Went ahead and opened
https://issues.apache.org/jira/browse/SPARK-19586
though I'd generally expect to just close it as fixed in 2.1.0 and roll on.
On Sat, Feb 11, 2017 at 5:01 PM, Everett Anderson <ever...@nuna.com> 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 <ever...@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 <
>> akosti...@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))usernam
>>> e
>>> +- *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
>>>
>>>
>>
>
