[jira] [Commented] (SPARK-21994) Spark 2.2 can not read Parquet table created by itself
[
https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16167276#comment-16167276
]
Xiayun Sun commented on SPARK-21994:
I'm unable to reproduce this for latest master build (commit a28728a, version
2.3.0-SNAPSHOT)
{{
scala> spark.sql("create database test")
res0: org.apache.spark.sql.DataFrame = []
scala> val df = spark.sql("show databases")
df: org.apache.spark.sql.DataFrame = [databaseName: string]
scala> df.show()
++
|databaseName|
++
| default|
|test|
++
scala> df.write.format("parquet").saveAsTable("test.spark22_test")
scala> spark.sql("select * from test.spark22_test").show()
++
|databaseName|
++
| default|
|test|
++
}}
> Spark 2.2 can not read Parquet table created by itself
> --
>
> Key: SPARK-21994
> URL: https://issues.apache.org/jira/browse/SPARK-21994
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.0
> Environment: Spark 2.2 on Cloudera CDH 5.10.1, Hive 1.1
>Reporter: Jurgis Pods
>
> This seems to be a new bug introduced in Spark 2.2, since it did not occur
> under Spark 2.1.
> When writing a dataframe to a table in Parquet format, Spark SQL does not
> write the 'path' of the table to the Hive metastore, unlike in previous
> versions.
> As a consequence, Spark 2.2 is not able to read the table it just created. It
> just outputs the table header without any row content.
> A parallel installation of Spark 1.6 at least produces an appropriate error
> trace:
> {code:java}
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Version information not found
> in metastore. hive.metastore.schema.verification is not enabled so recording
> the schema version 1.1.0
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Failed to get database default,
> returning NoSuchObjectException
> org.spark-project.guava.util.concurrent.UncheckedExecutionException:
> java.util.NoSuchElementException: key not found: path
> [...]
> {code}
> h3. Steps to reproduce:
> Run the following in spark2-shell:
> {code:java}
> scala> val df = spark.sql("show databases")
> scala> df.show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> scala> df.write.format("parquet").saveAsTable("test.spark22_test")
> scala> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> ++{code}
> When manually setting the path, it works:
> {code:java}
> scala> df.write.option("path",
> "/hadoop/eco/hive/warehouse/test.db/spark22_parquet_with_path").format("parquet").saveAsTable("test.spark22_parquet_with_path")
> scala> spark.sql("select * from test.spark22_parquet_with_path").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> It is kind of a disaster that we are not able to read tables created by the
> very same Spark version and have to manually specify the path as an explicit
> option.
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[jira] [Commented] (SPARK-21994) Spark 2.2 can not read Parquet table created by itself
[
https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16167280#comment-16167280
]
Jia-Xuan Liu commented on SPARK-21994:
--
I also can't reproduce this in Spark 2.2 release.
{code:java}
Spark context available as 'sc' (master = local[*], app id =
local-1505446512312).
Spark session available as 'spark'.
Welcome to
__
/ __/__ ___ _/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.2.0
/_/
Using Scala version 2.11.8 (OpenJDK 64-Bit Server VM, Java 1.8.0_131)
Type in expressions to have them evaluated.
Type :help for more information.
scala> val df = spark.sql("show databases")
df: org.apache.spark.sql.DataFrame = [databaseName: string]
scala> df.show()
++
|databaseName|
++
| default|
|test|
++
scala> df.write.format("parquet").saveAsTable("test.spark22_test_2")
scala> spark.sql("select * from test.spark22_test_2").show()
++
|databaseName|
++
| default|
|test|
++
{code}
> Spark 2.2 can not read Parquet table created by itself
> --
>
> Key: SPARK-21994
> URL: https://issues.apache.org/jira/browse/SPARK-21994
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.0
> Environment: Spark 2.2 on Cloudera CDH 5.10.1, Hive 1.1
>Reporter: Jurgis Pods
>
> This seems to be a new bug introduced in Spark 2.2, since it did not occur
> under Spark 2.1.
