[jira] [Updated] (SPARK-37321) Wrong size estimation leads to "Cannot broadcast the table that is larger than 8GB: 8 GB"
[
https://issues.apache.org/jira/browse/SPARK-37321?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Izek Greenfield updated SPARK-37321:
Description:
When CBO is enabled then a situation occurs where spark tries to broadcast very
large DataFrame due to wrong output size estimation.
In `EstimationUtils.getSizePerRow`, if there is no statistics then spark will
use `DataType.defaultSize`.
In the case where the output contains `functions.concat_ws`, the
`getSizePerRow` function will estimate the size to be 20 bytes, while in our
case the actual size can be a lot larger.
As a result, we in some cases end up with an estimated size of < 300K while the
actual size can be > 8GB, thus leading to exceptions as spark thinks the tables
may be broadcast but later realizes the data size is too large.
Code sample to reproduce:
for running that I used `-Xmx45G`
{code:scala}
import spark.implicits._
(1 to 10).toDF("index").withColumn("index",
col("index").cast("string")).write.parquet("/tmp/a")
(1 to 1000).toDF("index_b").withColumn("index_b",
col("index_b").cast("string")).write.parquet("/tmp/b")
val a = spark.read
.parquet("/tmp/a")
.withColumn("b", col("index"))
.withColumn("l1", functions.concat_ws("/", col("index"),
functions.current_date(), functions.current_date(), functions.current_date(),
functions.current_date()))
.withColumn("l2", functions.concat_ws("/", col("index"),
functions.current_date(), functions.current_date(), functions.current_date(),
functions.current_date()))
.withColumn("l3", functions.concat_ws("/", col("index"),
functions.current_date(), functions.current_date(), functions.current_date(),
functions.current_date()))
.withColumn("l4", functions.concat_ws("/", col("index"),
functions.current_date(), functions.current_date(), functions.current_date(),
functions.current_date()))
.withColumn("l5", functions.concat_ws("/", col("index"),
functions.current_date(), functions.current_date(), functions.current_date(),
functions.current_date()))
val r = Random.alphanumeric
val l = 220
val i = 2800
val b = spark.read
.parquet("/tmp/b")
.withColumn("l1", functions.concat_ws("/", (0 to i).flatMap(a =>
List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
.withColumn("l2", functions.concat_ws("/", (0 to i).flatMap(a =>
List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
.withColumn("l3", functions.concat_ws("/", (0 to i).flatMap(a =>
List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
.withColumn("l4", functions.concat_ws("/", (0 to i).flatMap(a =>
List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
.withColumn("l5", functions.concat_ws("/", (0 to i).flatMap(a =>
List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
.withColumn("l6", functions.concat_ws("/", (0 to i).flatMap(a =>
List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
.withColumn("l7", functions.concat_ws("/", (0 to i).flatMap(a =>
List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
a.join(b, col("index") === col("index_b")).show(2000)
{code}
was:
When CBO is enabled then a situation occurs where spark tries to broadcast very
large DataFrame due to wrong output size estimation.
In `EstimationUtils.getSizePerRow`, if there is no statistics then spark will
use `DataType.defaultSize`.
In the case where the output contains `functions.concat_ws`, the
`getSizePerRow` function will estimate the size to be 20 bytes, while in our
case the actual size can be a lot larger.
As a result, we in some cases end up with an estimated size of < 300K while the
actual size can be > 8GB, thus leading to exceptions as spark thinks the tables
may be broadcast but later realizes the data size is too large.
Code sample to reproduce:
{code:scala}
import spark.implicits._
(1 to 10).toDF("index").withColumn("index",
col("index").cast("string")).write.parquet("/tmp/a")
(1 to 1000).toDF("index_b").withColumn("index_b",
col("index_b").cast("string")).write.parquet("/tmp/b")
val a = spark.read
.parquet("/tmp/a")
.withColumn("b", col("index"))
.withColumn("l1", functions.concat_ws("/", col("index"),
functions.current_date(), functions.current_date(), functions.current_date(),
functions.current_date()))
.withColumn("l2", functions.concat_ws("/", col("index"),
functions.current_date(), functions.current_date(), functions.current_date(),
functions.current_date()))
.withColumn("l3", functions.concat_ws("/", col("index"),
functions.current_date(), functions.current_date(), functions.current_date(),
functions.current_date()))
.withColumn("l4", functions.concat_ws("/", col("index"),
functions.current_date(), functions.current_date(), functions.current_date(),
functions.current_date()))
[jira] [Updated] (SPARK-37321) Wrong size estimation leads to "Cannot broadcast the table that is larger than 8GB: 8 GB"
[
https://issues.apache.org/jira/browse/SPARK-37321?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Izek Greenfield updated SPARK-37321:
Component/s: SQL
> Wrong size estimation leads to "Cannot broadcast the table that is larger
> than 8GB: 8 GB"
> -
>
> Key: SPARK-37321
> URL: https://issues.apache.org/jira/browse/SPARK-37321
> Project: Spark
> Issue Type: Bug
> Components: Optimizer, SQL
>Affects Versions: 3.1.1, 3.2.0
>Reporter: Izek Greenfield
>Priority: Major
>
> When CBO is enabled then a situation occurs where spark tries to broadcast
> very large DataFrame due to wrong output size estimation.
