[jira] [Commented] (SPARK-10301) For struct type, if parquet's global schema has less fields than a file's schema, data reading will fail
[
https://issues.apache.org/jira/browse/SPARK-10301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14736947#comment-14736947
]
Apache Spark commented on SPARK-10301:
--
User 'liancheng' has created a pull request for this issue:
https://github.com/apache/spark/pull/8670
> For struct type, if parquet's global schema has less fields than a file's
> schema, data reading will fail
>
>
> Key: SPARK-10301
> URL: https://issues.apache.org/jira/browse/SPARK-10301
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 1.5.0
>Reporter: Yin Huai
>Assignee: Cheng Lian
>Priority: Critical
> Fix For: 1.6.0, 1.5.1
>
>
> We hit this issue when reading a complex Parquet dateset without turning on
> schema merging. The data set consists of Parquet files with different but
> compatible schemas. In this way, the schema of the dataset is defined by
> either a summary file or a random physical Parquet file if no summary files
> are available. Apparently, this schema may not containing all fields
> appeared in all physicla files.
> Parquet was designed with schema evolution and column pruning in mind, so it
> should be legal for a user to use a tailored schema to read the dataset to
> save disk IO. For example, say we have a Parquet dataset consisting of two
> physical Parquet files with the following two schemas:
> {noformat}
> message m0 {
> optional group f0 {
> optional int64 f00;
> optional int64 f01;
> }
> }
> message m1 {
> optional group f0 {
> optional int64 f01;
> optional int64 f01;
> optional int64 f02;
> }
> optional double f1;
> }
> {noformat}
> Users should be allowed to read the dataset with the following schema:
> {noformat}
> message m1 {
> optional group f0 {
> optional int64 f01;
> optional int64 f02;
> }
> }
> {noformat}
> so that {{f0.f00}} and {{f1}} are never touched. The above case can be
> expressed by the following {{spark-shell}} snippet:
> {noformat}
> import sqlContext._
> import sqlContext.implicits._
> import org.apache.spark.sql.types.{LongType, StructType}
> val path = "/tmp/spark/parquet"
> range(3).selectExpr("NAMED_STRUCT('f00', id, 'f01', id) AS f0").coalesce(1)
> .write.mode("overwrite").parquet(path)
> range(3).selectExpr("NAMED_STRUCT('f00', id, 'f01', id, 'f02', id) AS f0",
> "CAST(id AS DOUBLE) AS f1").coalesce(1)
> .write.mode("append").parquet(path)
> val tailoredSchema =
> new StructType()
> .add(
> "f0",
> new StructType()
> .add("f01", LongType, nullable = true)
> .add("f02", LongType, nullable = true),
> nullable = true)
> read.schema(tailoredSchema).parquet(path).show()
> {noformat}
> Expected output should be:
> {noformat}
> ++
> | f0|
> ++
> |[0,null]|
> |[1,null]|
> |[2,null]|
> | [0,0]|
> | [1,1]|
> | [2,2]|
> ++
> {noformat}
> However, current 1.5-SNAPSHOT version throws the following exception:
> {noformat}
> org.apache.parquet.io.ParquetDecodingException: Can not read value at 0 in
> block -1 in file
> hdfs://localhost:9000/tmp/spark/parquet/part-r-0-56c4604e-c546-4f97-a316-05da8ab1a0bf.gz.parquet
> at
> org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:228)
> at
> org.apache.parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:201)
> at
> org.apache.spark.rdd.SqlNewHadoopRDD$$anon$1.hasNext(SqlNewHadoopRDD.scala:168)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at
> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at
> scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215)
> at
>
[jira] [Commented] (SPARK-10301) For struct type, if parquet's global schema has less fields than a file's schema, data reading will fail
[
https://issues.apache.org/jira/browse/SPARK-10301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14735735#comment-14735735
]
Yin Huai commented on SPARK-10301:
--
[~lian cheng] Let's also have a follow-up pr for the master branch to address
post-hoc review comments.
