Hi Xiao, Just report this with JIRA SPARK-28103.
https://issues.apache.org/jira/browse/SPARK-28103 Thanks and Regards, William On Wed, 19 Jun 2019 at 1:35 AM, Xiao Li <gatorsm...@gmail.com> wrote: > Hi, William, > > Thanks for reporting it. Could you open a JIRA? > > Cheers, > > Xiao > > William Wong <william1...@gmail.com> 于2019年6月18日周二 上午8:57写道： > >> BTW, I noticed a workaround is creating a custom rule to remove 'empty >> local relation' from a union table. However, I am not 100% sure if it is >> the right approach. >> >> On Tue, Jun 18, 2019 at 11:53 PM William Wong <william1...@gmail.com> >> wrote: >> >>> Dear all, >>> >>> I am not sure if it is something expected or not, and should I report it >>> as a bug. Basically, the constraints of a union table could be turned >>> empty if any subtable is turned into an empty local relation. The side >>> effect is filter cannot be inferred correctly (by >>> InferFiltersFromConstrains) >>> >>> We may reproduce the issue with the following setup: >>> 1) Prepare two tables: >>> * spark.sql("CREATE TABLE IF NOT EXISTS table1(id string, val string) >>> USING PARQUET"); >>> * spark.sql("CREATE TABLE IF NOT EXISTS table2(id string, val string) >>> USING PARQUET"); >>> >>> 2) Create a union view on table1. >>> * spark.sql(""" >>> | CREATE VIEW partitioned_table_1 AS >>> | SELECT * FROM table1 WHERE id = 'a' >>> | UNION ALL >>> | SELECT * FROM table1 WHERE id = 'b' >>> | UNION ALL >>> | SELECT * FROM table1 WHERE id = 'c' >>> | UNION ALL >>> | SELECT * FROM table1 WHERE id NOT IN ('a','b','c') >>> | """.stripMargin) >>> >>> 3) View the optimized plan of this SQL. The filter 't2.id = 'a'' cannot >>> be inferred. We can see that the constraints of the left table are empty. >>> >>> scala> spark.sql("SELECT * FROM partitioned_table_1 t1, table2 t2 WHERE >>> t1.id = t2.id AND t1.id = 'a'").queryExecution.optimizedPlan >>> res39: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan = >>> Join Inner, (id#0 = id#4) >>> :- Union >>> : :- Filter (isnotnull(id#0) && (id#0 = a)) >>> : : +- Relation[id#0,val#1] parquet >>> : :- LocalRelation <empty>, [id#0, val#1] >>> : :- LocalRelation <empty>, [id#0, val#1] >>> : +- Filter ((isnotnull(id#0) && NOT id#0 IN (a,b,c)) && (id#0 = a)) >>> : +- Relation[id#0,val#1] parquet >>> +- Filter isnotnull(id#4) >>> +- Relation[id#4,val#5] parquet >>> >>> scala> spark.sql("SELECT * FROM partitioned_table_1 t1, table2 t2 WHERE >>> t1.id = t2.id AND t1.id = >>> 'a'").queryExecution.optimizedPlan.children(0).constraints >>> res40: org.apache.spark.sql.catalyst.expressions.ExpressionSet = Set() >>> >>> 4) Modified the query to avoid empty local relation. The filter 't2.id >>> in ('a','b','c','d')' is then inferred properly. The constraints of the >>> left table are not empty as well. >>> >>> scala> spark.sql("SELECT * FROM partitioned_table_1 t1, table2 t2 WHERE >>> t1.id = t2.id AND t1.id IN >>> ('a','b','c','d')").queryExecution.optimizedPlan >>> res42: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan = >>> Join Inner, (id#0 = id#4) >>> :- Union >>> : :- Filter ((isnotnull(id#0) && (id#0 = a)) && id#0 IN (a,b,c,d)) >>> : : +- Relation[id#0,val#1] parquet >>> : :- Filter ((isnotnull(id#0) && (id#0 = b)) && id#0 IN (a,b,c,d)) >>> : : +- Relation[id#0,val#1] parquet >>> : :- Filter ((isnotnull(id#0) && (id#0 = c)) && id#0 IN (a,b,c,d)) >>> : : +- Relation[id#0,val#1] parquet >>> : +- Filter ((NOT id#0 IN (a,b,c) && id#0 IN (a,b,c,d)) && >>> isnotnull(id#0)) >>> : +- Relation[id#0,val#1] parquet >>> +- Filter ((id#4 IN (a,b,c,d) && ((isnotnull(id#4) && (((id#4 = a) || >>> (id#4 = b)) || (id#4 = c))) || NOT id#4 IN (a,b,c))) && isnotnull(id#4)) >>> +- Relation[id#4,val#5] parquet >>> >>> scala> spark.sql("SELECT * FROM partitioned_table_1 t1, table2 t2 WHERE >>> t1.id = t2.id AND t1.id IN >>> ('a','b','c','d')").queryExecution.optimizedPlan.children(0).constraints >>> res44: org.apache.spark.sql.catalyst.expressions.ExpressionSet = >>> Set(isnotnull(id#0), id#0 IN (a,b,c,d), ((((id#0 = a) || (id#0 = b)) || >>> (id#0 = c)) || NOT id#0 IN (a,b,c))) >>> >>> >>> Thanks and regards, >>> William >>> >>> >>> On Sat, Jun 15, 2019 at 1:13 AM William Wong <william1...@gmail.com> >>> wrote: >>> >>>> Hi all, >>>> >>>> Appreciate any expert may help on this strange behavior.. >>>> >>>> It is interesting that... I implemented a custom rule to remove empty >>>> LocalRelation children under Union and run the same query. The filter 'id = >>>> 'a' is inferred to the table2 and pushed via the Join. >>>> >>>> scala> spark2.sql("SELECT * FROM partitioned_table_1 t1, table2 t2 >>>> WHERE t1.id = t2.id AND t1.id = 'a'").explain >>>> == Physical Plan == >>>> *(4) BroadcastHashJoin [id#0], [id#4], Inner, BuildRight >>>> :- Union >>>> : :- *(1) Project [id#0, val#1] >>>> : : +- *(1) Filter (isnotnull(id#0) && (id#0 = a)) >>>> : : +- *(1) FileScan parquet default.table1[id#0,val#1] Batched: >>>> true, Format: Parquet, Location: >>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table1], >>>> PartitionFilters: [], PushedFilters: [IsNotNull(id), EqualTo(id,a)], >>>> ReadSchema: struct<id:string,val:string> >>>> : +- *(2) Project [id#0, val#1] >>>> : +- *(2) Filter ((isnotnull(id#0) && NOT id#0 IN (a,b,c)) && (id#0 >>>> = a)) >>>> : +- *(2) FileScan parquet default.table1[id#0,val#1] Batched: >>>> true, Format: Parquet, Location: >>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table1], >>>> PartitionFilters: [], PushedFilters: [IsNotNull(id), Not(In(id, [a,b,c])), >>>> EqualTo(id,a)], ReadSchema: struct<id:string,val:string> >>>> +- BroadcastExchange HashedRelationBroadcastMode(List(input[0, string, >>>> true])) >>>> +- *(3) Project [id#4, val#5] >>>> +- *(3) Filter ((id#4 = a) && isnotnull(id#4)) >>>> +- *(3) FileScan parquet default.table2[id#4,val#5] Batched: >>>> true, Format: Parquet, Location: >>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table2], >>>> PartitionFilters: [], *PushedFilters: [EqualTo(id,a), IsNotNull(id)],* >>>> ReadSchema: struct<id:string,val:string> >>>> >>>> scala> >>>> >>>> Thanks and regards, >>>> William >>>> >>>> >>>> >>>> On Sat, Jun 15, 2019 at 12:13 AM William Wong <william1...@gmail.com> >>>> wrote: >>>> >>>>> Dear all, >>>>> >>>>> I created two tables. >>>>> >>>>> scala> spark.sql("CREATE TABLE IF NOT EXISTS table1(id string, val >>>>> string) USING PARQUET"); >>>>> 19/06/14 23:49:10 WARN ObjectStore: Version information not found in >>>>> metastore. hive.metastore.schema.verification is not enabled so recording >>>>> the schema version 1.2.0 >>>>> 19/06/14 23:49:11 WARN ObjectStore: Failed to get database default, >>>>> returning NoSuchObjectException >>>>> res1: org.apache.spark.sql.DataFrame = [] >>>>> >>>>> scala> spark.sql("CREATE