Hi,
While running commands from Hudi quick start guide, I found that the
library does not check for the table name in the request against the table
name in the metadata available in the base path, I think it should throw
TableAlreadyExist, In case of Save mode: *overwrite *it warns.
*spark-2.4.4-bin-hadoop2.7/bin/spark-shell --packages
org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4
--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'*
scala> df.write.format("hudi").
| options(getQuickstartWriteConfigs).
| option(PRECOMBINE_FIELD_OPT_KEY, "ts").
| option(RECORDKEY_FIELD_OPT_KEY, "uuid").
| option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
* | option(TABLE_NAME, "test_table").*
| mode(*Append*).
| save(basePath)
20/04/29 17:23:42 WARN DefaultSource: Snapshot view not supported yet via
data source, for MERGE_ON_READ tables. Please query the Hive table
registered using Spark SQL.
scala>
No exception is thrown if we run this
scala> df.write.format("hudi").
| options(getQuickstartWriteConfigs).
| option(PRECOMBINE_FIELD_OPT_KEY, "ts").
| option(RECORDKEY_FIELD_OPT_KEY, "uuid").
| option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
* | option(TABLE_NAME, "foo_table").*
| mode(*Append*).
| save(basePath)
20/04/29 17:24:37 WARN DefaultSource: Snapshot view not supported yet via
data source, for MERGE_ON_READ tables. Please query the Hive table
registered using Spark SQL.
scala>
scala> df.write.format("hudi").
| options(getQuickstartWriteConfigs).
| option(PRECOMBINE_FIELD_OPT_KEY, "ts").
| option(RECORDKEY_FIELD_OPT_KEY, "uuid").
| option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
| option(TABLE_NAME, *tableName*).
| mode(*Overwrite*).
| save(basePath)
*20/04/29 22:25:16 WARN HoodieSparkSqlWriter$: hoodie table at
file:/tmp/hudi_trips_cow already exists. Deleting existing data &
overwriting with new data.*
20/04/29 22:25:18 WARN DefaultSource: Snapshot view not supported yet via
data source, for MERGE_ON_READ tables. Please query the Hive table
registered using Spark SQL.
scala>
Regards,
Aakash
