Hi,
Lexicographically speaking, Min/Max should work because String(s) support a
comparator operator. So anything which supports an equality test (<,>, <= , >=
, == …) can also support min and max functions as well.
I guess the question is if Spark does support this, and if not, why?
Yes, it makes sense.
> On Jan 17, 2017, at 9:17 AM, Jörn Franke <jornfra...@gmail.com> wrote:
>
> Hallo,
>
> I am not sure what you mean by min/max for strings. I do not know if this
> makes sense. What the ORC format has is bloom filters for strings etc. - are
> you referring to this?
>
> In order to apply min/max filters Spark needs to read the meta data of the
> file. If the filter is applied or not - this you can see from the number of
> bytes read.
>
>
> Best regards
>
>> On 17 Jan 2017, at 15:28, djiang <dji...@dataxu.com> wrote:
>>
>> Hi,
>>
>> I have been looking into how Spark stores statistics (min/max) in Parquet as
>> well as how it uses the info for query optimization.
>> I have got a few questions.
>> First setup: Spark 2.1.0, the following sets up a Dataframe of 1000 rows,
>> with a long type and a string type column.
>> They are sorted by different columns, though.
>>
>> scala> spark.sql("select id, cast(id as string) text from
>> range(1000)").sort("id").write.parquet("/secret/spark21-sortById")
>> scala> spark.sql("select id, cast(id as string) text from
>> range(1000)").sort("Text").write.parquet("/secret/spark21-sortByText")
>>
>> I added some code to parquet-tools to print out stats and examine the
>> generated parquet files:
>>
>> hadoop jar parquet-tools-1.9.1-SNAPSHOT.jar meta
>> /secret/spark21-sortById/part-00000-39f7ac12-6038-46ee-b5c3-d7a5a06e4425.snappy.parquet
>>
>> file:
>> file:/secret/spark21-sortById/part-00000-39f7ac12-6038-46ee-b5c3-d7a5a06e4425.snappy.parquet
>>
>> creator: parquet-mr version 1.8.1 (build
>> 4aba4dae7bb0d4edbcf7923ae1339f28fd3f7fcf)
>> extra: org.apache.spark.sql.parquet.row.metadata =
>> {"type":"struct","fields":[{"name":"id","type":"long","nullable":false,"metadata":{}},{"name":"text","type":"string","nullable":false,"metadata":{}}]}
>>
>>
>> file schema: spark_schema
>> --------------------------------------------------------------------------------
>> id: REQUIRED INT64 R:0 D:0
>> text: REQUIRED BINARY O:UTF8 R:0 D:0
>>
>> row group 1: RC:5 TS:133 OFFSET:4
>> --------------------------------------------------------------------------------
>> id: INT64 SNAPPY DO:0 FPO:4 SZ:71/81/1.14 VC:5
>> ENC:PLAIN,BIT_PACKED STA:[min: 0, max: 4, num_nulls: 0]
>> text: BINARY SNAPPY DO:0 FPO:75 SZ:53/52/0.98 VC:5
>> ENC:PLAIN,BIT_PACKED
>>
>> hadoop jar parquet-tools-1.9.1-SNAPSHOT.jar meta
>> /secret/spark21-sortByText/part-00000-3d7eac74-5ca0-44a0-b8a6-d67cc38a2bde.snappy.parquet
>>
>> file:
>> file:/secret/spark21-sortByText/part-00000-3d7eac74-5ca0-44a0-b8a6-d67cc38a2bde.snappy.parquet
>>
>> creator: parquet-mr version 1.8.1 (build
>> 4aba4dae7bb0d4edbcf7923ae1339f28fd3f7fcf)
>> extra: org.apache.spark.sql.parquet.row.metadata =
>> {"type":"struct","fields":[{"name":"id","type":"long","nullable":false,"metadata":{}},{"name":"text","type":"string","nullable":false,"metadata":{}}]}
>>
>>
>> file schema: spark_schema
>> --------------------------------------------------------------------------------
>> id: REQUIRED INT64 R:0 D:0
>> text: REQUIRED BINARY O:UTF8 R:0 D:0
>>
>> row group 1: RC:5 TS:140 OFFSET:4
>> --------------------------------------------------------------------------------
>> id: INT64 SNAPPY DO:0 FPO:4 SZ:71/81/1.14 VC:5
>> ENC:PLAIN,BIT_PACKED STA:[min: 0, max: 101, num_nulls: 0]
>> text: BINARY SNAPPY DO:0 FPO:75 SZ:60/59/0.98 VC:5
>> ENC:PLAIN,BIT_PACKED
>>
>> So the question is why is Spark, particularly, 2.1.0, only generate min/max
>> for numeric columns, but not strings(BINARY) fields, even if the string
>> field is included in the sort? Maybe I missed a configuraiton?
>>
>> The second issue, is how can I confirm Spark is utilizing the min/max?
>> scala> sc.setLogLevel("INFO")
>> scala> spark.sql("select * from parquet.`/secret/spark21-sortById` where
>> id=4").show
>> I got many lines like this:
>> 17/01/17 09:23:35 INFO FilterCompat: Filtering using predicate:
>> and(noteq(id, null), eq(id, 4))
>> 17/01/17 09:23:35 INFO FileScanRDD: Reading File path:
>> file:///secret/spark21-sortById/part-00000-39f7ac12-6038-46ee-b5c3-d7a5a06e4425.snappy.parquet,
>> range: 0-558, partition values: [empty row]
>> ...
>> 17/01/17 09:23:35 INFO FilterCompat: Filtering using predicate:
>> and(noteq(id, null), eq(id, 4))
>> 17/01/17 09:23:35 INFO FileScanRDD: Reading File path:
>> file:///secret/spark21-sortById/part-00193-39f7ac12-6038-46ee-b5c3-d7a5a06e4425.snappy.parquet,
>> range: 0-574, partition values: [empty row]
>> ...
>>
>> The question is it looks like Spark is scanning every file, even if from the
>> min/max, Spark should be able to determine only part-00000 has the relevant
>> data. Or maybe I read it wrong, that Spark is skipping the files? Maybe
>> Spark can only use partition value for data skipping?
>>
>> Thanks,
>>
>> Dong
>>
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Parquet-Statistics-question-tp28312.html
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>>
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