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?