> When writing a dataframe to a table in Parquet format, Spark SQL does not
> write the 'path' of the table to the Hive metastore, unlike in previous
> versions.
> As a consequence, Spark 2.2 is not able to read the table it just created. It
> just outputs the table header without any row content.
> A parallel installation of Spark 1.6 at least produces an appropriate error
> trace:
> {code:java}
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Version information not found
> in metastore. hive.metastore.schema.verification is not enabled so recording
> the schema version 1.1.0
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Failed to get database default,
> returning NoSuchObjectException
> org.spark-project.guava.util.concurrent.UncheckedExecutionException:
> java.util.NoSuchElementException: key not found: path
> [...]
> {code}
> h3. Steps to reproduce:
> Run the following in spark2-shell:
> {code:java}
> scala> val df = spark.sql("show databases")
> scala> df.show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> scala> df.write.format("parquet").saveAsTable("test.spark22_test")
> scala> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> ++{code}
> When manually setting the path, it works:
> {code:java}
> scala> df.write.option("path",
> "/hadoop/eco/hive/warehouse/test.db/spark22_parquet_with_path").format("parquet").saveAsTable("test.spark22_parquet_with_path")
> scala> spark.sql("select * from test.spark22_parquet_with_path").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> It is kind of a disaster that we are not able to read tables created by the
> very same Spark version and have to manually specify the path as an explicit
> option.
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[jira] [Commented] (SPARK-21994) Spark 2.2 can not read Parquet table created by itself
[
https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16167407#comment-16167407
]
Jurgis Pods commented on SPARK-21994:
-
Thank you for testing. Which version of Hive are you using? It might be an
incompatibility between Spark 2.2 and Hive 1.1 (or other components of on
Cloudera CDH 5.10.1). I will upgrade to latest CDH 5.12 and report back if the
problem persists.
> Spark 2.2 can not read Parquet table created by itself
> --
>
> Key: SPARK-21994
> URL: https://issues.apache.org/jira/browse/SPARK-21994
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.0
> Environment: Spark 2.2 on Cloudera CDH 5.10.1, Hive 1.1
>Reporter: Jurgis Pods
>
> This seems to be a new bug introduced in Spark 2.2, since it did not occur
> under Spark 2.1.
> When writing a dataframe to a table in Parquet format, Spark SQL does not
> write the 'path' of the table to the Hive metastore, unlike in previous
> versions.
> As a consequence, Spark 2.2 is not able to read the table it just created. It
> just outputs the table header without any row content.
> A parallel installation of Spark 1.6 at least produces an appropriate error
> trace:
> {code:java}
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Version information not found
> in metastore. hive.metastore.schema.verification is not enabled so recording
> the schema version 1.1.0
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Failed to get database default,
> returning NoSuchObjectException
> org.spark-project.guava.util.concurrent.UncheckedExecutionException:
> java.util.NoSuchElementException: key not found: path
> [...]
> {code}
> h3. Steps to reproduce:
> Run the following in spark2-shell:
> {code:java}
> scala> val df = spark.sql("show databases")
> scala> df.show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> scala> df.write.format("parquet").saveAsTable("test.spark22_test")
> scala> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> ++{code}
> When manually setting the path, it works:
> {code:java}
> scala> df.write.option("path",
> "/hadoop/eco/hive/warehouse/test.db/spark22_parquet_with_path").format("parquet").saveAsTable("test.spark22_parquet_with_path")
> scala> spark.sql("select * from test.spark22_parquet_with_path").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> It is kind of a disaster that we are not able to read tables created by the
> very same Spark version and have to manually specify the path as an explicit
> option.
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[jira] [Commented] (SPARK-21994) Spark 2.2 can not read Parquet table created by itself
[
https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16167788#comment-16167788
]
Jurgis Pods commented on SPARK-21994:
-
I have updated to CDH 5.12.1 and the problem persists. There is an existing
topic on the Cloudera forums with exactly this problem:
http://community.cloudera.com/t5/forums/replypage/board-id/Spark/message-id/2867
> Spark 2.2 can not read Parquet table created by itself
> --
>
> Key: SPARK-21994
> URL: https://issues.apache.org/jira/browse/SPARK-21994
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.0
> Environment: Spark 2.2 on Cloudera CDH 5.10.1, Hive 1.1
>Reporter: Jurgis Pods
>
> This seems to be a new bug introduced in Spark 2.2, since it did not occur
> under Spark 2.1.
