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https://issues.apache.org/jira/browse/SPARK-29721?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Kai Kang updated SPARK-29721:
-----------------------------
Description:
This is a follow up for SPARK-4502. SPARK-4502 correctly addressed column
pruning for nested structures. However, when explode() is called on a nested
field, all columns for that nested structure is still fetched from data source.
We are working on a project to create a parquet store for a big pre-joined
table between two tables that has one-to-many relationship, and this is a
blocking issue for us.
The following code illustrates the issue.
Part 1: loading some nested data
val jsonStr = """{
"items": [
{
"itemId": 1,
"itemData": "a"
},
{
"itemId": 1,
"itemData": "b"
}
]
}"""
val df = spark.read.json(Seq(jsonStr).toDS)
df.write.format("parquet").mode("overwrite").saveAsTable("persisted")
Part 2: reading it back and explaining the queries
{{val read = spark.table("persisted")}}
\{{ spark.conf.set("spark.sql.optimizer.nestedSchemaPruning.enabled", true)}}
\{{ read.select($"items.itemId").explain(true) // pruned, only loading
itemIdread.select(explode($"items.itemId")).explain(true) // not pruned,
loading both itemId and itemData}}{{ }}
was:
This is a follow up for SPARK-4502. SPARK-4502 correctly addressed column
pruning for nested structures. However, when explode() is called on a nested
field, all columns for that nested structure is still fetched from data source.
We are working on a project to create a parquet store for a big pre-joined
table between two tables that has one-to-many relationship, and this is a
blocking issue for us.
The following code illustrates the issue.
Part 1: loading some nested data
val jsonStr = """{
"items": [
{
"itemId": 1,
"itemData": "a"
},
{
"itemId": 1,
"itemData": "b"
}
]
}"""
val df = spark.read.json(Seq(jsonStr).toDS)
df.write.format("parquet").mode("overwrite").saveAsTable("persisted")
Part 2: reading it back and explaining the queries
{{val read = spark.table("persisted")}}
\{{ spark.conf.set("spark.sql.optimizer.nestedSchemaPruning.enabled", true)}}
\{{ read.select($"items.itemId").explain(true) // pruned, only loading
itemIdread.select(explode($"items.itemId")).explain(true) // not pruned,
loading both itemId and itemData}}{{ }}
> Spark SQL reads unnecessary nested fields from Parquet after using explode
> --------------------------------------------------------------------------
>
> Key: SPARK-29721
> URL: https://issues.apache.org/jira/browse/SPARK-29721
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.4.4
> Reporter: Kai Kang
> Priority: Critical
>
> This is a follow up for SPARK-4502. SPARK-4502 correctly addressed column
> pruning for nested structures. However, when explode() is called on a nested
> field, all columns for that nested structure is still fetched from data
> source.
> We are working on a project to create a parquet store for a big pre-joined
> table between two tables that has one-to-many relationship, and this is a
> blocking issue for us.
>
> The following code illustrates the issue.
> Part 1: loading some nested data
> val jsonStr = """{
> "items": [
> {
> "itemId": 1,
> "itemData": "a"
> },
> {
> "itemId": 1,
> "itemData": "b"
> }
> ]
> }"""
> val df = spark.read.json(Seq(jsonStr).toDS)
> df.write.format("parquet").mode("overwrite").saveAsTable("persisted")
>
> Part 2: reading it back and explaining the queries
> {{val read = spark.table("persisted")}}
> \{{ spark.conf.set("spark.sql.optimizer.nestedSchemaPruning.enabled", true)}}
> \{{ read.select($"items.itemId").explain(true) // pruned, only loading
> itemIdread.select(explode($"items.itemId")).explain(true) // not pruned,
> loading both itemId and itemData}}{{ }}
>
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