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author | Dongjoon Hyun <dongjoon@apache.org> | 2016-05-17 20:50:22 +0200 |
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committer | Nick Pentreath <nickp@za.ibm.com> | 2016-05-17 20:50:22 +0200 |
commit | 9f176dd3918129a72282a6b7a12e2899cbb6dac9 (patch) | |
tree | a7feca1f7b01ea38112e6ec7498f1d070ad415ff | |
parent | 3308a862ba0983268c9d5acf9e2a7d2b62d3ec27 (diff) | |
download | spark-9f176dd3918129a72282a6b7a12e2899cbb6dac9.tar.gz spark-9f176dd3918129a72282a6b7a12e2899cbb6dac9.tar.bz2 spark-9f176dd3918129a72282a6b7a12e2899cbb6dac9.zip |
[MINOR][DOCS] Replace remaining 'sqlContext' in ScalaDoc/JavaDoc.
## What changes were proposed in this pull request?
According to the recent change, this PR replaces all the remaining `sqlContext` usage with `spark` in ScalaDoc/JavaDoc (.scala/.java files) except `SQLContext.scala`, `SparkPlan.scala', and `DatasetHolder.scala`.
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #13125 from dongjoon-hyun/minor_doc_sparksession.
6 files changed, 15 insertions, 15 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala index 4571ab2680..b94187ae78 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala @@ -44,7 +44,7 @@ import org.apache.spark.sql.DataFrame * import org.apache.spark.ml.Pipeline * * // a DataFrame with three columns: id (integer), text (string), and rating (double). - * val df = sqlContext.createDataFrame(Seq( + * val df = spark.createDataFrame(Seq( * (0, "Hi I heard about Spark", 3.0), * (1, "I wish Java could use case classes", 4.0), * (2, "Logistic regression models are neat", 4.0) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala index e1a64dfc5e..011aff4ff6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala @@ -446,10 +446,10 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging { * Each line in the text file is a new row in the resulting Dataset. For example: * {{{ * // Scala: - * sqlContext.read.text("/path/to/spark/README.md") + * spark.read.text("/path/to/spark/README.md") * * // Java: - * sqlContext.read().text("/path/to/spark/README.md") + * spark.read().text("/path/to/spark/README.md") * }}} * * @param paths input path diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala index 3eb1f0f0d5..1855eab96e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala @@ -160,8 +160,8 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * @return A DataFrame containing for the contingency table. * * {{{ - * val df = sqlContext.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), - * (3, 3))).toDF("key", "value") + * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3))) + * .toDF("key", "value") * val ct = df.stat.crosstab("key", "value") * ct.show() * +---------+---+---+---+ @@ -197,7 +197,7 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * val rows = Seq.tabulate(100) { i => * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0) * } - * val df = sqlContext.createDataFrame(rows).toDF("a", "b") + * val df = spark.createDataFrame(rows).toDF("a", "b") * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns * // "a" and "b" * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4) @@ -258,7 +258,7 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * val rows = Seq.tabulate(100) { i => * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0) * } - * val df = sqlContext.createDataFrame(rows).toDF("a", "b") + * val df = spark.createDataFrame(rows).toDF("a", "b") * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns * // "a" and "b" * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4) @@ -314,7 +314,7 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { * @return a new [[DataFrame]] that represents the stratified sample * * {{{ - * val df = sqlContext.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), + * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), * (3, 3))).toDF("key", "value") * val fractions = Map(1 -> 1.0, 3 -> 0.5) * df.stat.sampleBy("key", fractions, 36L).show() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala b/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala index a49da6dc2b..a435734b0c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/ExperimentalMethods.scala @@ -27,7 +27,7 @@ import org.apache.spark.sql.catalyst.rules.Rule * regarding binary compatibility and source compatibility of methods here. * * {{{ - * sqlContext.experimental.extraStrategies += ... + * spark.experimental.extraStrategies += ... * }}} * * @since 1.3.0 diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningAwareFileCatalog.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningAwareFileCatalog.scala index e0e4ddc30b..406d2e8e81 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningAwareFileCatalog.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningAwareFileCatalog.scala @@ -168,17 +168,17 @@ abstract class PartitioningAwareFileCatalog( * * By default, the paths of the dataset provided by users will be base paths. * Below are three typical examples, - * Case 1) `sqlContext.read.parquet("/path/something=true/")`: the base path will be + * Case 1) `spark.read.parquet("/path/something=true/")`: the base path will be * `/path/something=true/`, and the returned DataFrame will not contain a column of `something`. - * Case 2) `sqlContext.read.parquet("/path/something=true/a.parquet")`: the base path will be + * Case 2) `spark.read.parquet("/path/something=true/a.parquet")`: the base path will be * still `/path/something=true/`, and the returned DataFrame will also not contain a column of * `something`. - * Case 3) `sqlContext.read.parquet("/path/")`: the base path will be `/path/`, and the returned + * Case 3) `spark.read.parquet("/path/")`: the base path will be `/path/`, and the returned * DataFrame will have the column of `something`. * * Users also can override the basePath by setting `basePath` in the options to pass the new base * path to the data source. - * For example, `sqlContext.read.option("basePath", "/path/").parquet("/path/something=true/")`, + * For example, `spark.read.option("basePath", "/path/").parquet("/path/something=true/")`, * and the returned DataFrame will have the column of `something`. */ private def basePaths: Set[Path] = { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala index 07f55042ee..65bc043076 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -2952,8 +2952,8 @@ object functions { * import org.apache.spark.sql._ * * val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value") - * val sqlContext = df.sqlContext - * sqlContext.udf.register("simpleUDF", (v: Int) => v * v) + * val spark = df.sparkSession + * spark.udf.register("simpleUDF", (v: Int) => v * v) * df.select($"id", callUDF("simpleUDF", $"value")) * }}} * |