> When writing a dataframe to a table in Parquet format, Spark SQL does not
> write the 'path' of the table to the Hive metastore, unlike in previous
> versions.
> As a consequence, Spark 2.2 is not able to read the table it just created. It
> just outputs the table header without any row content.
> A parallel installation of Spark 1.6 at least produces an appropriate error
> trace:
> {code:java}
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Version information not found
> in metastore. hive.metastore.schema.verification is not enabled so recording
> the schema version 1.1.0
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Failed to get database default,
> returning NoSuchObjectException
> org.spark-project.guava.util.concurrent.UncheckedExecutionException:
> java.util.NoSuchElementException: key not found: path
> [...]
> {code}
> h3. Steps to reproduce:
> Run the following in spark2-shell:
> {code:java}
> scala> val df = spark.sql("show databases")
> scala> df.show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> scala> df.write.format("parquet").saveAsTable("test.spark22_test")
> scala> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> ++{code}
> When manually setting the path (causing the data to be saved as external
> table), it works:
> {code:java}
> scala> df.write.option("path",
> "/hadoop/eco/hive/warehouse/test.db/spark22_parquet_with_path").format("parquet").saveAsTable("test.spark22_parquet_with_path")
> scala> spark.sql("select * from test.spark22_parquet_with_path").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> A second workaround is to update the metadata of the managed table created by
> Spark 2.2:
> {code}
> spark.sql("alter table test.spark22_test set SERDEPROPERTIES
> ('path'='hdfs://my-cluster-name:8020/hadoop/eco/hive/warehouse/test.db/spark22_test')")
> spark.catalog.refreshTable("test.spark22_test")
> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> It is kind of a disaster that we are not able to read tables created by the
> very same Spark version and have to manually specify the path as an explicit
> option.
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[jira] [Commented] (SPARK-21994) Spark 2.2 can not read Parquet table created by itself
[
https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16246463#comment-16246463
]
Srinivasa Reddy Vundela commented on SPARK-21994:
-
This issue is related to Cloudera spark and got fixed recently. We can close
this jira.
> Spark 2.2 can not read Parquet table created by itself
> --
>
> Key: SPARK-21994
> URL: https://issues.apache.org/jira/browse/SPARK-21994
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.0
> Environment: Spark 2.2 on Cloudera CDH 5.10.1, Hive 1.1
>Reporter: Jurgis Pods
>
> This seems to be a new bug introduced in Spark 2.2, since it did not occur
> under Spark 2.1.
> When writing a dataframe to a table in Parquet format, Spark SQL does not
> write the 'path' of the table to the Hive metastore, unlike in previous
> versions.
> As a consequence, Spark 2.2 is not able to read the table it just created. It
> just outputs the table header without any row content.
> A parallel installation of Spark 1.6 at least produces an appropriate error
> trace:
> {code:java}
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Version information not found
> in metastore. hive.metastore.schema.verification is not enabled so recording
> the schema version 1.1.0
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Failed to get database default,
> returning NoSuchObjectException
> org.spark-project.guava.util.concurrent.UncheckedExecutionException:
> java.util.NoSuchElementException: key not found: path
> [...]
