My basic test is here - https://github.com/rohitkapoor1/sparkPushDownAggregate
From: German Schiavon <gschiavonsp...@gmail.com> Date: Thursday, 4 November 2021 at 2:17 AM To: huaxin gao <huaxin.ga...@gmail.com> Cc: Kapoor, Rohit <rohit.kap...@envestnet.com>, user@spark.apache.org <user@spark.apache.org> Subject: Re: [Spark SQL]: Aggregate Push Down / Spark 3.2 EXTERNAL MAIL: USE CAUTION BEFORE CLICKING LINKS OR OPENING ATTACHMENTS. ALWAYS VERIFY THE SOURCE OF MESSAGES. Hi, Rohit, can you share how it looks using DSv2? Thanks! On Wed, 3 Nov 2021 at 19:35, huaxin gao <huaxin.ga...@gmail.com<mailto:huaxin.ga...@gmail.com>> wrote: Great to hear. Thanks for testing this! On Wed, Nov 3, 2021 at 4:03 AM Kapoor, Rohit <rohit.kap...@envestnet.com<mailto:rohit.kap...@envestnet.com>> wrote: Thanks for your guidance Huaxin. I have been able to test the push down operators successfully against Postgresql using DS v2. From: huaxin gao <huaxin.ga...@gmail.com<mailto:huaxin.ga...@gmail.com>> Date: Tuesday, 2 November 2021 at 12:35 AM To: Kapoor, Rohit <rohit.kap...@envestnet.com<mailto:rohit.kap...@envestnet.com>> Subject: Re: [Spark SQL]: Aggregate Push Down / Spark 3.2 EXTERNAL MAIL: USE CAUTION BEFORE CLICKING LINKS OR OPENING ATTACHMENTS. ALWAYS VERIFY THE SOURCE OF MESSAGES. EXTERNAL MAIL: USE CAUTION BEFORE CLICKING LINKS OR OPENING ATTACHMENTS. ALWAYS VERIFY THE SOURCE OF MESSAGES. No need to write a customized data source reader. You may want to follow the example here https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/v2/jdbc/JDBCTableCatalogSuite.scala#L40<https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/v2/jdbc/JDBCTableCatalogSuite.scala#L40> to use DS v2. The example uses h2 database. Please modify it to use postgresql. Huaxin On Mon, Nov 1, 2021 at 11:21 AM Kapoor, Rohit <rohit.kap...@envestnet.com<mailto:rohit.kap...@envestnet.com>> wrote: Hi Huaxin, Thanks a lot for your response. Do I need to write a custom data source reader (in my case, for PostgreSql) using the Spark DS v2 APIs, instead of the standard spark.read.format(“jdbc”) ? Thanks, Rohit From: huaxin gao <huaxin.ga...@gmail.com<mailto:huaxin.ga...@gmail.com>> Date: Monday, 1 November 2021 at 11:32 PM To: Kapoor, Rohit <rohit.kap...@envestnet.com<mailto:rohit.kap...@envestnet.com>> Cc: user@spark.apache.org<mailto:user@spark.apache.org> <user@spark.apache.org<mailto:user@spark.apache.org>> Subject: Re: [Spark SQL]: Aggregate Push Down / Spark 3.2 EXTERNAL MAIL: USE CAUTION BEFORE CLICKING LINKS OR OPENING ATTACHMENTS. ALWAYS VERIFY THE SOURCE OF MESSAGES. EXTERNAL MAIL: USE CAUTION BEFORE CLICKING LINKS OR OPENING ATTACHMENTS. ALWAYS VERIFY THE SOURCE OF MESSAGES. Hi Rohit, Thanks for testing this. Seems to me that you are using DS v1. We only support aggregate push down in DS v2. Could you please try again using DS v2 and let me know how it goes? Thanks, Huaxin On Mon, Nov 1, 2021 at 10:39 AM Chao Sun <sunc...@apache.org<mailto:sunc...@apache.org>> wrote: ---------- Forwarded message --------- From: Kapoor, Rohit <rohit.kap...@envestnet.com<mailto:rohit.kap...@envestnet.com>> Date: Mon, Nov 1, 2021 at 6:27 AM Subject: [Spark SQL]: Aggregate Push Down / Spark 3.2 To: user@spark.apache.org<mailto:user@spark.apache.org> <user@spark.apache.org<mailto:user@spark.apache.org>> Hi, I am testing the aggregate push down for JDBC after going through the JIRA - https://issues.apache.org/jira/browse/SPARK-34952<https://issues.apache.org/jira/browse/SPARK-34952> I have the latest Spark 3.2 setup in local mode (laptop). I have PostgreSQL v14 locally on my laptop. I am trying a basic aggregate query on “emp” table that has 1000002 rows and a simple schema with 3 columns (empid, ename and sal) as below: val jdbcString = "jdbc:postgresql://" + "localhost" + ":5432/postgres" val jdbcDF = spark.read .format("jdbc") .option("url", jdbcString) .option("dbtable", "emp") .option("pushDownAggregate","true") .option("user", "xxxx") .option("password", "xxxx") .load() .where("empid > 1") .agg(max("SAL")).alias("max_sal") The complete plan details are: == Parsed Logical Plan == SubqueryAlias max_sal +- Aggregate [max(SAL#2) AS max(SAL)#10] +- Filter (empid#0 > 1) +- Relation [empid#0,ename#1,sal#2] JDBCRelation(emp) [numPartitions=1] == Analyzed Logical Plan == max(SAL): int SubqueryAlias max_sal +- Aggregate [max(SAL#2) AS max(SAL)#10] +- Filter (empid#0 > 1) +- Relation [empid#0,ename#1,sal#2] JDBCRelation(emp) [numPartitions=1] == Optimized Logical Plan == Aggregate [max(SAL#2) AS max(SAL)#10] +- Project [sal#2] +- Filter (isnotnull(empid#0) AND (empid#0 > 1)) +- Relation [empid#0,ename#1,sal#2] JDBCRelation(emp) [numPartitions=1] == Physical Plan == AdaptiveSparkPlan isFinalPlan=false +- HashAggregate(keys=[], functions=[max(SAL#2)], output=[max(SAL)#10]) +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#15] +- HashAggregate(keys=[], functions=[partial_max(SAL#2)], output=[max#13]) +- Scan JDBCRelation(emp) [numPartitions=1] [sal#2] PushedAggregates: [], PushedFilters: [*IsNotNull(empid), *GreaterThan(empid,1)], PushedGroupby: [], ReadSchema: struct<sal:int> I also checked the sql submitted to the database, querying pg_stat_statements, and it confirms that the aggregate was not pushed down to the database. Here is the query submitted to the database: SELECT "sal" FROM emp WHERE ("empid" IS NOT NULL) AND ("empid" > $1) All the rows are read and aggregated in the Spark layer. Is there any configuration I missing here? Why is aggregate push down not working for me? Any pointers would be greatly appreciated. Thanks, Rohit ________________________________ Disclaimer: The information in this email is confidential and may be legally privileged. Access to this Internet email by anyone else other than the recipient is unauthorized. Envestnet, Inc. and its affiliated companies do not accept time-sensitive transactional messages, including orders to buy and sell securities, account allocation instructions, or any other instructions affecting a client account, via e-mail. If you are not the intended recipient of this email, any disclosure, copying, or distribution of it is prohibited and may be unlawful. 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