diff options
Diffstat (limited to 'docs')
-rw-r--r-- | docs/sql-programming-guide.md | 126 |
1 files changed, 66 insertions, 60 deletions
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 2ea7572c60..282ea75e1e 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -109,7 +109,7 @@ As an example, the following creates a `DataFrame` based on the content of a JSO val sc: SparkContext // An existing SparkContext. val sqlContext = new org.apache.spark.sql.SQLContext(sc) -val df = sqlContext.jsonFile("examples/src/main/resources/people.json") +val df = sqlContext.read.json("examples/src/main/resources/people.json") // Displays the content of the DataFrame to stdout df.show() @@ -122,7 +122,7 @@ df.show() JavaSparkContext sc = ...; // An existing JavaSparkContext. SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc); -DataFrame df = sqlContext.jsonFile("examples/src/main/resources/people.json"); +DataFrame df = sqlContext.read().json("examples/src/main/resources/people.json"); // Displays the content of the DataFrame to stdout df.show(); @@ -135,7 +135,7 @@ df.show(); from pyspark.sql import SQLContext sqlContext = SQLContext(sc) -df = sqlContext.jsonFile("examples/src/main/resources/people.json") +df = sqlContext.read.json("examples/src/main/resources/people.json") # Displays the content of the DataFrame to stdout df.show() @@ -171,7 +171,7 @@ val sc: SparkContext // An existing SparkContext. val sqlContext = new org.apache.spark.sql.SQLContext(sc) // Create the DataFrame -val df = sqlContext.jsonFile("examples/src/main/resources/people.json") +val df = sqlContext.read.json("examples/src/main/resources/people.json") // Show the content of the DataFrame df.show() @@ -221,7 +221,7 @@ JavaSparkContext sc // An existing SparkContext. SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc) // Create the DataFrame -DataFrame df = sqlContext.jsonFile("examples/src/main/resources/people.json"); +DataFrame df = sqlContext.read().json("examples/src/main/resources/people.json"); // Show the content of the DataFrame df.show(); @@ -277,7 +277,7 @@ from pyspark.sql import SQLContext sqlContext = SQLContext(sc) # Create the DataFrame -df = sqlContext.jsonFile("examples/src/main/resources/people.json") +df = sqlContext.read.json("examples/src/main/resources/people.json") # Show the content of the DataFrame df.show() @@ -777,8 +777,8 @@ In the simplest form, the default data source (`parquet` unless otherwise config <div data-lang="scala" markdown="1"> {% highlight scala %} -val df = sqlContext.load("examples/src/main/resources/users.parquet") -df.select("name", "favorite_color").save("namesAndFavColors.parquet") +val df = sqlContext.read.load("examples/src/main/resources/users.parquet") +df.select("name", "favorite_color").write.save("namesAndFavColors.parquet") {% endhighlight %} </div> @@ -787,8 +787,8 @@ df.select("name", "favorite_color").save("namesAndFavColors.parquet") {% highlight java %} -DataFrame df = sqlContext.load("examples/src/main/resources/users.parquet"); -df.select("name", "favorite_color").save("namesAndFavColors.parquet"); +DataFrame df = sqlContext.read().load("examples/src/main/resources/users.parquet"); +df.select("name", "favorite_color").write().save("namesAndFavColors.parquet"); {% endhighlight %} @@ -798,8 +798,8 @@ df.select("name", "favorite_color").save("namesAndFavColors.parquet"); {% highlight python %} -df = sqlContext.load("examples/src/main/resources/users.parquet") -df.select("name", "favorite_color").save("namesAndFavColors.parquet") +df = sqlContext.read.load("examples/src/main/resources/users.parquet") +df.select("name", "favorite_color").write.save("namesAndFavColors.parquet") {% endhighlight %} @@ -827,8 +827,8 @@ using this syntax. <div data-lang="scala" markdown="1"> {% highlight scala %} -val df = sqlContext.load("examples/src/main/resources/people.json", "json") -df.select("name", "age").save("namesAndAges.parquet", "parquet") +val df = sqlContext.read.format("json").load("examples/src/main/resources/people.json") +df.select("name", "age").write.format("json").save("namesAndAges.parquet") {% endhighlight %} </div> @@ -837,8 +837,8 @@ df.select("name", "age").save("namesAndAges.parquet", "parquet") {% highlight java %} -DataFrame df = sqlContext.load("examples/src/main/resources/people.json", "json"); -df.select("name", "age").save("namesAndAges.parquet", "parquet"); +DataFrame df = sqlContext.read().format("json").load("examples/src/main/resources/people.json"); +df.select("name", "age").write().format("parquet").save("namesAndAges.parquet"); {% endhighlight %} @@ -848,8 +848,8 @@ df.select("name", "age").save("namesAndAges.parquet", "parquet"); {% highlight python %} -df = sqlContext.load("examples/src/main/resources/people.json", "json") -df.select("name", "age").save("namesAndAges.parquet", "parquet") +df = sqlContext.read.load("examples/src/main/resources/people.json", format="json") +df.select("name", "age").write.save("namesAndAges.parquet", format="parquet") {% endhighlight %} @@ -947,11 +947,11 @@ import sqlContext.implicits._ val people: RDD[Person] = ... // An RDD of case class objects, from the previous example. // The RDD is implicitly converted to a DataFrame by implicits, allowing it to be stored using Parquet. -people.saveAsParquetFile("people.parquet") +people.write.parquet("people.parquet") // Read in the parquet file created above. Parquet files are self-describing so the schema is preserved. // The result of loading a Parquet file is also a DataFrame. -val parquetFile = sqlContext.parquetFile("people.parquet") +val parquetFile = sqlContext.read.parquet("people.parquet") //Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile") @@ -969,11 +969,11 @@ teenagers.map(t => "Name: " + t(0)).collect().foreach(println) DataFrame schemaPeople = ... // The DataFrame from the previous example. // DataFrames can be saved as Parquet files, maintaining the schema information. -schemaPeople.saveAsParquetFile("people.parquet"); +schemaPeople.write().parquet("people.parquet"); // Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved. // The result of loading a parquet file is also a DataFrame. -DataFrame parquetFile = sqlContext.parquetFile("people.parquet"); +DataFrame parquetFile = sqlContext.read().parquet("people.parquet"); //Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile"); @@ -995,11 +995,11 @@ List<String> teenagerNames = teenagers.javaRDD().map(new Function<Row, String>() schemaPeople # The DataFrame from the previous example. # DataFrames can be saved as Parquet files, maintaining the schema information. -schemaPeople.saveAsParquetFile("people.parquet") +schemaPeople.read.parquet("people.parquet") # Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved. # The result of loading a parquet file is also a DataFrame. -parquetFile = sqlContext.parquetFile("people.parquet") +parquetFile = sqlContext.write.parquet("people.parquet") # Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile"); @@ -1087,9 +1087,9 @@ path {% endhighlight %} -By passing `path/to/table` to either `SQLContext.parquetFile` or `SQLContext.load`, Spark SQL will -automatically extract the partitioning information from the paths. Now the schema of the returned -DataFrame becomes: +By passing `path/to/table` to either `SQLContext.read.parquet` or `SQLContext.read.load`, Spark SQL +will automatically extract the partitioning information from the paths. +Now the schema of the returned DataFrame becomes: {% highlight text %} @@ -1122,15 +1122,15 @@ import sqlContext.implicits._ // Create a simple DataFrame, stored into a partition directory val df1 = sparkContext.makeRDD(1 to 5).map(i => (i, i * 2)).toDF("single", "double") -df1.saveAsParquetFile("data/test_table/key=1") +df1.write.parquet("data/test_table/key=1") // Create another DataFrame in a new partition directory, // adding a new column and dropping an existing column val df2 = sparkContext.makeRDD(6 to 10).map(i => (i, i * 3)).toDF("single", "triple") -df2.saveAsParquetFile("data/test_table/key=2") +df2.write.parquet("data/test_table/key=2") // Read the partitioned table -val df3 = sqlContext.parquetFile("data/test_table") +val df3 = sqlContext.read.parquet("data/test_table") df3.printSchema() // The final schema consists of all 3 columns in the Parquet files together @@ -1269,12 +1269,10 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext` <div data-lang="scala" markdown="1"> Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. -This conversion can be done using one of two methods in a `SQLContext`: +This conversion can be done using `SQLContext.read.json()` on either an RDD of String, +or a JSON file. -* `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object. -* `jsonRDD` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object. - -Note that the file that is offered as _jsonFile_ is not a typical JSON file. Each +Note that the file that is offered as _a json file_ is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail. @@ -1285,8 +1283,7 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc) // A JSON dataset is pointed to by path. // The path can be either a single text file or a directory storing text files. val path = "examples/src/main/resources/people.json" -// Create a DataFrame from the file(s) pointed to by path -val people = sqlContext.jsonFile(path) +val people = sqlContext.read.json(path) // The inferred schema can be visualized using the printSchema() method. people.printSchema() @@ -1304,19 +1301,17 @@ val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age // an RDD[String] storing one JSON object per string. val anotherPeopleRDD = sc.parallelize( """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil) -val anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD) +val anotherPeople = sqlContext.read.json(anotherPeopleRDD) {% endhighlight %} </div> <div data-lang="java" markdown="1"> Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. -This conversion can be done using one of two methods in a `SQLContext` : - -* `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object. -* `jsonRDD` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object. +This conversion can be done using `SQLContext.read().json()` on either an RDD of String, +or a JSON file. -Note that the file that is offered as _jsonFile_ is not a typical JSON file. Each +Note that the file that is offered as _a json file_ is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail. @@ -1326,9 +1321,7 @@ SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc); // A JSON dataset is pointed to by path. // The path can be either a single text file or a directory storing text files. -String path = "examples/src/main/resources/people.json"; -// Create a DataFrame from the file(s) pointed to by path -DataFrame people = sqlContext.jsonFile(path); +DataFrame people = sqlContext.read().json("examples/src/main/resources/people.json"); // The inferred schema can be visualized using the printSchema() method. people.printSchema(); @@ -1347,18 +1340,15 @@ DataFrame teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AN List<String> jsonData = Arrays.asList( "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}"); JavaRDD<String> anotherPeopleRDD = sc.parallelize(jsonData); -DataFrame anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD); +DataFrame anotherPeople = sqlContext.read().json(anotherPeopleRDD); {% endhighlight %} </div> <div data-lang="python" markdown="1"> Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. -This conversion can be done using one of two methods in a `SQLContext`: - -* `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object. -* `jsonRDD` - loads data from an existing RDD where each element of the RDD is a string containing a JSON object. +This conversion can be done using `SQLContext.read.json` on a JSON file. -Note that the file that is offered as _jsonFile_ is not a typical JSON file. Each +Note that the file that is offered as _a json file_ is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail. @@ -1369,9 +1359,7 @@ sqlContext = SQLContext(sc) # A JSON dataset is pointed to by path. # The path can be either a single text file or a directory storing text files. -path = "examples/src/main/resources/people.json" -# Create a DataFrame from the file(s) pointed to by path -people = sqlContext.jsonFile(path) +people = sqlContext.read.json("examples/src/main/resources/people.json") # The inferred schema can be visualized using the printSchema() method. people.printSchema() @@ -1394,12 +1382,11 @@ anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD) </div> <div data-lang="r" markdown="1"> -Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. -This conversion can be done using one of two methods in a `SQLContext`: - -* `jsonFile` - loads data from a directory of JSON files where each line of the files is a JSON object. +Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. using +the `jsonFile` function, which loads data from a directory of JSON files where each line of the +files is a JSON object. -Note that the file that is offered as _jsonFile_ is not a typical JSON file. Each +Note that the file that is offered as _a json file_ is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail. @@ -1883,6 +1870,25 @@ options. ## Upgrading from Spark SQL 1.3 to 1.4 +#### DataFrame data reader/writer interface + +Based on user feedback, we created a new, more fluid API for reading data in (`SQLContext.read`) +and writing data out (`DataFrame.write`), +and deprecated the old APIs (e.g. `SQLContext.parquetFile`, `SQLContext.jsonFile`). + +See the API docs for `SQLContext.read` ( + <a href="api/scala/index.html#org.apache.spark.sql.SQLContext@read:DataFrameReader">Scala</a>, + <a href="api/java/org/apache/spark/sql/SQLContext.html#read()">Java</a>, + <a href="api/python/pyspark.sql.html#pyspark.sql.SQLContext.read">Python</a> +) and `DataFrame.write` ( + <a href="api/scala/index.html#org.apache.spark.sql.DataFrame@write:DataFrameWriter">Scala</a>, + <a href="api/java/org/apache/spark/sql/DataFrame.html#write()">Java</a>, + <a href="api/python/pyspark.sql.html#pyspark.sql.DataFrame.write">Python</a> +) more information. + + +#### DataFrame.groupBy retains grouping columns + Based on user feedback, we changed the default behavior of `DataFrame.groupBy().agg()` to retain the grouping columns in the resulting `DataFrame`. To keep the behavior in 1.3, set `spark.sql.retainGroupColumns` to `false`. <div class="codetabs"> |