Actually, I was talking about the support for inferring different but compatible schemata from various files, automatically merging them into a single schema. However, you are right that I think you need to specify the columns / types if you create it as a Hive table.
On Fri, May 8, 2015 at 3:11 PM, Carlos Pereira <cpere...@groupon.com> wrote: > Thanks Michael for the quick return. I was looking forward the automatic > schema inferring (I think that's you mean by 'schema merging' ?), and I > think the STORED AS would still require me to define the table columns > right? > > Anyways, I am glad to hear you guys already working to fix this on future > releases. > > Thanks, > Carlos > > On Fri, May 8, 2015 at 2:43 PM, Michael Armbrust <mich...@databricks.com> > wrote: > >> This is an unfortunate limitation of the datasource api which does not >> support multiple databases. For parquet in particular (if you aren't using >> schema merging). You can create a hive table using STORED AS PARQUET >> today. I hope to fix this limitation in Spark 1.5. >> >> On Fri, May 8, 2015 at 2:41 PM, Carlos Pereira <cpere...@groupon.com> >> wrote: >> >>> Hi, I would like to create a hive table on top a existent parquet file as >>> described here: >>> >>> https://databricks.com/blog/2015/03/24/spark-sql-graduates-from-alpha-in-spark-1-3.html >>> >>> Due network restrictions, I need to store the metadata definition in a >>> different path than '/user/hive/warehouse', so I first set a new >>> database on >>> my own HDFS dir: >>> >>> CREATE DATABASE foo_db LOCATION '/user/foo'; >>> USE foo_db; >>> >>> And then I run the following query: >>> >>> CREATE TABLE mytable_parquet >>> USING parquet >>> OPTIONS (path "/user/foo/data.parquet") >>> >>> The problem is that SparkSQL is not using the same database defined the >>> in >>> shell context, but the default metastore instead of: >>> >>> ---------------------------- >>> > CREATE TABLE mytable_parquet USING parquet OPTIONS (path >>> "/user/foo/data.parquet"); >>> 15/05/08 20:42:21 INFO metastore.HiveMetaStore: 0: get_table : >>> *db=foo_db* >>> tbl=mytable_parquet >>> >>> 15/05/08 20:42:21 INFO HiveMetaStore.audit: ugi=foo >>> ip=unknown-ip-addr >>> cmd=get_table : db=foo_db tbl=mytable_parquet >>> >>> 15/05/08 20:42:21 INFO metastore.HiveMetaStore: 0: create_table: >>> Table(tableName:mytable_parquet, *dbName:default,* owner:foo, >>> createTime:1431117741, lastAccessTime:0, retention:0, >>> sd:StorageDescriptor(cols:[FieldSchema(name:col, type:array<string>, >>> comment:from deserializer)], location:null, >>> inputFormat:org.apache.hadoop.mapred.SequenceFileInputFormat, >>> outputFormat:org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat, >>> compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null, >>> >>> serializationLib:org.apache.hadoop.hive.serde2.MetadataTypedColumnsetSerDe, >>> parameters:{serialization.format=1, path=/user/foo/data.parquet}), >>> bucketCols:[], sortCols:[], parameters:{}, >>> skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[], >>> skewedColValueLocationMaps:{})), partitionKeys:[], >>> parameters:{EXTERNAL=TRUE, spark.sql.sources.provider=parquet}, >>> viewOriginalText:null, viewExpandedText:null, tableType:EXTERNAL_TABLE) >>> 15/05/08 20:42:21 INFO HiveMetaStore.audit: ugi=foo >>> ip=unknown-ip-addr >>> cmd=create_table: Table(tableName:mytable_parquet, dbName:default, >>> owner:foo, createTime:1431117741, lastAccessTime:0, retention:0, >>> sd:StorageDescriptor(cols:[FieldSchema(name:col, type:array<string>, >>> comment:from deserializer)], location:null, >>> inputFormat:org.apache.hadoop.mapred.SequenceFileInputFormat, >>> outputFormat:org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat, >>> compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null, >>> >>> serializationLib:org.apache.hadoop.hive.serde2.MetadataTypedColumnsetSerDe, >>> parameters:{serialization.format=1, path=/user/foo/data.parquet}), >>> bucketCols:[], sortCols:[], parameters:{}, >>> skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[], >>> skewedColValueLocationMaps:{})), partitionKeys:[], >>> parameters:{EXTERNAL=TRUE, spark.sql.sources.provider=parquet}, >>> viewOriginalText:null, viewExpandedText:null, tableType:EXTERNAL_TABLE) >>> >>> 15/05/08 20:42:21 ERROR hive.log: Got exception: >>> org.apache.hadoop.security.AccessControlException Permission denied: >>> user=foo, access=WRITE, >>> inode="/user/hive/warehouse":hive:grp_gdoop_hdfs:drwxr-xr-x >>> ---------------------------- >>> >>> >>> The permission error above happens because my linux user does not have >>> write >>> access on the default metastore path. I can workaround this issue if I >>> use >>> CREATE TEMPORARY TABLE and have no metadata written on disk. >>> >>> I would like to know if I am doing anything wrong here and if there is >>> any >>> additional property I can use to force the database/metastore_dir I need >>> to >>> write on. >>> >>> Thanks, >>> Carlos >>> >>> >>> >>> >>> -- >>> View this message in context: >>> http://apache-spark-user-list.1001560.n3.nabble.com/CREATE-TABLE-ignores-database-when-using-PARQUET-option-tp22824.html >>> Sent from the Apache Spark User List mailing list archive at Nabble.com. >>> >>> --------------------------------------------------------------------- >>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>> For additional commands, e-mail: user-h...@spark.apache.org >>> >>> >> >
