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author | wangfei <wangfei1@huawei.com> | 2014-12-01 14:02:02 -0800 |
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committer | Michael Armbrust <michael@databricks.com> | 2014-12-01 14:02:28 -0800 |
commit | 31cf51bfaa0e332b903cb5d7f511dfa76d36bdc5 (patch) | |
tree | e28efd0bb03d3b7e891edbc5744418e802fc9fdd | |
parent | b39cfee0620ccd9c4e966a7d9bbd6017e35023cd (diff) | |
download | spark-31cf51bfaa0e332b903cb5d7f511dfa76d36bdc5.tar.gz spark-31cf51bfaa0e332b903cb5d7f511dfa76d36bdc5.tar.bz2 spark-31cf51bfaa0e332b903cb5d7f511dfa76d36bdc5.zip |
[SQL] Minor fix for doc and comment
Author: wangfei <wangfei1@huawei.com>
Closes #3533 from scwf/sql-doc1 and squashes the following commits:
962910b [wangfei] doc and comment fix
(cherry picked from commit 7b79957879db4dfcc7c3601cb40ac4fd576259a5)
Signed-off-by: Michael Armbrust <michael@databricks.com>
3 files changed, 7 insertions, 5 deletions
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 96a3209c52..c38ca55653 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -1002,7 +1002,7 @@ Several caching related features are not supported yet: ## Compatibility with Apache Hive Spark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs. Currently Spark -SQL is based on Hive 0.12.0. +SQL is based on Hive 0.12.0 and 0.13.1. #### Deploying in Existing Hive Warehouses @@ -1041,6 +1041,7 @@ Spark SQL supports the vast majority of Hive features, such as: * Sampling * Explain * Partitioned tables +* View * All Hive DDL Functions, including: * `CREATE TABLE` * `CREATE TABLE AS SELECT` diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala index 227acc1175..138923c4d7 100644 --- a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala +++ b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala @@ -29,9 +29,10 @@ object HiveFromSpark { val sc = new SparkContext(sparkConf) val path = s"${System.getenv("SPARK_HOME")}/examples/src/main/resources/kv1.txt" - // A local hive context creates an instance of the Hive Metastore in process, storing - // the warehouse data in the current directory. This location can be overridden by - // specifying a second parameter to the constructor. + // A hive context adds support for finding tables in the MetaStore and writing queries + // using HiveQL. Users who do not have an existing Hive deployment can still create a + // HiveContext. When not configured by the hive-site.xml, the context automatically + // creates metastore_db and warehouse in the current directory. val hiveContext = new HiveContext(sc) import hiveContext._ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 6404fec435..9b89c3bfb3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -49,7 +49,7 @@ class DefaultSource extends RelationProvider { sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = { val path = - parameters.getOrElse("path", sys.error("'path' must be specifed for parquet tables.")) + parameters.getOrElse("path", sys.error("'path' must be specified for parquet tables.")) ParquetRelation2(path)(sqlContext) } |