you really care
>     about performance (more than code readability and maintenance
>     cost), then avoid immutable, functional code whenever possible on
>     any critical paths...
>>
>>     Cheers,
>>     ~N
>>
>>     Nathan McCarthy
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>
>
> Nathan McCarthy
> QUANTIUM
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> Sydney NSW 2000
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