TABLE IF NOT EXISTS table2(id string, val >>>>> string) USING PARQUET"); >>>>> res2: org.apache.spark.sql.DataFrame = [] >>>>> >>>>> >>>>> It is the plan of joining these two column via ID column. It looks >>>>> good to me as the filter 'id ='a'' is pushed to both tables as expected. >>>>> >>>>> scala> spark.sql("SELECT * FROM table2 t1, table2 t2 WHERE t1.id = >>>>> t2.id AND t1.id ='a'").explain >>>>> == Physical Plan == >>>>> *(2) BroadcastHashJoin [id#23], [id#68], Inner, BuildRight >>>>> :- *(2) Project [id#23, val#24] >>>>> : +- *(2) Filter (isnotnull(id#23) && (id#23 = a)) >>>>> : +- *(2) FileScan parquet default.table2[id#23,val#24] Batched: >>>>> true, Format: Parquet, Location: >>>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table2], >>>>> *PartitionFilters: >>>>> [], PushedFilters: [IsNotNull(id), EqualTo(id,a)],* ReadSchema: >>>>> struct<id:string,val:string> >>>>> +- BroadcastExchange HashedRelationBroadcastMode(List(input[0, string, >>>>> true])) >>>>> +- *(1) Project [id#68, val#69] >>>>> +- *(1) Filter ((id#68 = a) && isnotnull(id#68)) >>>>> +- *(1) FileScan parquet default.table2[id#68,val#69] >>>>> Batched: true, Format: Parquet, Location: >>>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table2], >>>>> *PartitionFilters: >>>>> [], PushedFilters: [EqualTo(id,a), IsNotNull(id)],* ReadSchema: >>>>> struct<id:string,val:string> >>>>> >>>>> >>>>> Somehow, we created a view on table1 by union a few partitions like >>>>> this: >>>>> >>>>> scala> spark.sql(""" >>>>> | CREATE VIEW partitioned_table_1 AS >>>>> | SELECT * FROM table1 WHERE id = 'a' >>>>> | UNION ALL >>>>> | SELECT * FROM table1 WHERE id = 'b' >>>>> | UNION ALL >>>>> | SELECT * FROM table1 WHERE id = 'c' >>>>> | UNION ALL >>>>> | SELECT * FROM table1 WHERE id NOT IN ('a','b','c') >>>>> | """.stripMargin) >>>>> res7: org.apache.spark.sql.DataFrame = [] >>>>> >>>>> >>>>> In theory, selecting data via this view 'partitioned_table_1' should >>>>> be the same as via the table 'table1' >>>>> >>>>> This query also can push the filter 'id IN ('a','b','c','d') to table2 >>>>> as expected. >>>>> >>>>> scala> spark.sql("SELECT * FROM partitioned_table_1 t1, table2 t2 >>>>> WHERE t1.id = t2.id AND t1.id IN ('a','b','c','d')").explain >>>>> == Physical Plan == >>>>> *(6) BroadcastHashJoin [id#0], [id#23], Inner, BuildRight >>>>> :- Union >>>>> : :- *(1) Project [id#0, val#1] >>>>> : : +- *(1) Filter ((isnotnull(id#0) && (id#0 = a)) && id#0 IN >>>>> (a,b,c,d)) >>>>> : : +- *(1) FileScan parquet default.table1[id#0,val#1] Batched: >>>>> true, Format: Parquet, Location: >>>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table1], >>>>> PartitionFilters: [], PushedFilters: [IsNotNull(id), EqualTo(id,a), In(id, >>>>> [a,b,c,d])], ReadSchema: struct<id:string,val:string> >>>>> : :- *(2) Project [id#0, val#1] >>>>> : : +- *(2) Filter ((isnotnull(id#0) && (id#0 = b)) && id#0 IN >>>>> (a,b,c,d)) >>>>> : : +- *(2) FileScan parquet default.table1[id#0,val#1] Batched: >>>>> true, Format: Parquet, Location: >>>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table1], >>>>> PartitionFilters: [], PushedFilters: [IsNotNull(id), EqualTo(id,b), In(id, >>>>> [a,b,c,d])], ReadSchema: struct<id:string,val:string> >>>>> : :- *(3) Project [id#0, val#1] >>>>> : : +- *(3) Filter ((isnotnull(id#0) && (id#0 = c)) && id#0 IN >>>>> (a,b,c,d)) >>>>> : : +- *(3) FileScan parquet default.table1[id#0,val#1] Batched: >>>>> true, Format: Parquet, Location: >>>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table1], >>>>> PartitionFilters: [], PushedFilters: [IsNotNull(id), EqualTo(id,c), In(id, >>>>> [a,b,c,d])], ReadSchema: struct<id:string,val:string> >>>>> : +- *(4) Project [id#0, val#1] >>>>> : +- *(4) Filter ((NOT id#0 IN (a,b,c) && id#0 IN (a,b,c,d)) && >>>>> isnotnull(id#0)) >>>>> : +- *(4) FileScan parquet default.table1[id#0,val#1] Batched: >>>>> true, Format: Parquet, Location: >>>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table1], >>>>> PartitionFilters: [], PushedFilters: [Not(In(id, [a,b,c])), In(id, >>>>> [a,b,c,d]), IsNotNull(id)], ReadSchema: struct<id:string,val:string> >>>>> +- BroadcastExchange HashedRelationBroadcastMode(List(input[0, string, >>>>> true])) >>>>> +- *(5) Project [id#23, val#24] >>>>> +- *(5) Filter ((id#23 IN (a,b,c,d) && ((isnotnull(id#23) && >>>>> (((id#23 = a) || (id#23 = b)) || (id#23 = c))) || NOT id#23 IN (a,b,c))) >>>>> && >>>>> isnotnull(id#23)) >>>>> +- *(5) FileScan parquet default.table2[id#23,val#24] >>>>> Batched: true, Format: Parquet, Location: >>>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table2], >>>>> PartitionFilters: [], *PushedFilters: [In(id, [a,b,c,d]), >>>>> Or(And(IsNotNull(id),Or(Or(EqualTo(id,a),EqualTo(id,b)),EqualTo(id,c))),Not(I..., >>>>> *ReadSchema: struct<id:string,val:string> >>>>> >>>>> scala> >>>>> >>>>> >>>>> However, if we change the filter to 'id ='a', something strange >>>>> happened. The filter 'id = 'a' cannot be pushed via table2... >>>>> >>>>> scala> spark.sql("SELECT * FROM partitioned_table_1 t1, table2 t2 >>>>> WHERE t1.id = t2.id AND t1.id = 'a'").explain >>>>> == Physical Plan == >>>>> *(4) BroadcastHashJoin [id#0], [id#23], Inner, BuildRight >>>>> :- Union >>>>> : :- *(1) Project [id#0, val#1] >>>>> : : +- *(1) Filter (isnotnull(id#0) && (id#0 = a)) >>>>> : : +- *(1) FileScan parquet default.table1[id#0,val#1] Batched: >>>>> true, Format: Parquet, Location: >>>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table1], >>>>> PartitionFilters: [], PushedFilters: [IsNotNull(id), EqualTo(id,a)], >>>>> ReadSchema: struct<id:string,val:string> >>>>> : :- LocalTableScan <empty>, [id#0, val#1] >>>>> : :- LocalTableScan <empty>, [id#0, val#1] >>>>> : +- *(2) Project [id#0, val#1] >>>>> : +- *(2) Filter ((isnotnull(id#0) && NOT id#0 IN (a,b,c)) && >>>>> (id#0 = a)) >>>>> : +- *(2) FileScan parquet default.table1[id#0,val#1] Batched: >>>>> true, Format: Parquet, Location: >>>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table1], >>>>> PartitionFilters: [], PushedFilters: [IsNotNull(id), Not(In(id, [a,b,c])), >>>>> EqualTo(id,a)], ReadSchema: struct<id:string,val:string> >>>>> +- BroadcastExchange HashedRelationBroadcastMode(List(input[0, string, >>>>> true])) >>>>> +- *(3) Project [id#23, val#24] >>>>> +- *(3) Filter isnotnull(id#23) >>>>> +- *(3) FileScan parquet default.table2[id#23,val#24] >>>>> Batched: true, Format: Parquet, Location: >>>>> InMemoryFileIndex[file:/Users/williamwong/spark-warehouse/table2], >>>>> PartitionFilters: [], PushedFilters: [IsNotNull(id)], ReadSchema: >>>>> struct<id:string,val:string> >>>>> >>>>> >>>>> Appreciate if anyone has an idea on it. Many thanks. >>>>> >>>>> Best regards, >>>>> William >>>>> >>>>
