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Thomas Graves commented on SPARK-21841: --------------------------------------- Oh no I guess that is explicit check in spark 2.1: if (source.toLowerCase == DDLUtils.HIVE_PROVIDER) { throw new AnalysisException("Cannot create hive serde table with saveAsTable API") } Perhaps this is only supported in spark 2.2? > Spark SQL doesn't pick up column added in hive when table created with > saveAsTable > ---------------------------------------------------------------------------------- > > Key: SPARK-21841 > URL: https://issues.apache.org/jira/browse/SPARK-21841 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 2.1.0, 2.2.0 > Reporter: Thomas Graves > > If you create a table in Spark sql but then you modify the table in hive to > add a column, spark sql doesn't pick up the new column. > Basic example: > {code} > t1 = spark.sql("select ip_address from mydb.test_table limit 1") > t1.show() > +------------+ > | ip_address| > +------------+ > |1.30.25.5| > +------------+ > t1.write.saveAsTable('mydb.t1') > In Hive: > alter table mydb.t1 add columns (bcookie string) > t1 = spark.table("mydb.t1") > t1.show() > +------------+ > | ip_address| > +------------+ > |1.30.25.5| > +------------+ > {code} > It looks like its because spark sql is picking up the schema from > spark.sql.sources.schema.part.0 rather then from hive. > Interestingly enough it appears that if you create the table differently like: > spark.sql("create table mydb.t1 select ip_address from mydb.test_table limit > 1") > Run your alter table on mydb.t1 > val t1 = spark.table("mydb.t1") > Then it works properly. > It looks like the difference is when it doesn't work > spark.sql.sources.provider=parquet is set. > Its doing this from createDataSourceTable where provider is parquet. -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org