> For struct type, if parquet's global schema has less fields than a file's
> schema, data reading will fail
>
>
> Key: SPARK-10301
> URL: https://issues.apache.org/jira/browse/SPARK-10301
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 1.5.0
>Reporter: Yin Huai
>Assignee: Cheng Lian
>Priority: Critical
> Labels: backport-needed
> Fix For: 1.6.0
>
>
> We hit this issue when reading a complex Parquet dateset without turning on
> schema merging. The data set consists of Parquet files with different but
> compatible schemas. In this way, the schema of the dataset is defined by
> either a summary file or a random physical Parquet file if no summary files
> are available. Apparently, this schema may not containing all fields
> appeared in all physicla files.
> Parquet was designed with schema evolution and column pruning in mind, so it
> should be legal for a user to use a tailored schema to read the dataset to
> save disk IO. For example, say we have a Parquet dataset consisting of two
> physical Parquet files with the following two schemas:
> {noformat}
> message m0 {
> optional group f0 {
> optional int64 f00;
> optional int64 f01;
> }
> }
> message m1 {
> optional group f0 {
> optional int64 f01;
> optional int64 f01;
> optional int64 f02;
> }
> optional double f1;
> }
> {noformat}
> Users should be allowed to read the dataset with the following schema:
> {noformat}
> message m1 {
> optional group f0 {
> optional int64 f01;
> optional int64 f02;
> }
> }
> {noformat}
> so that {{f0.f00}} and {{f1}} are never touched. The above case can be
> expressed by the following {{spark-shell}} snippet:
> {noformat}
> import sqlContext._
> import sqlContext.implicits._
> import org.apache.spark.sql.types.{LongType, StructType}
> val path = "/tmp/spark/parquet"
> range(3).selectExpr("NAMED_STRUCT('f00', id, 'f01', id) AS f0").coalesce(1)
> .write.mode("overwrite").parquet(path)
> range(3).selectExpr("NAMED_STRUCT('f00', id, 'f01', id, 'f02', id) AS f0",
> "CAST(id AS DOUBLE) AS f1").coalesce(1)
> .write.mode("append").parquet(path)
> val tailoredSchema =
> new StructType()
> .add(
> "f0",
> new StructType()
> .add("f01", LongType, nullable = true)
> .add("f02", LongType, nullable = true),
> nullable = true)
> read.schema(tailoredSchema).parquet(path).show()
> {noformat}
> Expected output should be:
> {noformat}
> ++
> | f0|
> ++
> |[0,null]|
> |[1,null]|
> |[2,null]|
> | [0,0]|
> | [1,1]|
> | [2,2]|
> ++
> {noformat}
> However, current 1.5-SNAPSHOT version throws the following exception:
> {noformat}
> org.apache.parquet.io.ParquetDecodingException: Can not read value at 0 in
> block -1 in file
> hdfs://localhost:9000/tmp/spark/parquet/part-r-0-56c4604e-c546-4f97-a316-05da8ab1a0bf.gz.parquet
> at
> org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:228)
> at
> org.apache.parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:201)
> at
> org.apache.spark.rdd.SqlNewHadoopRDD$$anon$1.hasNext(SqlNewHadoopRDD.scala:168)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at
> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at
> scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215)
> at
>
[jira] [Commented] (SPARK-10301) For struct type, if parquet's global schema has less fields than a file's schema, data reading will fail
[
https://issues.apache.org/jira/browse/SPARK-10301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14728807#comment-14728807
]
Apache Spark commented on SPARK-10301:
--
User 'liancheng' has created a pull request for this issue:
https://github.com/apache/spark/pull/8583
> For struct type, if parquet's global schema has less fields than a file's
> schema, data reading will fail
>
>
> Key: SPARK-10301
> URL: https://issues.apache.org/jira/browse/SPARK-10301
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 1.5.0
>Reporter: Yin Huai
>Assignee: Cheng Lian
>Priority: Critical
> Fix For: 1.6.0
>
>
> We hit this issue when reading a complex Parquet dateset without turning on
> schema merging. The data set consists of Parquet files with different but
> compatible schemas. In this way, the schema of the dataset is defined by
> either a summary file or a random physical Parquet file if no summary files
> are available. Apparently, this schema may not containing all fields
> appeared in all physicla files.
