Jurgis Pods created SPARK-21994:
-----------------------------------
Summary: 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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