[SPARK-45071][SQL] Optimize the processing speed of BinaryArithmetic#dataType when processing multi-column data
#42804
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wangyum
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Sep 5, 2023
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
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cloud-fan
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
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Please merge it to master , thanks |
wangyum
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Sep 6, 2023
…#dataType` when processing multi-column data
### What changes were proposed in this pull request?
Since `BinaryArithmetic#dataType` will recursively process the datatype of each node, the driver will be very slow when multiple columns are processed.
For example, the following code:
```scala
import spark.implicits._
import scala.util.Random
import org.apache.spark.sql.Row
import org.apache.spark.sql.functions.sum
import org.apache.spark.sql.types.{StructType, StructField, IntegerType}
val N = 30
val M = 100
val columns = Seq.fill(N)(Random.alphanumeric.take(8).mkString)
val data = Seq.fill(M)(Seq.fill(N)(Random.nextInt(16) - 5))
val schema = StructType(columns.map(StructField(_, IntegerType)))
val rdd = spark.sparkContext.parallelize(data.map(Row.fromSeq(_)))
val df = spark.createDataFrame(rdd, schema)
val colExprs = columns.map(sum(_))
// gen a new column , and add the other 30 column
df.withColumn("new_col_sum", expr(columns.mkString(" + ")))
```
This code will take a few minutes for the driver to execute in the spark3.4 version, but only takes a few seconds to execute in the spark3.2 version. Related issue: [SPARK-39316](#36698)
### Why are the changes needed?
Optimize the processing speed of `BinaryArithmetic#dataType` when processing multi-column data
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
manual testing
### Was this patch authored or co-authored using generative AI tooling?
no
Closes #42804 from zzzzming95/SPARK-45071.
Authored-by: zzzzming95 <505306252@qq.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
(cherry picked from commit 16e813c)
Signed-off-by: Yuming Wang <yumwang@ebay.com>
wangyum
pushed a commit
that referenced
this pull request
Sep 6, 2023
…#dataType` when processing multi-column data
### What changes were proposed in this pull request?
Since `BinaryArithmetic#dataType` will recursively process the datatype of each node, the driver will be very slow when multiple columns are processed.
For example, the following code:
```scala
import spark.implicits._
import scala.util.Random
import org.apache.spark.sql.Row
import org.apache.spark.sql.functions.sum
import org.apache.spark.sql.types.{StructType, StructField, IntegerType}
val N = 30
val M = 100
val columns = Seq.fill(N)(Random.alphanumeric.take(8).mkString)
val data = Seq.fill(M)(Seq.fill(N)(Random.nextInt(16) - 5))
val schema = StructType(columns.map(StructField(_, IntegerType)))
val rdd = spark.sparkContext.parallelize(data.map(Row.fromSeq(_)))
val df = spark.createDataFrame(rdd, schema)
val colExprs = columns.map(sum(_))
// gen a new column , and add the other 30 column
df.withColumn("new_col_sum", expr(columns.mkString(" + ")))
```
This code will take a few minutes for the driver to execute in the spark3.4 version, but only takes a few seconds to execute in the spark3.2 version. Related issue: [SPARK-39316](#36698)
### Why are the changes needed?
Optimize the processing speed of `BinaryArithmetic#dataType` when processing multi-column data
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
manual testing
### Was this patch authored or co-authored using generative AI tooling?
no
Closes #42804 from zzzzming95/SPARK-45071.
Authored-by: zzzzming95 <505306252@qq.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
(cherry picked from commit 16e813c)
Signed-off-by: Yuming Wang <yumwang@ebay.com>
|
Merged to master, branch-3.5 and branch-3.4. |
viirya
pushed a commit
to viirya/spark-1
that referenced
this pull request
Oct 19, 2023
…#dataType` when processing multi-column data
### What changes were proposed in this pull request?
Since `BinaryArithmetic#dataType` will recursively process the datatype of each node, the driver will be very slow when multiple columns are processed.
For example, the following code:
```scala
import spark.implicits._
import scala.util.Random
import org.apache.spark.sql.Row
import org.apache.spark.sql.functions.sum
import org.apache.spark.sql.types.{StructType, StructField, IntegerType}
val N = 30
val M = 100
val columns = Seq.fill(N)(Random.alphanumeric.take(8).mkString)
val data = Seq.fill(M)(Seq.fill(N)(Random.nextInt(16) - 5))
val schema = StructType(columns.map(StructField(_, IntegerType)))
val rdd = spark.sparkContext.parallelize(data.map(Row.fromSeq(_)))
val df = spark.createDataFrame(rdd, schema)
val colExprs = columns.map(sum(_))
// gen a new column , and add the other 30 column
df.withColumn("new_col_sum", expr(columns.mkString(" + ")))
```
This code will take a few minutes for the driver to execute in the spark3.4 version, but only takes a few seconds to execute in the spark3.2 version. Related issue: [SPARK-39316](apache#36698)
### Why are the changes needed?
Optimize the processing speed of `BinaryArithmetic#dataType` when processing multi-column data
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
manual testing
### Was this patch authored or co-authored using generative AI tooling?
no
Closes apache#42804 from zzzzming95/SPARK-45071.
Authored-by: zzzzming95 <505306252@qq.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
(cherry picked from commit 16e813c)
Signed-off-by: Yuming Wang <yumwang@ebay.com>
(cherry picked from commit a96804b)
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
zml1206
pushed a commit
to zml1206/spark
that referenced
this pull request
May 7, 2025
…#dataType` when processing multi-column data
### What changes were proposed in this pull request?
Since `BinaryArithmetic#dataType` will recursively process the datatype of each node, the driver will be very slow when multiple columns are processed.
For example, the following code:
```scala
import spark.implicits._
import scala.util.Random
import org.apache.spark.sql.Row
import org.apache.spark.sql.functions.sum
import org.apache.spark.sql.types.{StructType, StructField, IntegerType}
val N = 30
val M = 100
val columns = Seq.fill(N)(Random.alphanumeric.take(8).mkString)
val data = Seq.fill(M)(Seq.fill(N)(Random.nextInt(16) - 5))
val schema = StructType(columns.map(StructField(_, IntegerType)))
val rdd = spark.sparkContext.parallelize(data.map(Row.fromSeq(_)))
val df = spark.createDataFrame(rdd, schema)
val colExprs = columns.map(sum(_))
// gen a new column , and add the other 30 column
df.withColumn("new_col_sum", expr(columns.mkString(" + ")))
```
This code will take a few minutes for the driver to execute in the spark3.4 version, but only takes a few seconds to execute in the spark3.2 version. Related issue: [SPARK-39316](apache#36698)
### Why are the changes needed?
Optimize the processing speed of `BinaryArithmetic#dataType` when processing multi-column data
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
manual testing
### Was this patch authored or co-authored using generative AI tooling?
no
Closes apache#42804 from zzzzming95/SPARK-45071.
Authored-by: zzzzming95 <505306252@qq.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
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What changes were proposed in this pull request?
Since
BinaryArithmetic#dataTypewill recursively process the datatype of each node, the driver will be very slow when multiple columns are processed.For example, the following code:
This code will take a few minutes for the driver to execute in the spark3.4 version, but only takes a few seconds to execute in the spark3.2 version. Related issue: SPARK-39316
Why are the changes needed?
Optimize the processing speed of
BinaryArithmetic#dataTypewhen processing multi-column dataDoes this PR introduce any user-facing change?
No
How was this patch tested?
manual testing
Was this patch authored or co-authored using generative AI tooling?
no