By the way turn off the code generation could be an option to try, sometime code generation could introduce slowness
在2015年09月11日 15:58，Cheng, Hao 写道: Can you confirm if the query really run in the cluster mode? Not the local mode. Can you print the call stack of the executor when the query is running? BTW: spark.shuffle.reduceLocality.enabled is the configuration of Spark, not Spark SQL. From: Todd [mailto:bit1...@163.com] Sent: Friday, September 11, 2015 3:39 PM To: Todd Cc: Cheng, Hao; Jesse F Chen; Michael Armbrust; user@spark.apache.org Subject: Re:Re:RE: Re:RE: spark 1.5 SQL slows down dramatically by 50%+ compared with spark 1.4.1 SQL I add the following two options: spark.sql.planner.sortMergeJoin=false spark.shuffle.reduceLocality.enabled=false But it still performs the same as not setting them two. One thing is that on the spark ui, when I click the SQL tab, it shows an empty page but the header title 'SQL',there is no table to show queries and execution plan information. At 2015-09-11 14:39:06, "Todd" <bit1...@163.com> wrote: Thanks Hao. Yes，it is still low as SMJ。Let me try the option your suggested, At 2015-09-11 14:34:46, "Cheng, Hao" <hao.ch...@intel.com> wrote: You mean the performance is still slow as the SMJ in Spark 1.5? Can you set the spark.shuffle.reduceLocality.enabled=false when you start the spark-shell/spark-sql? It’s a new feature in Spark 1.5, and it’s true by default, but we found it probably causes the performance reduce dramatically. From: Todd [mailto:bit1...@163.com] Sent: Friday, September 11, 2015 2:17 PM To: Cheng, Hao Cc: Jesse F Chen; Michael Armbrust; user@spark.apache.org Subject: Re:RE: spark 1.5 SQL slows down dramatically by 50%+ compared with spark 1.4.1 SQL Thanks Hao for the reply. I turn the merge sort join off, the physical plan is below, but the performance is roughly the same as it on... == Physical Plan == TungstenProject [ss_quantity#10,ss_list_price#12,ss_coupon_amt#19,ss_cdemo_sk#4,ss_item_sk#2,ss_promo_sk#8,ss_sold_date_sk#0] ShuffledHashJoin [ss_item_sk#2], [ss_item_sk#25], BuildRight TungstenExchange hashpartitioning(ss_item_sk#2) ConvertToUnsafe Scan ParquetRelation[hdfs://ns1/tmp/spark_perf/scaleFactor=30/useDecimal=true/store_sales][ss_promo_sk#8,ss_quantity#10,ss_cdemo_sk#4,ss_list_price#12,ss_coupon_amt#19,ss_item_sk#2,ss_sold_date_sk#0] TungstenExchange hashpartitioning(ss_item_sk#25) ConvertToUnsafe Scan ParquetRelation[hdfs://ns1/tmp/spark_perf/scaleFactor=30/useDecimal=true/store_sales][ss_item_sk#25] Code Generation: true At 2015-09-11 13:48:23, "Cheng, Hao" <hao.ch...@intel.com> wrote: This is not a big surprise the SMJ is slower than the HashJoin, as we do not fully utilize the sorting yet, more details can be found at https://issues.apache.org/jira/browse/SPARK-2926 . Anyway, can you disable the sort merge join by “spark.sql.planner.sortMergeJoin=false;” in Spark 1.5, and run the query again? In our previous testing, it’s about 20% slower for sort merge join. I am not sure if there anything else slow down the performance. Hao From: Jesse F Chen [mailto:jfc...@us.ibm.com] Sent: Friday, September 11, 2015 1:18 PM To: Michael Armbrust Cc: Todd; user@spark.apache.org Subject: Re: spark 1.5 SQL slows down dramatically by 50%+ compared with spark 1.4.1 SQL Could this be a build issue (i.e., sbt package)? If I ran the same jar build for 1.4.1 in 1.5, I am seeing large regression too in queries (all other things identical)... I am curious, to build 1.5 (when it isn't released yet), what do I need to do with the build.sbt file? any special parameters i should be using to make sure I load the latest hive dependencies? Michael Armbrust ---09/10/2015 11:07:28 AM---I've been running TPC-DS SF=1500 daily on Spark 1.4.1 and Spark 1.5 on S3, so this is surprising. I From: Michael Armbrust <mich...@databricks.com> To: Todd <bit1...@163.com> Cc: "user@spark.apache.org" <user@spark.apache.org> Date: 09/10/2015 11:07 AM Subject: Re: spark 1.5 SQL slows down dramatically by 50%+ compared with spark 1.4.1 SQL I've been running TPC-DS SF=1500 daily on Spark 1.4.1 and Spark 1.5 on S3, so this is surprising. In my experiments Spark 1.5 is either the same or faster than 1.4 with only small exceptions. A few thoughts, - 600 partitions is probably way too many for 6G of data. - Providing the output of explain for both runs would be helpful whenever reporting performance changes. On Thu, Sep 10, 2015 at 1:24 AM, Todd <bit1...@163.com> wrote: Hi, I am using data generated with sparksqlperf(https://github.com/databricks/spark-sql-perf) to test the spark sql performance (spark on yarn, with 10 nodes) with the following code (The table store_sales is about 90 million records, 6G in size） val outputDir="hdfs://tmp/spark_perf/scaleFactor=30/useDecimal=true/store_sales" val name="store_sales" sqlContext.sql( s""" |CREATE TEMPORARY TABLE ${name} |USING org.apache.spark.sql.parquet |OPTIONS ( | path '${outputDir}' |) """.stripMargin) val sql=""" |select | t1.ss_quantity, | t1.ss_list_price, | t1.ss_coupon_amt, | t1.ss_cdemo_sk, | t1.ss_item_sk, | t1.ss_promo_sk, | t1.ss_sold_date_sk |from store_sales t1 join store_sales t2 on t1.ss_item_sk = t2.ss_item_sk |where | t1.ss_sold_date_sk between 2450815 and 2451179 """.stripMargin val df = sqlContext.sql(sql) df.rdd.foreach(row=>Unit) With 1.4.1, I can finish the query in 6 minutes, but I need 10+ minutes with 1.5. The configuration are basically the same, since I copy the configuration from 1.4.1 to 1.5: sparkVersion 1.4.1 1.5.0 scaleFactor 30 30 spark.sql.shuffle.partitions 600 600 spark.sql.sources.partitionDiscovery.enabled true true spark.default.parallelism 200 200 spark.driver.memory 4G 4G 4G spark.executor.memory 4G 4G spark.executor.instances 10 10 spark.shuffle.consolidateFiles true true spark.storage.memoryFraction 0.4 0.4 spark.executor.cores 3 3 I am not sure where is going wrong,any ideas?
