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From Xiao Li <gatorsm...@gmail.com>
Subject Re: Filter cannot be pushed via a Join
Date Tue, 18 Jun 2019 17:35:32 GMT
Hi, William,

Thanks for reporting it. Could you open a JIRA?

Cheers,

Xiao

William Wong <william1104@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 <william1104@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 <william1104@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 <william1104@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
>>>>
>>>

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