> Parquet was designed with schema evolution and column pruning in mind, so it
> should be legal for a user to use a tailored schema to read the dataset to
> save disk IO. For example, say we have a Parquet dataset consisting of two
> physical Parquet files with the following two schemas:
> {noformat}
> message m0 {
> optional group f0 {
> optional int64 f00;
> optional int64 f01;
> }
> }
> message m1 {
> optional group f0 {
> optional int64 f01;
> optional int64 f01;
> optional int64 f02;
> }
> optional double f1;
> }
> {noformat}
> Users should be allowed to read the dataset with the following schema:
> {noformat}
> message m1 {
> optional group f0 {
> optional int64 f01;
> optional int64 f02;
> }
> }
> {noformat}
> so that {{f0.f00}} and {{f1}} are never touched. The above case can be
> expressed by the following {{spark-shell}} snippet:
> {noformat}
> import sqlContext._
> import sqlContext.implicits._
> import org.apache.spark.sql.types.{LongType, StructType}
> val path = "/tmp/spark/parquet"
> range(3).selectExpr("NAMED_STRUCT('f00', id, 'f01', id) AS f0").coalesce(1)
> .write.mode("overwrite").parquet(path)
> range(3).selectExpr("NAMED_STRUCT('f00', id, 'f01', id, 'f02', id) AS f0",
> "CAST(id AS DOUBLE) AS f1").coalesce(1)
> .write.mode("append").parquet(path)
> val tailoredSchema =
> new StructType()
> .add(
> "f0",
> new StructType()
> .add("f01", LongType, nullable = true)
> .add("f02", LongType, nullable = true),
> nullable = true)
> read.schema(tailoredSchema).parquet(path).show()
> {noformat}
> Expected output should be:
> {noformat}
> ++
> | f0|
> ++
> |[0,null]|
> |[1,null]|
> |[2,null]|
> | [0,0]|
> | [1,1]|
> | [2,2]|
> ++
> {noformat}
> However, current 1.5-SNAPSHOT version throws the following exception:
> {noformat}
> org.apache.parquet.io.ParquetDecodingException: Can not read value at 0 in
> block -1 in file
> hdfs://localhost:9000/tmp/spark/parquet/part-r-0-56c4604e-c546-4f97-a316-05da8ab1a0bf.gz.parquet
> at
> org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:228)
> at
> org.apache.parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:201)
> at
> org.apache.spark.rdd.SqlNewHadoopRDD$$anon$1.hasNext(SqlNewHadoopRDD.scala:168)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at
> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at
> scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215)
> at
>
[jira] [Commented] (SPARK-10301) For struct type, if parquet's global schema has less fields than a file's schema, data reading will fail
[
https://issues.apache.org/jira/browse/SPARK-10301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14721447#comment-14721447
]
Cheng Lian commented on SPARK-10301:
Updated ticket description to provide a more general view of this issue. Would
also be helpful for reviewing https://github.com/apache/spark/pull/8509
For struct type, if parquet's global schema has less fields than a file's
schema, data reading will fail
Key: SPARK-10301
URL: https://issues.apache.org/jira/browse/SPARK-10301
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 1.5.0
Reporter: Yin Huai
Assignee: Cheng Lian
Priority: Critical
We hit this issue when reading a complex Parquet dateset without turning on
schema merging. The data set consists of Parquet files with different but
compatible schemas. In this way, the schema of the dataset is defined by
either a summary file or a random physical Parquet file if no summary files
are available. Apparently, this schema may not containing all fields
appeared in all physicla files.
