Spark SQL

落日映苍穹つ 2022-05-14 05:44 168阅读 0赞

教程：

[http://spark.apache.org/docs/latest/sql-programming-guide.html][http_spark.apache.org_docs_latest_sql-programming-guide.html]

Spark SQL是用于结构化数据处理的Spark模块。与基本的Spark RDD API不同，Spark SQL提供的接口为Spark提供了有关数据结构和正在执行的计算的更多信息。Spark SQL的一个用途是执行SQL查询。Spark SQL还可用于从现有Hive安装中读取数据。

### SparkSession ###

Spark中所有功能的入口点都是SparkSession类。要创建基本的SparkSession，只需使用SparkSession.builder()：

SparkSession spark = SparkSession
                    .builder()
                    .appName("Java Spark SQL basic example")
                    .master("local")
                    .config("spark.some.config.option", "some-value")
                    .getOrCreate();

### DataFrame ###

1、RDD、DataFrame、Dataset全都是spark平台下的分布式弹性数据集，为处理超大型数据提供便利

2、RDD不支持sparksql操作

3、DataFrame与Dataset均支持sparksql的操作，比如select，groupby之类，还能注册临时表/视窗，进行sql语句操作

4、在Java API中，用户需要使用Dataset来表示DataFrame

5、DataFrame与Dataset支持一些特别方便的保存方式，比如保存成csv，可以带上表头，这样每一列的字段名一目了然

6、DataFrame也可以叫Dataset\[Row\],每一行的类型是Row，不解析，每一行究竟有哪些字段，各个字段又是什么类型都无从得知，只能用上面提到的getAS方法或者模式匹配拿出特定字段

#### 使用Dataset ####

Dataset<Row> df = spark.read().json("/Users/kexin/work/projects/TestSpark/src/main/resources/people.json");
    df.show();
    df.printSchema();
    List list = df.filter(col("age").gt(18)).select("name","age").collectAsList();
    list.forEach(row->{
        System.out.print(((Row)row).get(0)+"\t");
        System.out.println(((Row)row).get(1));
    });

### DataSet ###

#### 1、直接创建dataset ####

// Create an instance of a Bean class
        Person person = new Person();
        person.setName("Andy");
        person.setAge(32);
        List<Person> l = new ArrayList<>();
        l.add(person);
        Person p = new Person();
        p.setName("tom");
        p.setAge(44);
        l.add(p);
    
        // Encoders are created for Java beans
        Encoder<Person> personEncoder = Encoders.bean(Person.class);
        Dataset<Person> javaBeanDS = spark.createDataset(l, personEncoder);
    
        javaBeanDS.show();
    
        List list = javaBeanDS.filter(col("age").lt(33)).select("name", "age").collectAsList();
        list.forEach(row -> {
            System.out.println(((Row) row).get(0) + "\t" + ((Row) row).get(1));
        });

#### 2、从数据文件中创建 ####

// DataFrames can be converted to a Dataset by providing a class. Mapping based on name
        String path = "/Users/kexin/work/projects/TestSpark/src/main/resources/people.json";
        Encoder<Person> personEncoder = Encoders.bean(Person.class);
        Dataset<Person> peopleDS = spark.read().json(path).as(personEncoder);
        peopleDS.show();

#### 3、与RDD转换 ####

// Create an RDD of Person objects from a text file
        JavaRDD<Person> peopleRDD = spark.read().textFile("/Users/kexin/work/projects/TestSpark/src/main/resources/people.txt")
                .javaRDD().map(line -> {
                    String[] parts = line.split(",");
                    Person person = new Person();
                    person.setName(parts[0]);
                    person.setAge(Integer.parseInt(parts[1].trim()));
                    return person;
                });
        // Apply a schema to an RDD of JavaBeans to get a DataFrame
        Dataset<Row> peopleDF = spark.createDataFrame(peopleRDD, Person.class);
        // Register the DataFrame as a temporary view
        peopleDF.createOrReplaceTempView("people");
    
