[SPARK-16734][EXAMPLES][SQL] Revise examples of all language bindings
## What changes were proposed in this pull request? This PR makes various minor updates to examples of all language bindings to make sure they are consistent with each other. Some typos and missing parts (JDBC example in Scala/Java/Python) are also fixed. ## How was this patch tested? Manually tested. Author: Cheng Lian <lian@databricks.com> Closes #14368 from liancheng/revise-examples.
This commit is contained in:
Cheng Lian
Wenchen Fan
parent
5184df06b3
commit
10e1c0e638
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@ -132,7 +132,7 @@ from a Hive table, or from [Spark data sources](#data-sources).
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As an example, the following creates a DataFrame based on the content of a JSON file:
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{% include_example create_DataFrames r/RSparkSQLExample.R %}
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{% include_example create_df r/RSparkSQLExample.R %}
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</div>
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</div>
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@ -180,7 +180,7 @@ In addition to simple column references and expressions, DataFrames also have a
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<div data-lang="r" markdown="1">
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{% include_example dataframe_operations r/RSparkSQLExample.R %}
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{% include_example untyped_ops r/RSparkSQLExample.R %}
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For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/R/index.html).
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@ -214,7 +214,7 @@ The `sql` function on a `SparkSession` enables applications to run SQL queries p
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<div data-lang="r" markdown="1">
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The `sql` function enables applications to run SQL queries programmatically and returns the result as a `SparkDataFrame`.
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{% include_example sql_query r/RSparkSQLExample.R %}
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{% include_example run_sql r/RSparkSQLExample.R %}
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</div>
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</div>
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@ -377,7 +377,7 @@ In the simplest form, the default data source (`parquet` unless otherwise config
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<div data-lang="r" markdown="1">
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{% include_example source_parquet r/RSparkSQLExample.R %}
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{% include_example generic_load_save_functions r/RSparkSQLExample.R %}
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</div>
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</div>
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@ -400,13 +400,11 @@ using this syntax.
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</div>
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<div data-lang="python" markdown="1">
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{% include_example manual_load_options python/sql/datasource.py %}
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</div>
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<div data-lang="r" markdown="1">
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{% include_example source_json r/RSparkSQLExample.R %}
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{% include_example manual_load_options r/RSparkSQLExample.R %}
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</div>
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</div>
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@ -425,13 +423,11 @@ file directly with SQL.
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</div>
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<div data-lang="python" markdown="1">
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{% include_example direct_sql python/sql/datasource.py %}
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</div>
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<div data-lang="r" markdown="1">
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{% include_example direct_query r/RSparkSQLExample.R %}
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{% include_example direct_sql r/RSparkSQLExample.R %}
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</div>
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</div>
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@ -523,7 +519,7 @@ Using the data from the above example:
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<div data-lang="r" markdown="1">
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{% include_example load_programmatically r/RSparkSQLExample.R %}
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{% include_example basic_parquet_example r/RSparkSQLExample.R %}
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</div>
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@ -839,7 +835,7 @@ Note that the file that is offered as _a json file_ is not a typical JSON file.
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line must contain a separate, self-contained valid JSON object. As a consequence,
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a regular multi-line JSON file will most often fail.
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{% include_example load_json_file r/RSparkSQLExample.R %}
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{% include_example json_dataset r/RSparkSQLExample.R %}
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</div>
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@ -925,7 +921,7 @@ You may need to grant write privilege to the user who starts the spark applicati
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When working with Hive one must instantiate `SparkSession` with Hive support. This
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adds support for finding tables in the MetaStore and writing queries using HiveQL.
