How to create external Hive table without location?

Question

I have a spark sql 2.1.1 job on a yarn cluster in cluster mode where I want to create an empty external hive table (partitions with location will be added in a later step).

CREATE EXTERNAL TABLE IF NOT EXISTS new_table (id BIGINT, StartTime TIMESTAMP, EndTime TIMESTAMP) PARTITIONED BY (year INT, month INT, day INT) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'

When I run the job I get the error:

CREATE EXTERNAL TABLE must be accompanied by LOCATION

But when I run the same query on Hive Editor on Hue it runs just fine. I was trying to find an answer in the SparkSQL 2.1.1 documentation but came up empty.

Does anyone know why Spark SQL is more strict on queries?

Answer 1

TL;DR EXTERNAL with no LOCATION is not allowed .

The definitive answer is in Spark SQL's grammar definition file SqlBase.g4 .

You can find the definition of CREATE EXTERNAL TABLE as createTableHeader :

CREATE TEMPORARY? EXTERNAL? TABLE (IF NOT EXISTS)? tableIdentifier

This definition is used in the supported SQL statements .

Unless I'm mistaken locationSpec is optional. That's according to the ANTLR grammar. The code may decide otherwise and it seems it does.

scala> spark.version
res4: String = 2.3.0-SNAPSHOT

val q = "CREATE EXTERNAL TABLE IF NOT EXISTS new_table (id BIGINT, StartTime TIMESTAMP, EndTime TIMESTAMP) PARTITIONED BY (year INT, month INT, day INT) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'"
scala> sql(q)
org.apache.spark.sql.catalyst.parser.ParseException:
Operation not allowed: CREATE EXTERNAL TABLE must be accompanied by LOCATION(line 1, pos 0)

== SQL ==
CREATE EXTERNAL TABLE IF NOT EXISTS new_table (id BIGINT, StartTime TIMESTAMP, EndTime TIMESTAMP) PARTITIONED BY (year INT, month INT, day INT) ROW FORMAT DELIMITED FIELDS TERMINATED BY ' '
^^^

  at org.apache.spark.sql.catalyst.parser.ParserUtils$.operationNotAllowed(ParserUtils.scala:39)
  at org.apache.spark.sql.execution.SparkSqlAstBuilder$$anonfun$visitCreateHiveTable$1.apply(SparkSqlParser.scala:1096)
  at org.apache.spark.sql.execution.SparkSqlAstBuilder$$anonfun$visitCreateHiveTable$1.apply(SparkSqlParser.scala:1064)
  at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:99)
  at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitCreateHiveTable(SparkSqlParser.scala:1064)
  at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitCreateHiveTable(SparkSqlParser.scala:55)
  at org.apache.spark.sql.catalyst.parser.SqlBaseParser$CreateHiveTableContext.accept(SqlBaseParser.java:1124)
  at org.antlr.v4.runtime.tree.AbstractParseTreeVisitor.visit(AbstractParseTreeVisitor.java:42)
  at org.apache.spark.sql.catalyst.parser.AstBuilder$$anonfun$visitSingleStatement$1.apply(AstBuilder.scala:71)
  at org.apache.spark.sql.catalyst.parser.AstBuilder$$anonfun$visitSingleStatement$1.apply(AstBuilder.scala:71)
  at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:99)
  at org.apache.spark.sql.catalyst.parser.AstBuilder.visitSingleStatement(AstBuilder.scala:70)
  at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parsePlan$1.apply(ParseDriver.scala:69)
  at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parsePlan$1.apply(ParseDriver.scala:68)
  at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:97)
  at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:48)
  at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:68)
  at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:623)
  ... 48 elided

The default SparkSqlParser (with astBuilder as SparkSqlAstBuilder ) has the following assertion that leads to the exception:

if (external && location.isEmpty) {
  operationNotAllowed("CREATE EXTERNAL TABLE must be accompanied by LOCATION", ctx)

---

I'd consider reporting an issue in Spark's JIRA if you think that the case should be allowed. See SPARK-2825 to have a strong argument to have the support:

CREATE EXTERNAL TABLE already works as far as I know and should have the same semantics as Hive.