Parquet was designed with schema evolution and column pruning in mind, so it
should be legal for a user to use a tailored schema to read the dataset to
save disk IO. For example, say we have a Parquet dataset consisting of two
physical Parquet files with the following two schemas:
{noformat}
message m0 {
optional group f0 {
optional int64 f00;
optional int64 f01;
}
}
message m1 {
optional group f0 {
optional int64 f01;
optional int64 f01;
optional int64 f02;
}
optional double f1;
}
{noformat}
Users should be allowed to read the dataset with the following schema:
{noformat}
message m1 {
optional group f0 {
optional int64 f01;
optional int64 f02;
}
}
{noformat}
so that {{f0.f00}} and {{f1}} are never touched. The above case can be
expressed by the following {{spark-shell}} snippet:
{noformat}
import sqlContext._
import sqlContext.implicits._
import org.apache.spark.sql.types.{LongType, StructType}
val path = /tmp/spark/parquet
range(3).selectExpr(NAMED_STRUCT('f00', id, 'f01', id) AS f0).coalesce(1)
.write.mode(overwrite).parquet(path)
range(3).selectExpr(NAMED_STRUCT('f00', id, 'f01', id, 'f02', id) AS f0,
CAST(id AS DOUBLE) AS f1).coalesce(1)
.write.mode(append).parquet(path)
val tailoredSchema =
new StructType()
.add(
f0,
new StructType()
.add(f01, LongType, nullable = true)
.add(f02, LongType, nullable = true),
nullable = true)
read.schema(tailoredSchema).parquet(path).show()
{noformat}
Expected output should be:
{noformat}
++
| f0|
++
|[0,null]|
|[1,null]|
|[2,null]|
| [0,0]|
| [1,1]|
| [2,2]|
++
{noformat}
However, current 1.5-SNAPSHOT version throws the following exception:
{noformat}
org.apache.parquet.io.ParquetDecodingException: Can not read value at 0 in
block -1 in file
hdfs://localhost:9000/tmp/spark/parquet/part-r-0-56c4604e-c546-4f97-a316-05da8ab1a0bf.gz.parquet
at
org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:228)
at
org.apache.parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:201)
at
org.apache.spark.rdd.SqlNewHadoopRDD$$anon$1.hasNext(SqlNewHadoopRDD.scala:168)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at
scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1844)
[jira] [Commented] (SPARK-10301) For struct type, if parquet's global schema has less fields than a file's schema, data reading will fail
[ https://issues.apache.org/jira/browse/SPARK-10301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14721187#comment-14721187 ] Apache Spark commented on SPARK-10301: -- User 'yhuai' has created a pull request for this issue: https://github.com/apache/spark/pull/8515 For struct type, if parquet's global schema has less fields than a file's schema, data reading will fail Key: SPARK-10301 URL: https://issues.apache.org/jira/browse/SPARK-10301 Project: Spark Issue Type: Bug Components: SQL Affects Versions: 1.5.0 Reporter: Yin Huai Assignee: Yin Huai Priority: Critical When parquet's global schema has less number of fields than the local schema of a file, the data reading path will fail. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-10301) For struct type, if parquet's global schema has less fields than a file's schema, data reading will fail
[ https://issues.apache.org/jira/browse/SPARK-10301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14721312#comment-14721312 ] Yin Huai commented on SPARK-10301: -- https://github.com/apache/spark/pull/8515 has been merged. It is not the fix for this issue but will give users a nice error message when the global schema as less struct fields than local parquet file schema (it will ask users to enable schema merging). I am re-targeting this issue to 1.6 for the proper fix (https://github.com/apache/spark/pull/8509). For struct type, if parquet's global schema has less fields than a file's schema, data reading will fail Key: SPARK-10301 URL: https://issues.apache.org/jira/browse/SPARK-10301 Project: Spark Issue Type: Bug Components: SQL Affects Versions: 1.5.0 Reporter: Yin Huai Assignee: Yin Huai Priority: Critical When parquet's global schema has less number of fields than the local schema of a file, the data reading path will fail. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-10301) For struct type, if parquet's global schema has less fields than a file's schema, data reading will fail
[ https://issues.apache.org/jira/browse/SPARK-10301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14720112#comment-14720112 ] Apache Spark commented on SPARK-10301: -- User 'liancheng' has created a pull request for this issue: https://github.com/apache/spark/pull/8509 For struct type, if parquet's global schema has less fields than a file's schema, data reading will fail Key: SPARK-10301 URL: https://issues.apache.org/jira/browse/SPARK-10301 Project: Spark Issue Type: Bug Components: SQL Affects Versions: 1.5.0 Reporter: Yin Huai Assignee: Yin Huai Priority: Critical When parquet's global schema has less number of fields than the local schema of a file, the data reading path will fail. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
