[
https://issues.apache.org/jira/browse/SPARK-47230?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ling Qin updated SPARK-47230:
-----------------------------
Description:
See the code snippet below, when explode an array of struct and select one
field in the struct, some unexpected behaviour observed:
* If the field in the struct is in the select clause, not in the where clause,
the column pruning works as expected.
* If the field in the struct is in the select clause and in the where clause,
the column pruning not working.
* If the field in the struct is not even selected, the column pruning not
working
{code:java}
from pyspark.sql import SparkSession
from pyspark.sql.types import IntegerType, StructField, StructType, ArrayType
import random
spark = SparkSession.builder.appName("example").getOrCreate()# Create an RDD
with an array of structs, each array having a random size between 5 to 10
rdd = spark.range(1000).rdd.map(lambda x: (x.id + 3, [(x.id + i, x.id - i) for
i in range(1, random.randint(5, 11))]))
# Define a new schema
schema = StructType([
StructField("a", IntegerType(), True),
StructField("b", ArrayType(StructType([
StructField("x", IntegerType(), True),
StructField("y", IntegerType(), True)
])), True)
])
# Create a DataFrame with the new schema
df = spark.createDataFrame(rdd, schema=schema)
# Write the DataFrame to a parquet file
df.repartition(1).write.mode('overwrite').parquet('test.parquet')
# Read the parquet file back into a DataFrame
df = spark.read.parquet('test.parquet')
spark.conf.set('spark.sql.optimizer.nestedSchemaPruning.enabled', 'true')
# case 1, as expected
sql_query = """
SELECT a, EXPLODE(b.x) AS bb
FROM df_view
"""
spark.sql(sql_query).explain()
# ReadSchema: struct<a:int,b:array<struct<x:int>>>
# case 2, as expected
sql_query = """
SELECT a, bb.x
FROM df_view
lateral view explode(b) as bb
"""
spark.sql(sql_query).explain()
# ReadSchema: struct<a:int,b:array<struct<x:int>>>
# case 3, bug: should only read b.x
sql_query = """
SELECT a, bb.x
FROM df_view
lateral view explode(b) as bb
where bb.x is not null
"""
spark.sql(sql_query).explain()
#ReadSchema: struct<a:int,b:array<struct<x:int,y:int>>>
#case 4, bug? seems no need to read both a and b
sql_query = """
SELECT a
FROM df_view
lateral view explode(b) as bb
"""
spark.sql(sql_query).explain()
#ReadSchema: struct<a:int,b:array<struct<x:int,y:int>>>{code}
was:
{code:java}
from pyspark.sql import SparkSession
from pyspark.sql.types import IntegerType, StructField, StructType, ArrayType
import random
spark = SparkSession.builder.appName("example").getOrCreate()# Create an RDD
with an array of structs, each array having a random size between 5 to 10
rdd = spark.range(1000).rdd.map(lambda x: (x.id + 3, [(x.id + i, x.id - i) for
i in range(1, random.randint(5, 11))]))
# Define a new schema
schema = StructType([
StructField("a", IntegerType(), True),
StructField("b", ArrayType(StructType([
StructField("x", IntegerType(), True),
StructField("y", IntegerType(), True)
])), True)
])
# Create a DataFrame with the new schema
df = spark.createDataFrame(rdd, schema=schema)
# Write the DataFrame to a parquet file
df.repartition(1).write.mode('overwrite').parquet('test.parquet')
# Read the parquet file back into a DataFrame
df = spark.read.parquet('test.parquet')
spark.conf.set('spark.sql.optimizer.nestedSchemaPruning.enabled', 'true')
# case 1, as expected
sql_query = """
SELECT a, EXPLODE(b.x) AS bb
FROM df_view
"""
spark.sql(sql_query).explain()
# ReadSchema: struct<a:int,b:array<struct<x:int>>>
# case 2, as expected
sql_query = """
SELECT a, bb.x
FROM df_view
lateral view explode(b) as bb
"""
spark.sql(sql_query).explain()
# ReadSchema: struct<a:int,b:array<struct<x:int>>>
# case 3, bug: should only read b.x
sql_query = """
SELECT a, bb.x
FROM df_view
lateral view explode(b) as bb
where bb.x is not null
"""
spark.sql(sql_query).explain()
#ReadSchema: struct<a:int,b:array<struct<x:int,y:int>>>
#case 4, bug? seems no need to read both a and b
sql_query = """
SELECT a
FROM df_view
lateral view explode(b) as bb
"""
spark.sql(sql_query).explain()
#ReadSchema: struct<a:int,b:array<struct<x:int,y:int>>>{code}
> The column pruning is not working as expected for the nested struct in an
> array
> -------------------------------------------------------------------------------
>
> Key: SPARK-47230
> URL: https://issues.apache.org/jira/browse/SPARK-47230
> Project: Spark
> Issue Type: Bug
> Components: Optimizer, Spark Core, Spark Shell
> Affects Versions: 3.3.0, 3.4.0, 3.5.0, 3.5.1
> Reporter: Ling Qin
> Priority: Major
>
> See the code snippet below, when explode an array of struct and select one
> field in the struct, some unexpected behaviour observed:
> * If the field in the struct is in the select clause, not in the where
> clause, the column pruning works as expected.
> * If the field in the struct is in the select clause and in the where
> clause, the column pruning not working.
> * If the field in the struct is not even selected, the column pruning not
> working
> {code:java}
> from pyspark.sql import SparkSession
> from pyspark.sql.types import IntegerType, StructField, StructType, ArrayType
> import random
> spark = SparkSession.builder.appName("example").getOrCreate()# Create an RDD
> with an array of structs, each array having a random size between 5 to 10
> rdd = spark.range(1000).rdd.map(lambda x: (x.id + 3, [(x.id + i, x.id - i)
> for i in range(1, random.randint(5, 11))]))
> # Define a new schema
> schema = StructType([
> StructField("a", IntegerType(), True),
> StructField("b", ArrayType(StructType([
> StructField("x", IntegerType(), True),
> StructField("y", IntegerType(), True)
> ])), True)
> ])
> # Create a DataFrame with the new schema
> df = spark.createDataFrame(rdd, schema=schema)
> # Write the DataFrame to a parquet file
> df.repartition(1).write.mode('overwrite').parquet('test.parquet')
> # Read the parquet file back into a DataFrame
> df = spark.read.parquet('test.parquet')
> spark.conf.set('spark.sql.optimizer.nestedSchemaPruning.enabled', 'true')
> # case 1, as expected
> sql_query = """
> SELECT a, EXPLODE(b.x) AS bb
> FROM df_view
> """
> spark.sql(sql_query).explain()
> # ReadSchema: struct<a:int,b:array<struct<x:int>>>
> # case 2, as expected
> sql_query = """
> SELECT a, bb.x
> FROM df_view
> lateral view explode(b) as bb
> """
> spark.sql(sql_query).explain()
> # ReadSchema: struct<a:int,b:array<struct<x:int>>>
> # case 3, bug: should only read b.x
> sql_query = """
> SELECT a, bb.x
> FROM df_view
> lateral view explode(b) as bb
> where bb.x is not null
> """
> spark.sql(sql_query).explain()
> #ReadSchema: struct<a:int,b:array<struct<x:int,y:int>>>
> #case 4, bug? seems no need to read both a and b
> sql_query = """
> SELECT a
> FROM df_view
> lateral view explode(b) as bb
> """
> spark.sql(sql_query).explain()
> #ReadSchema: struct<a:int,b:array<struct<x:int,y:int>>>{code}
>
>
>
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