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https://issues.apache.org/jira/browse/SPARK-29721?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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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
{noformat}
val jsonStr = """{
"items": [
{"itemId": 1, "itemData": "a"},
{"itemId": 2, "itemData": "b"}
]
}"""
val df = spark.read.json(Seq(jsonStr).toDS)
df.write.format("parquet").mode("overwrite").saveAsTable("persisted")
{noformat}
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($"items.itemId").explain(true) // pruned, only loading itemId
read.select(explode($"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
{quote}
val jsonStr = """{
"items": [
{"itemId": 1, "itemData": "a"},
{"itemId": 2, "itemData": "b"}
]
}"""
val df = spark.read.json(Seq(jsonStr).toDS)
df.write.format("parquet").mode("overwrite").saveAsTable("persisted")
{quote}
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($"items.itemId").explain(true) // pruned, only loading itemId
read.select(explode($"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
> {noformat}
> val jsonStr = """{
> "items": [
> {"itemId": 1, "itemData": "a"},
> {"itemId": 2, "itemData": "b"}
> ]
> }"""
> val df = spark.read.json(Seq(jsonStr).toDS)
> df.write.format("parquet").mode("overwrite").saveAsTable("persisted")
> {noformat}
>
> 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($"items.itemId").explain(true) // pruned, only loading itemId
> read.select(explode($"items.itemId")).explain(true) // not pruned, loading
> both itemId and itemData
>
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