        // SQL statements can be run by using the sql methods provided by spark
        Dataset<Row> teenagersDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19");
    
        // The columns of a row in the result can be accessed by field index
        Encoder<String> stringEncoder = Encoders.STRING();
        Dataset<String> teenagerNamesByIndexDF = teenagersDF.map(
                (MapFunction<Row, String>) row -> "Name: " + row.getString(0), stringEncoder);
    
        teenagerNamesByIndexDF.show();
        // or by field name
        Dataset<String> teenagerNamesByFieldDF = teenagersDF.map((MapFunction<Row, String>) row -> "Name: " + row.<String>getAs("name"), stringEncoder);
        teenagerNamesByFieldDF.show();

#### 4、使用rdd和schema创建dataframe ####

// Create an RDD
        JavaRDD<String> peopleRDD = spark.sparkContext().textFile("/Users/kexin/work/projects/TestSpark/src/main/resources/people.txt", 1).toJavaRDD();
    
        // The schema is encoded in a string
        String schemaString = "name age";
    
        // Generate the schema based on the string of schema
        List<StructField> fields = new ArrayList<>();
        for (String fieldName : schemaString.split(" ")) {
            StructField field = DataTypes.createStructField(fieldName, DataTypes.StringType, true);
            fields.add(field);
        }
        StructType schema = DataTypes.createStructType(fields);
    
        // Convert records of the RDD (people) to Rows
        JavaRDD<Row> rowRDD = peopleRDD.map((Function<String, Row>) record -> {
            String[] attributes = record.split(",");
            return RowFactory.create(attributes[0], attributes[1].trim());
        });
    
        // Apply the schema to the RDD
        Dataset<Row> peopleDataFrame = spark.createDataFrame(rowRDD, schema);
    
        // Creates a temporary view using the DataFrame
        peopleDataFrame.createOrReplaceTempView("people");
    
        // SQL can be run over a temporary view created using DataFrames
        Dataset<Row> results = spark.sql("SELECT name FROM people");
    
        // The results of SQL queries are DataFrames and support all the normal RDD operations
        // The columns of a row in the result can be accessed by field index or by field name
        Dataset<String> namesDS = results.map((MapFunction<Row, String>) row -> "Name: " + row.getString(0), Encoders.STRING());
        namesDS.show();

### 临时视图 ###

将DataFrame注册为临时视图允许您对其数据运行SQL查询

#### 1、临时视图 ####

可以直接执行sql语句，Spark SQL中的临时视图是会话范围的，如果创建它的会话终止，它将消失。

// Register the DataFrame as a SQL temporary view
    df.createOrReplaceTempView("people");
    Dataset<Row> sqlDF = spark.sql("SELECT * FROM people");
    sqlDF.show();

#### 2、全局临时视图 ####

如果想要实现一个在所有会话之间共享的临时视图并保持活动状态，直到Spark应用程序终止，可以创建一个全局临时视图。全局临时视图与系统保留的数据库绑定global\_temp，需要使用限定名称来引用它，例如SELECT \* FROM global\_temp.view1

df.createGlobalTempView("people");
    // Global temporary view is tied to a system preserved database `global_temp`
    spark.sql("SELECT * FROM global_temp.people").show();
    // Global temporary view is cross-session
    spark.newSession().sql("SELECT * FROM global_temp.people").show();

### 自定义聚合 ###

[http://spark.apache.org/docs/latest/sql-programming-guide.html\#untyped-user-defined-aggregate-functions][http_spark.apache.org_docs_latest_sql-programming-guide.html_untyped-user-defined-aggregate-functions]

[http_spark.apache.org_docs_latest_sql-programming-guide.html]: http://spark.apache.org/docs/latest/sql-programming-guide.html
[http_spark.apache.org_docs_latest_sql-programming-guide.html_untyped-user-defined-aggregate-functions]: http://spark.apache.org/docs/latest/sql-programming-guide.html#untyped-user-defined-aggregate-functions