>
> In `EstimationUtils.getSizePerRow`, if there is no statistics then spark will
> use `DataType.defaultSize`.
> In the case where the output contains `functions.concat_ws`, the
> `getSizePerRow` function will estimate the size to be 20 bytes, while in our
> case the actual size can be a lot larger.
> As a result, we in some cases end up with an estimated size of < 300K while
> the actual size can be > 8GB, thus leading to exceptions as spark thinks the
> tables may be broadcast but later realizes the data size is too large.
>
> Code sample to reproduce:
> {code:scala}
> import spark.implicits._
> (1 to 10).toDF("index").withColumn("index",
> col("index").cast("string")).write.parquet("/tmp/a")
> (1 to 1000).toDF("index_b").withColumn("index_b",
> col("index_b").cast("string")).write.parquet("/tmp/b")
> val a = spark.read
> .parquet("/tmp/a")
> .withColumn("b", col("index"))
> .withColumn("l1", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l2", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l3", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l4", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l5", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> val r = Random.alphanumeric
> val l = 220
> val i = 2800
> val b = spark.read
> .parquet("/tmp/b")
> .withColumn("l1", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l2", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l3", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l4", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l5", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l6", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l7", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
>
> a.join(b, col("index") === col("index_b")).show(2000)
> {code}
>
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[jira] [Updated] (SPARK-37321) Wrong size estimation leads to "Cannot broadcast the table that is larger than 8GB: 8 GB"
[
https://issues.apache.org/jira/browse/SPARK-37321?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Izek Greenfield updated SPARK-37321:
Summary: Wrong size estimation leads to "Cannot broadcast the table that is
larger than 8GB: 8 GB" (was: Wrong size estimation that leads to "Cannot
broadcast the table that is larger than 8GB: 8 GB")
> Wrong size estimation leads to "Cannot broadcast the table that is larger
> than 8GB: 8 GB"
> -
>
> Key: SPARK-37321
> URL: https://issues.apache.org/jira/browse/SPARK-37321
> Project: Spark
> Issue Type: Bug
> Components: Optimizer
>Affects Versions: 3.1.1, 3.2.0
>Reporter: Izek Greenfield
>Priority: Major
>
> When CBO is enabled then a situation occurs where spark tries to broadcast
> very large DataFrame due to wrong output size estimation.
>
> In `EstimationUtils.getSizePerRow`, if there is no statistics then spark will
> use `DataType.defaultSize`.
> In the case where the output contains `functions.concat_ws`, the
> `getSizePerRow` function will estimate the size to be 20 bytes, while in our
> case the actual size can be a lot larger.
> As a result, we in some cases end up with an estimated size of < 300K while
> the actual size can be > 8GB, thus leading to exceptions as spark thinks the
> tables may be broadcast but later realizes the data size is too large.
>
> Code sample to reproduce:
> {code:scala}
> import spark.implicits._
> (1 to 10).toDF("index").withColumn("index",
> col("index").cast("string")).write.parquet("/tmp/a")
> (1 to 1000).toDF("index_b").withColumn("index_b",
> col("index_b").cast("string")).write.parquet("/tmp/b")
> val a = spark.read
> .parquet("/tmp/a")
> .withColumn("b", col("index"))
> .withColumn("l1", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l2", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l3", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l4", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> .withColumn("l5", functions.concat_ws("/", col("index"),
> functions.current_date(), functions.current_date(), functions.current_date(),
> functions.current_date()))
> val r = Random.alphanumeric
> val l = 220
> val i = 2800
> val b = spark.read
> .parquet("/tmp/b")
> .withColumn("l1", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l2", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l3", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l4", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l5", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l6", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
> .withColumn("l7", functions.concat_ws("/", (0 to i).flatMap(a =>
> List(col("index_b"), lit(r.take(l).mkString), lit(r.take(l).mkString))): _*))
>
> a.join(b, col("index") === col("index_b")).show(2000)
> {code}
>
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