> {code}
> h3. Steps to reproduce:
> Run the following in spark2-shell:
> {code:java}
> scala> val df = spark.sql("show databases")
> scala> df.show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> scala> df.write.format("parquet").saveAsTable("test.spark22_test")
> scala> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> ++{code}
> When manually setting the path (causing the data to be saved as external
> table), it works:
> {code:java}
> scala> df.write.option("path",
> "/hadoop/eco/hive/warehouse/test.db/spark22_parquet_with_path").format("parquet").saveAsTable("test.spark22_parquet_with_path")
> scala> spark.sql("select * from test.spark22_parquet_with_path").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> A second workaround is to update the metadata of the managed table created by
> Spark 2.2:
> {code}
> spark.sql("alter table test.spark22_test set SERDEPROPERTIES
> ('path'='hdfs://my-cluster-name:8020/hadoop/eco/hive/warehouse/test.db/spark22_test')")
> spark.catalog.refreshTable("test.spark22_test")
> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> It is kind of a disaster that we are not able to read tables created by the
> very same Spark version and have to manually specify the path as an explicit
> option.
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[jira] [Commented] (SPARK-21994) Spark 2.2 can not read Parquet table created by itself
[
https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16247403#comment-16247403
]
Guillaume Van Delsen commented on SPARK-21994:
--
[~vsr] Good news, could share Cloudera logchange about this fix ? Did not find
it anywhere. Thanks!
> Spark 2.2 can not read Parquet table created by itself
> --
>
> Key: SPARK-21994
> URL: https://issues.apache.org/jira/browse/SPARK-21994
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.0
> Environment: Spark 2.2 on Cloudera CDH 5.10.1, Hive 1.1
>Reporter: Jurgis Pods
>
> This seems to be a new bug introduced in Spark 2.2, since it did not occur
> under Spark 2.1.
> When writing a dataframe to a table in Parquet format, Spark SQL does not
> write the 'path' of the table to the Hive metastore, unlike in previous
> versions.
> As a consequence, Spark 2.2 is not able to read the table it just created. It
> just outputs the table header without any row content.
> A parallel installation of Spark 1.6 at least produces an appropriate error
> trace:
> {code:java}
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Version information not found
> in metastore. hive.metastore.schema.verification is not enabled so recording
> the schema version 1.1.0
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Failed to get database default,
> returning NoSuchObjectException
> org.spark-project.guava.util.concurrent.UncheckedExecutionException:
> java.util.NoSuchElementException: key not found: path
> [...]
> {code}
> h3. Steps to reproduce:
> Run the following in spark2-shell:
> {code:java}
> scala> val df = spark.sql("show databases")
> scala> df.show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> scala> df.write.format("parquet").saveAsTable("test.spark22_test")
> scala> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> ++{code}
> When manually setting the path (causing the data to be saved as external
> table), it works:
> {code:java}
> scala> df.write.option("path",
> "/hadoop/eco/hive/warehouse/test.db/spark22_parquet_with_path").format("parquet").saveAsTable("test.spark22_parquet_with_path")
> scala> spark.sql("select * from test.spark22_parquet_with_path").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> A second workaround is to update the metadata of the managed table created by
> Spark 2.2:
> {code}
> spark.sql("alter table test.spark22_test set SERDEPROPERTIES
> ('path'='hdfs://my-cluster-name:8020/hadoop/eco/hive/warehouse/test.db/spark22_test')")
> spark.catalog.refreshTable("test.spark22_test")
> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> It is kind of a disaster that we are not able to read tables created by the
> very same Spark version and have to manually specify the path as an explicit
> option.
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[jira] [Commented] (SPARK-21994) Spark 2.2 can not read Parquet table created by itself
[
https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16250032#comment-16250032
]
Srinivasa Reddy Vundela commented on SPARK-21994:
-
commit d5e3ba3e970c7241298db2578f0d7965b6e16ae3
Author: Srinivasa Reddy Vundela
Date: Mon Oct 9 14:25:01 2017 -0700
CDH-60037. Not able to read hive table from Cloudera version of Spark 2.2
> Spark 2.2 can not read Parquet table created by itself
> --
>
> Key: SPARK-21994
> URL: https://issues.apache.org/jira/browse/SPARK-21994
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.0
> Environment: Spark 2.2 on Cloudera CDH 5.10.1, Hive 1.1
>Reporter: Jurgis Pods
>
> This seems to be a new bug introduced in Spark 2.2, since it did not occur
> under Spark 2.1.