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{% include_example hive_table r/RSparkSQLExample.R %}
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{% include_example spark_hive r/RSparkSQLExample.R %}
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</div>
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</div>
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@ -1067,43 +1063,19 @@ the Data Sources API. The following options are supported:
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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{% highlight scala %}
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val jdbcDF = spark.read.format("jdbc").options(
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Map("url" -> "jdbc:postgresql:dbserver",
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"dbtable" -> "schema.tablename")).load()
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{% endhighlight %}
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{% include_example jdbc_dataset scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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{% highlight java %}
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Map<String, String> options = new HashMap<>();
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options.put("url", "jdbc:postgresql:dbserver");
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options.put("dbtable", "schema.tablename");
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Dataset<Row> jdbcDF = spark.read().format("jdbc"). options(options).load();
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{% endhighlight %}
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{% include_example jdbc_dataset java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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{% highlight python %}
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df = spark.read.format('jdbc').options(url='jdbc:postgresql:dbserver', dbtable='schema.tablename').load()
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{% endhighlight %}
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{% include_example jdbc_dataset python/sql/datasource.py %}
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</div>
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<div data-lang="r" markdown="1">
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{% include_example jdbc r/RSparkSQLExample.R %}
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{% include_example jdbc_dataset r/RSparkSQLExample.R %}
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</div>
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<div data-lang="sql" markdown="1">
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@ -25,7 +25,6 @@ import java.util.List;
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// $example on:basic_parquet_example$
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import org.apache.spark.api.java.function.MapFunction;
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import org.apache.spark.sql.Encoders;
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// import org.apache.spark.sql.Encoders;
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// $example on:schema_merging$
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// $example on:json_dataset$
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import org.apache.spark.sql.Dataset;
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@ -92,7 +91,7 @@ public class JavaSQLDataSourceExample {
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public static void main(String[] args) {
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SparkSession spark = SparkSession
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.builder()
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.appName("Java Spark SQL Data Sources Example")
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.appName("Java Spark SQL data sources example")
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.config("spark.some.config.option", "some-value")
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.getOrCreate();
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@ -100,6 +99,7 @@ public class JavaSQLDataSourceExample {
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runBasicParquetExample(spark);
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runParquetSchemaMergingExample(spark);
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runJsonDatasetExample(spark);
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runJdbcDatasetExample(spark);
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spark.stop();
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}
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@ -183,10 +183,10 @@ public class JavaSQLDataSourceExample {
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// The final schema consists of all 3 columns in the Parquet files together
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// with the partitioning column appeared in the partition directory paths
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// root
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// |-- value: int (nullable = true)
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// |-- square: int (nullable = true)
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// |-- cube: int (nullable = true)
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// |-- key : int (nullable = true)
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// |-- value: int (nullable = true)
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// |-- square: int (nullable = true)
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// |-- cube: int (nullable = true)
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// |-- key: int (nullable = true)
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// $example off:schema_merging$
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}
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@ -216,4 +216,15 @@ public class JavaSQLDataSourceExample {
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// $example off:json_dataset$
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}
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private static void runJdbcDatasetExample(SparkSession spark) {
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// $example on:jdbc_dataset$
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Dataset<Row> jdbcDF = spark.read()
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.format("jdbc")
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.option("url", "jdbc:postgresql:dbserver")
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.option("dbtable", "schema.tablename")
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.option("user", "username")
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.option("password", "password")
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.load();
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// $example off:jdbc_dataset$
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}
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}
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@ -88,7 +88,7 @@ public class JavaSparkSQLExample {
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// $example on:init_session$
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SparkSession spark = SparkSession
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.builder()
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.appName("Java Spark SQL Example")
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.appName("Java Spark SQL basic example")
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.config("spark.some.config.option", "some-value")
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.getOrCreate();
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// $example off:init_session$
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@ -182,7 +182,7 @@ if __name__ == "__main__":
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# $example on:init_session$
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spark = SparkSession \
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.builder \
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.appName("PythonSQL") \
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.appName("Python Spark SQL basic example") \
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.config("spark.some.config.option", "some-value") \
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.getOrCreate()
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# $example off:init_session$
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@ -92,14 +92,14 @@ def parquet_schema_merging_example(spark):
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# The final schema consists of all 3 columns in the Parquet files together
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# with the partitioning column appeared in the partition directory paths.
