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Diffstat (limited to 'docs/sql-programming-guide.md')
| -rw-r--r-- | docs/sql-programming-guide.md | 20 |
1 files changed, 10 insertions, 10 deletions
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 8e98cc0c80..e25379bd76 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -14,8 +14,8 @@ title: Spark SQL Programming Guide Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using Spark. At the core of this component is a new type of RDD, -[SchemaRDD](api/sql/core/index.html#org.apache.spark.sql.SchemaRDD). SchemaRDDs are composed -[Row](api/sql/catalyst/index.html#org.apache.spark.sql.catalyst.expressions.Row) objects along with +[SchemaRDD](api/scala/index.html#org.apache.spark.sql.SchemaRDD). SchemaRDDs are composed +[Row](api/scala/index.html#org.apache.spark.sql.catalyst.expressions.Row) objects along with a schema that describes the data types of each column in the row. A SchemaRDD is similar to a table in a traditional relational database. A SchemaRDD can be created from an existing RDD, parquet file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/). @@ -27,8 +27,8 @@ file, or by running HiveQL against data stored in [Apache Hive](http://hive.apac <div data-lang="java" markdown="1"> Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using Spark. At the core of this component is a new type of RDD, -[JavaSchemaRDD](api/sql/core/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD). JavaSchemaRDDs are composed -[Row](api/sql/catalyst/index.html#org.apache.spark.sql.api.java.Row) objects along with +[JavaSchemaRDD](api/scala/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD). JavaSchemaRDDs are composed +[Row](api/scala/index.html#org.apache.spark.sql.api.java.Row) objects along with a schema that describes the data types of each column in the row. A JavaSchemaRDD is similar to a table in a traditional relational database. A JavaSchemaRDD can be created from an existing RDD, parquet file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/). @@ -38,8 +38,8 @@ file, or by running HiveQL against data stored in [Apache Hive](http://hive.apac Spark SQL allows relational queries expressed in SQL or HiveQL to be executed using Spark. At the core of this component is a new type of RDD, -[SchemaRDD](api/pyspark/pyspark.sql.SchemaRDD-class.html). SchemaRDDs are composed -[Row](api/pyspark/pyspark.sql.Row-class.html) objects along with +[SchemaRDD](api/python/pyspark.sql.SchemaRDD-class.html). SchemaRDDs are composed +[Row](api/python/pyspark.sql.Row-class.html) objects along with a schema that describes the data types of each column in the row. A SchemaRDD is similar to a table in a traditional relational database. A SchemaRDD can be created from an existing RDD, parquet file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/). @@ -56,7 +56,7 @@ file, or by running HiveQL against data stored in [Apache Hive](http://hive.apac <div data-lang="scala" markdown="1"> The entry point into all relational functionality in Spark is the -[SQLContext](api/sql/core/index.html#org.apache.spark.sql.SQLContext) class, or one of its +[SQLContext](api/scala/index.html#org.apache.spark.sql.SQLContext) class, or one of its descendants. To create a basic SQLContext, all you need is a SparkContext. {% highlight scala %} @@ -72,7 +72,7 @@ import sqlContext._ <div data-lang="java" markdown="1"> The entry point into all relational functionality in Spark is the -[JavaSQLContext](api/sql/core/index.html#org.apache.spark.sql.api.java.JavaSQLContext) class, or one +[JavaSQLContext](api/scala/index.html#org.apache.spark.sql.api.java.JavaSQLContext) class, or one of its descendants. To create a basic JavaSQLContext, all you need is a JavaSparkContext. {% highlight java %} @@ -85,7 +85,7 @@ JavaSQLContext sqlCtx = new org.apache.spark.sql.api.java.JavaSQLContext(ctx); <div data-lang="python" markdown="1"> The entry point into all relational functionality in Spark is the -[SQLContext](api/pyspark/pyspark.sql.SQLContext-class.html) class, or one +[SQLContext](api/python/pyspark.sql.SQLContext-class.html) class, or one of its decedents. To create a basic SQLContext, all you need is a SparkContext. {% highlight python %} @@ -331,7 +331,7 @@ val teenagers = people.where('age >= 10).where('age <= 19).select('name) The DSL uses Scala symbols to represent columns in the underlying table, which are identifiers prefixed with a tick (`'`). Implicit conversions turn these symbols into expressions that are evaluated by the SQL execution engine. A full list of the functions supported can be found in the -[ScalaDoc](api/sql/core/index.html#org.apache.spark.sql.SchemaRDD). +[ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD). <!-- TODO: Include the table of operations here. --> |