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 <cloud0...@gmail.com> 写道： > > Try to replace your UDF with Spark built-in expressions, it should be as > simple as `$”x” * (lit(1) - $”y”)`. > >> On 14 Jul 2017, at 5:46 PM, 163 <hewenting_...@163.com >> <mailto:hewenting_...@163.com>> wrote: >> >> I modify the tech query5 to DataFrame: >> val forders = >> spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/orders >> >> <hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/orders>”).filter("o_orderdate >> < 1995-01-01 and o_orderdate >= 1994-01-01").select("o_custkey", >> "o_orderkey") >> val flineitem = >> spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/lineitem >> <hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/lineitem>") >> val fcustomer = >> spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/customer >> <hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/customer>") >> val fsupplier = >> spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/supplier >> <hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/supplier>") >> val fregion = >> spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/region >> >> <hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/region>”).where("r_name >> = 'ASIA'").select($"r_regionkey") >> val fnation = >> spark.read.parquet("hdfs://dell127:20500/SparkParquetDoubleTimestamp100G/nation >> <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 >> >