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# root
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# |-- double: long (nullable = true)
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# |-- single: long (nullable = true)
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# |-- triple: long (nullable = true)
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# |-- key: integer (nullable = true)
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# |-- double: long (nullable = true)
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# |-- single: long (nullable = true)
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# |-- triple: long (nullable = true)
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# |-- key: integer (nullable = true)
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# $example off:schema_merging$
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def json_dataset_examplg(spark):
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def json_dataset_example(spark):
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# $example on:json_dataset$
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# spark is from the previous example.
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sc = spark.sparkContext
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@ -112,8 +112,8 @@ def json_dataset_examplg(spark):
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# The inferred schema can be visualized using the printSchema() method
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peopleDF.printSchema()
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# root
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# |-- age: long (nullable = true)
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# |-- name: string (nullable = true)
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# |-- age: long (nullable = true)
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# |-- name: string (nullable = true)
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# Creates a temporary view using the DataFrame
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peopleDF.createOrReplaceTempView("people")
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# +---------------+----+
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# $example off:json_dataset$
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def jdbc_dataset_example(spark):
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# $example on:jdbc_dataset$
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jdbcDF = spark.read \
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.format("jdbc") \
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.option("url", "jdbc:postgresql:dbserver") \
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.option("dbtable", "schema.tablename") \
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.option("user", "username") \
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.option("password", "password") \
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.load()
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# $example off:jdbc_dataset$
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if __name__ == "__main__":
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spark = SparkSession \
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.builder \
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.appName("PythonSQL") \
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.appName("Python Spark SQL data source example") \
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.getOrCreate()
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basic_datasource_example(spark)
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parquet_example(spark)
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parquet_schema_merging_example(spark)
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json_dataset_examplg(spark)
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json_dataset_example(spark)
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jdbc_dataset_example(spark)
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spark.stop()
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@ -38,7 +38,7 @@ if __name__ == "__main__":
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spark = SparkSession \
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.builder \
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.appName("PythonSQL") \
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.appName("Python Spark SQL Hive integration example") \
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.config("spark.sql.warehouse.dir", warehouse_location) \
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.enableHiveSupport() \
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.getOrCreate()
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@ -18,31 +18,43 @@
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library(SparkR)
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# $example on:init_session$
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sparkR.session(appName = "MyApp", sparkConfig = list(spark.executor.memory = "1g"))
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sparkR.session(appName = "MyApp", sparkConfig = list(spark.some.config.option = "some-value"))
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# $example off:init_session$
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# $example on:create_DataFrames$
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# $example on:create_df$
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df <- read.json("examples/src/main/resources/people.json")
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# Displays the content of the DataFrame
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head(df)
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## age name
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## 1 NA Michael
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## 2 30 Andy
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## 3 19 Justin
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# Another method to print the first few rows and optionally truncate the printing of long values
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showDF(df)
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# $example off:create_DataFrames$
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## +----+-------+
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## | age| name|
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## +----+-------+
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## |null|Michael|
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## | 30| Andy|
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## | 19| Justin|
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## +----+-------+
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## $example off:create_df$
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# $example on:dataframe_operations$
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# $example on:untyped_ops$
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# Create the DataFrame
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df <- read.json("examples/src/main/resources/people.json")
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# Show the content of the DataFrame
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head(df)
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## age name
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## null Michael
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## 30 Andy
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## 19 Justin
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## age name
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## 1 NA Michael
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## 2 30 Andy
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## 3 19 Justin
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# Print the schema in a tree format
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printSchema(df)
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@ -52,58 +64,58 @@ printSchema(df)
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# Select only the "name" column
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head(select(df, "name"))
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## name
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## Michael
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## Andy
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## Justin
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## name
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## 1 Michael
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## 2 Andy
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## 3 Justin
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# Select everybody, but increment the age by 1
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head(select(df, df$name, df$age + 1))
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## name (age + 1)
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## Michael null
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## Andy 31
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## Justin 20
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## name (age + 1.0)
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## 1 Michael NA
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## 2 Andy 31
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## 3 Justin 20
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# Select people older than 21
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head(where(df, df$age > 21))
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## age name
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## 30 Andy
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## age name
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## 1 30 Andy
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# Count people by age
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head(count(groupBy(df, "age")))
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## age count
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## null 1
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## 19 1
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## 30 1
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# $example off:dataframe_operations$
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## age count
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## 1 19 1
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## 2 NA 1
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## 3 30 1
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# $example off:untyped_ops$
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# Register this DataFrame as a table.
