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From Pralabh Kumar <pralabhku...@gmail.com>
Subject Re: How to tune the performance of Tpch query5 within Spark
Date Mon, 17 Jul 2017 13:50:47 GMT
Hi

To read file parallely , you can follow the below code.


 case class readData (fileName : String , spark : SparkSession) extends
Callable[Dataset[Row]]{
  override def call(): Dataset[Row] = {
    spark.read.parquet(fileName)
   // spark.read.csv(fileName)
  }
}

val spark =  SparkSession.builder()
     .appName("practice")
     .config("spark.scheduler.mode","FAIR")
     .enableHiveSupport().getOrCreate()
   val pool = Executors.newFixedThreadPool(6)
   val list = new util.ArrayList[Future[Dataset[Row]]]()


 for(fileName<-"orders,lineitem,customer,supplier,region,nation".split(",")){
     val o1 = new readData(fileName,spark)
     //pool.submit(o1).
     list.add(pool.submit(o1))
   }
   val rddList = new ArrayBuffer[Dataset[Row]]()
   for(result <- list){
     rddList += result.get()
   }

   pool.shutdown()
   pool.awaitTermination(Long.MaxValue, TimeUnit.NANOSECONDS)
   for(finalData<-rddList){
     finalData.show()
   }


This will read data in parallel ,which I think is your main bottleneck.

Regards
Pralabh Kumar



On Mon, Jul 17, 2017 at 6:25 PM, vaquar khan <vaquar.khan@gmail.com> wrote:

> Could you please let us know your Spark version?
>
>
> Regards,
> vaquar khan
>
>
> On Jul 17, 2017 12:18 AM, "163" <hewenting_ict@163.com> wrote:
>
>> I change the UDF but the performance seems still slow. What can I do else?
>>
>>
>> 在 2017年7月14日,下午8:34,Wenchen Fan <cloud0fan@gmail.com> 写道:
>>
>> Try to replace your UDF with Spark built-in expressions, it should be as
>> simple as `$”x” * (lit(1) - $”y”)`.
>>
>> On 14 Jul 2017, at 5:46 PM, 163 <hewenting_ict@163.com> wrote:
>>
>> I modify the tech query5 to DataFrame:
>>
>> val forders = spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/orders*”*).filter("o_orderdate
< 1995-01-01 and o_orderdate >= 1994-01-01").select("o_custkey", "o_orderkey")
>> val flineitem = spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/lineitem")
>> val fcustomer = spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/customer")
>> val fsupplier = spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/supplier")
>> val fregion = spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/region*”*).where("r_name
= 'ASIA'").select($"r_regionkey")
>> val fnation = spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/nation*”*)
>>
>> val decrease = udf { (x: Double, y: Double) => x * (1 - y) }
>>
>> val res =   flineitem.join(forders, $"l_orderkey" === forders("o_orderkey"))
>>      .join(fcustomer, $"o_custkey" === fcustomer("c_custkey"))
>>      .join(fsupplier, $"l_suppkey" === fsupplier("s_suppkey") && $"c_nationkey"
=== fsupplier("s_nationkey"))
>>      .join(fnation, $"s_nationkey" === fnation("n_nationkey"))
>>      .join(fregion, $"n_regionkey" === fregion("r_regionkey"))
>>      .select($"n_name", decrease($"l_extendedprice", $"l_discount").as("value"))
>>      .groupBy($"n_name")
>>      .agg(sum($"value").as("revenue"))
>>      .sort($"revenue".desc).show()
>>
>>
>> My environment is one master(Hdfs-namenode), four workers(HDFS-datanode), each with
40 cores and 128GB memory.  TPCH 100G stored on HDFS using parquet format.
>>
>> It executed about 1.5m, I found that read these 6 tables using spark.read.parqeut
is sequential, How can I made this to run parallelly ?
>>
>>  I’ve already set data locality and spark.default.parallelism, spark.serializer,
using G1, But the runtime  is still not reduced.
>>
>> And is there any advices for me to tuning this performance?
>>
>> Thank you.
>>
>> Wenting He
>>
>>
>>
>>
>>

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