> When writing a dataframe to a table in Parquet format, Spark SQL does not
> write the 'path' of the table to the Hive metastore, unlike in previous
> versions.
> As a consequence, Spark 2.2 is not able to read the table it just created. It
> just outputs the table header without any row content.
> A parallel installation of Spark 1.6 at least produces an appropriate error
> trace:
> {code:java}
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Version information not found
> in metastore. hive.metastore.schema.verification is not enabled so recording
> the schema version 1.1.0
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Failed to get database default,
> returning NoSuchObjectException
> org.spark-project.guava.util.concurrent.UncheckedExecutionException:
> java.util.NoSuchElementException: key not found: path
> [...]
> {code}
> h3. Steps to reproduce:
> Run the following in spark2-shell:
> {code:java}
> scala> val df = spark.sql("show databases")
> scala> df.show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> scala> df.write.format("parquet").saveAsTable("test.spark22_test")
> scala> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> ++{code}
> When manually setting the path (causing the data to be saved as external
> table), it works:
> {code:java}
> scala> df.write.option("path",
> "/hadoop/eco/hive/warehouse/test.db/spark22_parquet_with_path").format("parquet").saveAsTable("test.spark22_parquet_with_path")
> scala> spark.sql("select * from test.spark22_parquet_with_path").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> A second workaround is to update the metadata of the managed table created by
> Spark 2.2:
> {code}
> spark.sql("alter table test.spark22_test set SERDEPROPERTIES
> ('path'='hdfs://my-cluster-name:8020/hadoop/eco/hive/warehouse/test.db/spark22_test')")
> spark.catalog.refreshTable("test.spark22_test")
> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> It is kind of a disaster that we are not able to read tables created by the
> very same Spark version and have to manually specify the path as an explicit
> option.
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[jira] [Commented] (SPARK-21994) Spark 2.2 can not read Parquet table created by itself
[
https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16250039#comment-16250039
]
Sean Owen commented on SPARK-21994:
---
(Don't think that would be meaningful outside Cloudera at the moment; the
commit doesn't exist in the public release/repo yet)
> Spark 2.2 can not read Parquet table created by itself
> --
>
> Key: SPARK-21994
> URL: https://issues.apache.org/jira/browse/SPARK-21994
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.0
> Environment: Spark 2.2 on Cloudera CDH 5.10.1, Hive 1.1
>Reporter: Jurgis Pods
>
> This seems to be a new bug introduced in Spark 2.2, since it did not occur
> under Spark 2.1.
> When writing a dataframe to a table in Parquet format, Spark SQL does not
> write the 'path' of the table to the Hive metastore, unlike in previous
> versions.
> As a consequence, Spark 2.2 is not able to read the table it just created. It
> just outputs the table header without any row content.
> A parallel installation of Spark 1.6 at least produces an appropriate error
> trace:
> {code:java}
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Version information not found
> in metastore. hive.metastore.schema.verification is not enabled so recording
> the schema version 1.1.0
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Failed to get database default,
> returning NoSuchObjectException
> org.spark-project.guava.util.concurrent.UncheckedExecutionException:
> java.util.NoSuchElementException: key not found: path
> [...]
> {code}
> h3. Steps to reproduce:
> Run the following in spark2-shell:
> {code:java}
> scala> val df = spark.sql("show databases")
> scala> df.show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> scala> df.write.format("parquet").saveAsTable("test.spark22_test")
> scala> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> ++{code}
> When manually setting the path (causing the data to be saved as external
> table), it works:
> {code:java}
> scala> df.write.option("path",
> "/hadoop/eco/hive/warehouse/test.db/spark22_parquet_with_path").format("parquet").saveAsTable("test.spark22_parquet_with_path")
> scala> spark.sql("select * from test.spark22_parquet_with_path").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> A second workaround is to update the metadata of the managed table created by
> Spark 2.2:
> {code}
> spark.sql("alter table test.spark22_test set SERDEPROPERTIES
> ('path'='hdfs://my-cluster-name:8020/hadoop/eco/hive/warehouse/test.db/spark22_test')")
> spark.catalog.refreshTable("test.spark22_test")
> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> It is kind of a disaster that we are not able to read tables created by the
> very same Spark version and have to manually specify the path as an explicit
> option.
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[jira] [Commented] (SPARK-21994) Spark 2.2 can not read Parquet table created by itself
[
https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16250066#comment-16250066
]
Srinivasa Reddy Vundela commented on SPARK-21994:
-
[~srowen] Thats right, it is not available in public release yet. I just posted
for reference.
> Spark 2.2 can not read Parquet table created by itself
> --
>
> Key: SPARK-21994
> URL: https://issues.apache.org/jira/browse/SPARK-21994
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.0
> Environment: Spark 2.2 on Cloudera CDH 5.10.1, Hive 1.1
>Reporter: Jurgis Pods
>
> This seems to be a new bug introduced in Spark 2.2, since it did not occur
> under Spark 2.1.
> When writing a dataframe to a table in Parquet format, Spark SQL does not
> write the 'path' of the table to the Hive metastore, unlike in previous
> versions.
> As a consequence, Spark 2.2 is not able to read the table it just created. It
> just outputs the table header without any row content.
> A parallel installation of Spark 1.6 at least produces an appropriate error
> trace:
> {code:java}
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Version information not found
> in metastore. hive.metastore.schema.verification is not enabled so recording
> the schema version 1.1.0
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Failed to get database default,
> returning NoSuchObjectException
> org.spark-project.guava.util.concurrent.UncheckedExecutionException:
> java.util.NoSuchElementException: key not found: path
> [...]
> {code}
> h3. Steps to reproduce:
> Run the following in spark2-shell:
> {code:java}
> scala> val df = spark.sql("show databases")
> scala> df.show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> scala> df.write.format("parquet").saveAsTable("test.spark22_test")
> scala> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> ++{code}
> When manually setting the path (causing the data to be saved as external
> table), it works:
> {code:java}
> scala> df.write.option("path",
> "/hadoop/eco/hive/warehouse/test.db/spark22_parquet_with_path").format("parquet").saveAsTable("test.spark22_parquet_with_path")
> scala> spark.sql("select * from test.spark22_parquet_with_path").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> A second workaround is to update the metadata of the managed table created by
> Spark 2.2:
> {code}
> spark.sql("alter table test.spark22_test set SERDEPROPERTIES
> ('path'='hdfs://my-cluster-name:8020/hadoop/eco/hive/warehouse/test.db/spark22_test')")
> spark.catalog.refreshTable("test.spark22_test")
> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> It is kind of a disaster that we are not able to read tables created by the
> very same Spark version and have to manually specify the path as an explicit
> option.
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[jira] [Commented] (SPARK-21994) Spark 2.2 can not read Parquet table created by itself
[
https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16299780#comment-16299780
]
Jurgis Pods commented on SPARK-21994:
-
[~vsr], do you know when the mentioned fix in Cloudera will make it to the next
release?
The Cloudera documentation does not list a new version of Spark 2.2 as of yet:
https://www.cloudera.com/documentation/spark2/latest/topics/spark2_packaging.html
> Spark 2.2 can not read Parquet table created by itself
> --
>
> Key: SPARK-21994
> URL: https://issues.apache.org/jira/browse/SPARK-21994
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.0
> Environment: Spark 2.2 on Cloudera CDH 5.10.1, Hive 1.1
>Reporter: Jurgis Pods
>
> This seems to be a new bug introduced in Spark 2.2, since it did not occur
> under Spark 2.1.
> When writing a dataframe to a table in Parquet format, Spark SQL does not
> write the 'path' of the table to the Hive metastore, unlike in previous
> versions.
> As a consequence, Spark 2.2 is not able to read the table it just created. It
> just outputs the table header without any row content.