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createOrReplaceTempView(df, "table")
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# $example on:sql_query$
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# $example on:run_sql$
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df <- sql("SELECT * FROM table")
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# $example off:sql_query$
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# $example off:run_sql$
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# $example on:source_parquet$
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# $example on:generic_load_save_functions$
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df <- read.df("examples/src/main/resources/users.parquet")
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write.df(select(df, "name", "favorite_color"), "namesAndFavColors.parquet")
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# $example off:source_parquet$
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# $example off:generic_load_save_functions$
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# $example on:source_json$
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# $example on:manual_load_options$
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df <- read.df("examples/src/main/resources/people.json", "json")
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namesAndAges <- select(df, "name", "age")
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write.df(namesAndAges, "namesAndAges.parquet", "parquet")
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# $example off:source_json$
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# $example off:manual_load_options$
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# $example on:direct_query$
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# $example on:direct_sql$
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df <- sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")
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# $example off:direct_query$
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# $example off:direct_sql$
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# $example on:load_programmatically$
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# $example on:basic_parquet_example$
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df <- read.df("examples/src/main/resources/people.json", "json")
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# SparkDataFrame can be saved as Parquet files, maintaining the schema information.
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@ -117,7 +129,7 @@ parquetFile <- read.parquet("people.parquet")
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createOrReplaceTempView(parquetFile, "parquetFile")
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teenagers <- sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
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head(teenagers)
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## name
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## name
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## 1 Justin
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# We can also run custom R-UDFs on Spark DataFrames. Here we prefix all the names with "Name:"
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@ -129,7 +141,7 @@ for (teenName in collect(teenNames)$name) {
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## Name: Michael
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## Name: Andy
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## Name: Justin
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# $example off:load_programmatically$
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# $example off:basic_parquet_example$
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# $example on:schema_merging$
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@ -146,18 +158,17 @@ write.df(df2, "data/test_table/key=2", "parquet", "overwrite")
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# Read the partitioned table
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df3 <- read.df("data/test_table", "parquet", mergeSchema = "true")
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printSchema(df3)
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# The final schema consists of all 3 columns in the Parquet files together
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# with the partitioning column appeared in the partition directory paths.
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# root
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# |-- single: double (nullable = true)
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# |-- double: double (nullable = true)
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# |-- triple: double (nullable = true)
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# |-- key : int (nullable = true)
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# with the partitioning column appeared in the partition directory paths
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## root
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## |-- single: double (nullable = true)
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## |-- double: double (nullable = true)
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## |-- triple: double (nullable = true)
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## |-- key: integer (nullable = true)
|
||||
# $example off:schema_merging$
|
||||
|
||||
|
||||
# $example on:load_json_file$
|
||||
# $example on:json_dataset$
|
||||
# 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"
|
||||
|
|
@ -166,9 +177,9 @@ people <- read.json(path)
|
|||
|
||||
# The inferred schema can be visualized using the printSchema() method.
|
||||
printSchema(people)
