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
Sent from the Apache Spark User List mailing list archive at Nabble.com.
---------------------------------------------------------------------
To unsubscribe e-mail: user-unsubscr...@spark.apache.org
