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From Takeshi Yamamuro <linguin....@gmail.com>
Subject Re: Strange behavior with 'not' and filter pushdown
Date Tue, 14 Feb 2017 03:33:49 GMT
Oh, Thanks for checking!

On Tue, Feb 14, 2017 at 12:32 PM, Xiao Li <gatorsmile@gmail.com> wrote:

> https://github.com/apache/spark/pull/16894
>
> Already backported to Spark 2.0
>
> Thanks!
>
> Xiao
>
> 2017-02-13 17:41 GMT-08:00 Takeshi Yamamuro <linguin.m.s@gmail.com>:
>
>> cc: xiao
>>
>> IIUC a xiao's commit below fixed this issue in master.
>> https://github.com/apache/spark/commit/2eb093decb5e87a1ea71b
>> baa28092876a8c84996
>>
>> Is this fix worth backporting to the v2.0 branch?
>> I checked I could reproduce there:
>>
>> ---
>>
>> scala> Seq((1, "a"), (2, "b"), (3, null)).toDF("c0",
>> "c1").write.mode("overwrite").parquet("/Users/maropu/Desktop/data")
>> scala> spark.read.parquet("/Users/maropu/Desktop/data").createOrRep
>> laceTempView("t")
>> scala> val df = sql("SELECT c0 FROM t WHERE NOT(c1 IS NOT NULL)")
>> scala> df.explain(true)
>> == Parsed Logical Plan ==
>> 'Project ['c0]
>> +- 'Filter NOT isnotnull('c1)
>>    +- 'UnresolvedRelation `t`
>>
>> == Analyzed Logical Plan ==
>> c0: int
>> Project [c0#16]
>> +- Filter NOT isnotnull(c1#17)
>>    +- SubqueryAlias t
>>       +- Relation[c0#16,c1#17] parquet
>>
>> == Optimized Logical Plan ==
>> Project [c0#16]
>> +- Filter (isnotnull(c1#17) && NOT isnotnull(c1#17))
>>            ^^^^^^^^^^^^^^^^
>>    +- Relation[c0#16,c1#17] parquet
>>
>> == Physical Plan ==
>> *Project [c0#16]
>> +- *Filter (isnotnull(c1#17) && NOT isnotnull(c1#17))
>>    +- *BatchedScan parquet [c0#16,c1#17] Format: ParquetFormat,
>> InputPaths: file:/Users/maropu/Desktop/data, PartitionFilters: [],
>> PushedFilters: [IsNotNull(c1), Not(IsNotNull(c1))], ReadSchema:
>> struct<c0:int,c1:string>
>>
>> scala> df.show
>> +---+
>> | c0|
>> +---+
>> +---+
>>
>>
>>
>>
>> // maropu
>>
>>
>> On Sun, Feb 12, 2017 at 10:01 AM, Everett Anderson <
>> everett@nuna.com.invalid> wrote:
>>
>>> On the plus side, looks like this may be fixed in 2.1.0:
>>>
>>> == Physical Plan ==
>>> *HashAggregate(keys=[], functions=[count(1)])
>>> +- Exchange SinglePartition
>>>    +- *HashAggregate(keys=[], functions=[partial_count(1)])
>>>       +- *Project
>>>          +- *Filter NOT isnotnull(username#14)
>>>             +- *FileScan parquet [username#14] Batched: true, Format:
>>> Parquet, Location: InMemoryFileIndex[file:/tmp/test_table],
>>> PartitionFilters: [], PushedFilters: [Not(IsNotNull(username))],
>>> ReadSchema: struct<username:string>
>>>
>>>
>>>
>>> On Fri, Feb 10, 2017 at 11:26 AM, Everett Anderson <everett@nuna.com>
>>> wrote:
>>>
>>>> Bumping this thread.
>>>>
>>>> Translating "where not(username is not null)" into a filter of  [IsNotNull(username),
>>>> Not(IsNotNull(username))] seems like a rather severe bug.
>>>>
>>>> Spark 1.6.2:
>>>>
>>>> explain select count(*) from parquet_table where not( username is not
>>>> null)
>>>>
>>>> == Physical Plan ==
>>>> TungstenAggregate(key=[], functions=[(count(1),mode=Final,isDistinct=false)],
>>>> output=[_c0#1822L])
>>>> +- TungstenExchange SinglePartition, None
>>>>  +- TungstenAggregate(key=[], functions=[(count(1),mode=Partial,isDistinct=false)],
>>>> output=[count#1825L])
>>>>  +- Project
>>>>  +- Filter NOT isnotnull(username#1590)
>>>>  +- Scan ParquetRelation[username#1590] InputPaths: <path to parquet>,
>>>> PushedFilters: [Not(IsNotNull(username))]
>>>>
>>>> Spark 2.0.2
>>>>
>>>> explain select count(*) from parquet_table where not( username is not
>>>> null)
>>>>
>>>> == Physical Plan ==
>>>> *HashAggregate(keys=[], functions=[count(1)])
>>>> +- Exchange SinglePartition
>>>>  +- *HashAggregate(keys=[], functions=[partial_count(1)])
>>>>  +- *Project
>>>>  +- *Filter (isnotnull(username#35) && NOT isnotnull(username#35))
>>>>  +- *BatchedScan parquet default.<hive table name>[username#35] Format:
>>>> ParquetFormat, InputPaths: <path to parquet>, PartitionFilters: [],
>>>> PushedFilters: [IsNotNull(username), Not(IsNotNull(username))],
>>>> ReadSchema: struct<username:string>
>>>>
>>>> Example to generate the above:
>>>>
>>>> // Create some fake data
>>>>
>>>> import org.apache.spark.sql.Row
>>>> import org.apache.spark.sql.Dataset
>>>> import org.apache.spark.sql.types._
>>>>
>>>> val rowsRDD = sc.parallelize(Seq(
>>>>     Row(1, "fred"),
>>>>     Row(2, "amy"),
>>>>     Row(3, null)))
>>>>
>>>> val schema = StructType(Seq(
>>>>     StructField("id", IntegerType, nullable = true),
>>>>     StructField("username", StringType, nullable = true)))
>>>>
>>>> val data = sqlContext.createDataFrame(rowsRDD, schema)
>>>>
>>>> val path = "SOME PATH HERE"
>>>>
>>>> data.write.mode("overwrite").parquet(path)
>>>>
>>>> val testData = sqlContext.read.parquet(path)
>>>>
>>>> testData.registerTempTable("filter_test_table")
>>>>
>>>>
>>>> %sql
>>>> explain select count(*) from filter_test_table where not( username is
>>>> not null)
>>>>
>>>>
>>>> On Wed, Feb 8, 2017 at 4:56 PM, Alexi Kostibas <
>>>> akostibas@nuna.com.invalid> wrote:
>>>>
>>>>> Hi,
>>>>>
>>>>> I have an application where I’m filtering data with SparkSQL with
>>>>> simple WHERE clauses. I also want the ability to show the unmatched rows
>>>>> for any filter, and so am wrapping the previous clause in `NOT()` to
get
>>>>> the inverse. Example:
>>>>>
>>>>> Filter:  username is not null
>>>>> Inverse filter:  NOT(username is not null)
>>>>>
>>>>> This worked fine in Spark 1.6. After upgrading to Spark 2.0.2, the
>>>>> inverse filter always returns zero results. It looks like this is a problem
>>>>> with how the filter is getting pushed down to Parquet. Specifically,
the
>>>>> pushdown includes both the “is not null” filter, AND “not(is not
null)”,
>>>>> which would obviously result in zero matches. An example below:
>>>>>
>>>>> pyspark:
>>>>> > x = spark.sql('select my_id from my_table where *username is not
>>>>> null*')
>>>>> > y = spark.sql('select my_id from my_table where not(*username is
>>>>> not null*)')
>>>>>
>>>>> > x.explain()
>>>>> == Physical Plan ==
>>>>> *Project [my_id#6L]
>>>>> +- *Filter isnotnull(username#91)
>>>>>    +- *BatchedScan parquet default.my_table[my_id#6L,username#91]
>>>>>        Format: ParquetFormat, InputPaths: s3://my-path/my.parquet,
>>>>>        PartitionFilters: [], PushedFilters: [IsNotNull(username)],
>>>>>        ReadSchema: struct<my_id:bigint,username:string>
>>>>> [1159]> y.explain()
>>>>> == Physical Plan ==
>>>>> *Project [my_id#6L]
>>>>> +- *Filter (isnotnull(username#91) && NOT
>>>>> isnotnull(username#91))username
>>>>>    +- *BatchedScan parquet default.my_table[my_id#6L,username#91]
>>>>>        Format: ParquetFormat, InputPaths: s3://my-path/my.parquet,
>>>>>        PartitionFilters: [],
>>>>>        PushedFilters: [IsNotNull(username),
>>>>> Not(IsNotNull(username))],username
>>>>>        ReadSchema: struct<my_id:bigint,username:string>
>>>>>
>>>>> Presently I’m working around this by using the new functionality of
>>>>> NOT EXISTS in Spark 2, but that seems like overkill.
>>>>>
>>>>> Any help appreciated.
>>>>>
>>>>>
>>>>> *Alexi Kostibas*Engineering
>>>>> *Nuna*
>>>>> 650 Townsend Street, Suite 425
>>>>> San Francisco, CA 94103
>>>>>
>>>>>
>>>>
>>>
>>
>>
>> --
>> ---
>> Takeshi Yamamuro
>>
>
>


-- 
---
Takeshi Yamamuro

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