|
||||
# root
|
||||
# |-- age: long (nullable = true)
|
||||
# |-- name: string (nullable = true)
|
||||
## root
|
||||
## |-- age: long (nullable = true)
|
||||
## |-- name: string (nullable = true)
|
||||
|
||||
# Register this DataFrame as a table.
|
||||
createOrReplaceTempView(people, "people")
|
||||
|
|
@ -176,12 +187,12 @@ createOrReplaceTempView(people, "people")
|
|||
# SQL statements can be run by using the sql methods.
|
||||
teenagers <- sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
|
||||
head(teenagers)
|
||||
## name
|
||||
## name
|
||||
## 1 Justin
|
||||
# $example off:load_json_file$
|
||||
# $example off:json_dataset$
|
||||
|
||||
|
||||
# $example on:hive_table$
|
||||
# $example on:spark_hive$
|
||||
# enableHiveSupport defaults to TRUE
|
||||
sparkR.session(enableHiveSupport = TRUE)
|
||||
sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
|
||||
|
|
@ -189,12 +200,12 @@ sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src
|
|||
|
||||
# Queries can be expressed in HiveQL.
|
||||
results <- collect(sql("FROM src SELECT key, value"))
|
||||
# $example off:hive_table$
|
||||
# $example off:spark_hive$
|
||||
|
||||
|
||||
# $example on:jdbc$
|
||||
# $example on:jdbc_dataset$
|
||||
df <- read.jdbc("jdbc:postgresql:dbserver", "schema.tablename", user = "username", password = "password")
|
||||
# $example off:jdbc$
|
||||
# $example off:jdbc_dataset$
|
||||
|
||||
# Stop the SparkSession now
|
||||
sparkR.session.stop()
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ object SQLDataSourceExample {
|
|||
def main(args: Array[String]) {
|
||||
val spark = SparkSession
|
||||
.builder()
|
||||
.appName("Spark SQL Data Soures Example")
|
||||
.appName("Spark SQL data sources example")
|
||||
.config("spark.some.config.option", "some-value")
|
||||
.getOrCreate()
|
||||
|
||||
|
|
@ -33,6 +33,7 @@ object SQLDataSourceExample {
|
|||
runBasicParquetExample(spark)
|
||||
runParquetSchemaMergingExample(spark)
|
||||
runJsonDatasetExample(spark)
|
||||
runJdbcDatasetExample(spark)
|
||||
|
||||
spark.stop()
|
||||
}
|
||||
|
|
@ -99,10 +100,10 @@ object SQLDataSourceExample {
|
|||
// The final schema consists of all 3 columns in the Parquet files together
|
||||
// with the partitioning column appeared in the partition directory paths
|
||||
// root
|
||||
// |-- value: int (nullable = true)
|
||||
// |-- square: int (nullable = true)
|
||||
// |-- cube: int (nullable = true)
|
||||
// |-- key : int (nullable = true)
|
||||
// |-- value: int (nullable = true)
|
||||
// |-- square: int (nullable = true)
|
||||
// |-- cube: int (nullable = true)
|
||||
// |-- key: int (nullable = true)
|
||||
// $example off:schema_merging$
|
||||
}
|
||||
|
||||
|
|
@ -145,4 +146,15 @@ object SQLDataSourceExample {
|
|||
// $example off:json_dataset$
|
||||
}
|
||||
|
||||
private def runJdbcDatasetExample(spark: SparkSession): Unit = {
|
||||
// $example on:jdbc_dataset$
|
||||
val jdbcDF = spark.read
|
||||
.format("jdbc")
|
||||
.option("url", "jdbc:postgresql:dbserver")
|
||||
.option("dbtable", "schema.tablename")
|
||||
.option("user", "username")
|
||||
.option("password", "password")
|
||||
.load()
|
||||
// $example off:jdbc_dataset$
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -42,7 +42,7 @@ object SparkSQLExample {
|
|||
// $example on:init_session$
|
||||
val spark = SparkSession
|
||||
.builder()
|
||||
.appName("Spark SQL Example")
|
||||
.appName("Spark SQL basic example")
|
||||
.config("spark.some.config.option", "some-value")
|
||||
.getOrCreate()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue