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https://issues.apache.org/jira/browse/SPARK-21994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16246463#comment-16246463
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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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