> A parallel installation of Spark 1.6 at least produces an appropriate error
> trace:
> {code:java}
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Version information not found
> in metastore. hive.metastore.schema.verification is not enabled so recording
> the schema version 1.1.0
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Failed to get database default,
> returning NoSuchObjectException
> org.spark-project.guava.util.concurrent.UncheckedExecutionException:
> java.util.NoSuchElementException: key not found: path
> [...]
> {code}
> h3. Steps to reproduce:
> Run the following in spark2-shell:
> {code:java}
> scala> val df = spark.sql("show databases")
> scala> df.show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> scala> df.write.format("parquet").saveAsTable("test.spark22_test")
> scala> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> ++{code}
> When manually setting the path (causing the data to be saved as external
> table), it works:
> {code:java}
> scala> df.write.option("path",
> "/hadoop/eco/hive/warehouse/test.db/spark22_parquet_with_path").format("parquet").saveAsTable("test.spark22_parquet_with_path")
> scala> spark.sql("select * from test.spark22_parquet_with_path").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> A second workaround is to update the metadata of the managed table created by
> Spark 2.2:
> {code}
> spark.sql("alter table test.spark22_test set SERDEPROPERTIES
> ('path'='hdfs://my-cluster-name:8020/hadoop/eco/hive/warehouse/test.db/spark22_test')")
> spark.catalog.refreshTable("test.spark22_test")
> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> It is kind of a disaster that we are not able to read tables created by the
> very same Spark version and have to manually specify the path as an explicit
> option.
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[jira] [Commented] (SPARK-21994) Spark 2.2 can not read Parquet table created by itself
[
https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16923450#comment-16923450
]
Tomasz Belina commented on SPARK-21994:
---
I've experienced the same issue on spark 2.4.3
> Spark 2.2 can not read Parquet table created by itself
> --
>
> Key: SPARK-21994
> URL: https://issues.apache.org/jira/browse/SPARK-21994
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.0
> Environment: Spark 2.2 on Cloudera CDH 5.10.1, Hive 1.1
>Reporter: Jurgis Pods
>Priority: Major
>
> This seems to be a new bug introduced in Spark 2.2, since it did not occur
> under Spark 2.1.
> When writing a dataframe to a table in Parquet format, Spark SQL does not
> write the 'path' of the table to the Hive metastore, unlike in previous
> versions.
> As a consequence, Spark 2.2 is not able to read the table it just created. It
> just outputs the table header without any row content.
> A parallel installation of Spark 1.6 at least produces an appropriate error
> trace:
> {code:java}
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Version information not found
> in metastore. hive.metastore.schema.verification is not enabled so recording
> the schema version 1.1.0
> 17/09/13 10:22:12 WARN metastore.ObjectStore: Failed to get database default,
> returning NoSuchObjectException
> org.spark-project.guava.util.concurrent.UncheckedExecutionException:
> java.util.NoSuchElementException: key not found: path
> [...]
> {code}
> h3. Steps to reproduce:
> Run the following in spark2-shell:
> {code:java}
> scala> val df = spark.sql("show databases")
> scala> df.show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> scala> df.write.format("parquet").saveAsTable("test.spark22_test")
> scala> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> ++{code}
> When manually setting the path (causing the data to be saved as external
> table), it works:
> {code:java}
> scala> df.write.option("path",
> "/hadoop/eco/hive/warehouse/test.db/spark22_parquet_with_path").format("parquet").saveAsTable("test.spark22_parquet_with_path")
> scala> spark.sql("select * from test.spark22_parquet_with_path").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> A second workaround is to update the metadata of the managed table created by
> Spark 2.2:
> {code}
> spark.sql("alter table test.spark22_test set SERDEPROPERTIES
> ('path'='hdfs://my-cluster-name:8020/hadoop/eco/hive/warehouse/test.db/spark22_test')")
> spark.catalog.refreshTable("test.spark22_test")
> spark.sql("select * from test.spark22_test").show()
> ++
> |databaseName|
> ++
> | mydb1|
> | mydb2|
> | default|
> |test|
> ++
> {code}
> It is kind of a disaster that we are not able to read tables created by the
> very same Spark version and have to manually specify the path as an explicit
> option.
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