10.spark sql之快速入門

前世此生

Hive&Shark

  隨着大數據時代的來臨，Hadoop風靡一時。爲了使熟悉RDBMS但又不理解MapReduce的技術人員快速進行大數據開發，Hive應運而生。Hive是當時惟一運行在Hadoop上的SQL-on-Hadoop工具。

  可是MapReduce計算過程當中大量的中間磁盤落地過程消耗了大量的I/O，下降的運行效率。爲了提升SQL-on-Hadoop的效率，大量的SQL-on-Hadoop工具開始產生，其中表現較爲突出的是：

• MapR的Drill
• Cloudera的Impala
• Shark

  Shark是伯克利實驗室Spark生態的組件之一，它修改了Hive Driver的內存管理、物理計劃、執行三個模塊，使之能運行在Spark引擎上，從而使得SQL查詢的速度獲得10-100倍的提高。

Shark&Spark SQL

  Shark對於Hive的太多依賴（如採用Hive的語法解析器、查詢優化器等等），制約了Spark的One Stack Rule Them All的既定方針，制約了Spark各個組件的相互集成，因此提出了SparkSQL項目。

  SparkSQL拋棄原有Shark的代碼，汲取了Shark的一些優勢，如內存列存儲（In-Memory Columnar Storage）、Hive兼容性等，從新開發了SparkSQL代碼。因爲擺脫了對Hive的依賴性，SparkSQL不管在數據兼容、性能優化、組件擴展方面都獲得了極大地提高。

• 數據兼容方面

  不但兼容Hive，還能夠從RDD、parquet文件、JSON文件中獲取數據，也支持獲取RDBMS數據以及cassandra等NOSQL數據。

• 性能優化方面

  除了採起In-Memory Columnar Storage、byte-code generation等優化技術外,引進Cost Model對查詢進行動態評估、獲取最佳物理計劃等。

• 組件擴展方面

  不管是SQL的語法解析器、分析器仍是優化器均可以從新定義，進行擴展。

  2014年Shark中止開發，團隊將全部資源放SparkSQL項目上，至此，Shark的發展畫上了句號，但也所以發展出兩條線：SparkSQL和Hive on Spark。

  其中SparkSQL做爲Spark生態的一員繼續發展，而再也不受限於Hive，只是兼容Hive；而Hive on Spark是一個Hive的發展計劃，該計劃將Spark做爲Hive的底層引擎之一，也就是說，Hive將再也不受限於一個引擎，能夠採用Map-Reduce、Tez、Spark等引擎。

簡介

  Spark SQL是一個用於結構化數據處理的模塊。Spark SQL賦予待處理數據一些結構化信息，可使用SQL語句或DataSet API接口與Spark SQL進行交互。

• SQL

  Spark SQL可使用sql讀寫Hive中的數據；也能夠在編程語言中使用sql，返回Dataset/DataFrame結果集。

• DataSets&DataFrames

  Dataset是一個分佈式數據集，它結合了RDD與SparkSQL執行引擎的優勢。Dataset能夠經過JVM對象構造，而後使用算子操做進行處理。Java和Scala都有Dataset API；Python和R自己支持Dataset特性。

  DataFrame是一個二維結構的DataSet，至關於RDBMS中的表。DataFrame能夠有多種方式構造，好比結構化數據文件、hive表、外部數據庫、RDD等。在Scala、Java、Python及R中都有DataFrame API。

DataFrame與DataSet

DataFrame建立及操做

• scala

import org.apache.spark.sql.SparkSession

// 構造SparkSession
val spark = SparkSession
  .builder()
  .appName("Spark SQL basic example")
  .config("spark.some.config.option", "some-value")
  .getOrCreate()

// 建立DataFrame
val df = spark.read.json("examples/src/main/resources/people.json")

// Displays the content of the DataFrame to stdout
df.show()
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+

// DataFrame操做
// This import is needed to use the $-notation
import spark.implicits._
// Print the schema in a tree format
df.printSchema()
// root
// |-- age: long (nullable = true)
// |-- name: string (nullable = true)

// Select only the "name" column
df.select("name").show()
// +-------+
// |   name|
// +-------+
// |Michael|
// |   Andy|
// | Justin|
// +-------+

// Select everybody, but increment the age by 1
df.select($"name", $"age" + 1).show()
// +-------+---------+
// |   name|(age + 1)|
// +-------+---------+
// |Michael|     null|
// |   Andy|       31|
// | Justin|       20|
// +-------+---------+

// Select people older than 21
df.filter($"age" > 21).show()
// +---+----+
// |age|name|
// +---+----+
// | 30|Andy|
// +---+----+

// Count people by age
df.groupBy("age").count().show()
// +----+-----+
// | age|count|
// +----+-----+
// |  19|    1|
// |null|    1|
// |  30|    1|
// +----+-----+

• java

import org.apache.spark.sql.SparkSession;

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

//建立DataFrame
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

Dataset<Row> df = spark.read().json("examples/src/main/resources/people.json");

// Displays the content of the DataFrame to stdout
df.show();
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+

//DataFrame操做
// col("...") is preferable to df.col("...")
import static org.apache.spark.sql.functions.col;

// Print the schema in a tree format
df.printSchema();
// root
// |-- age: long (nullable = true)
// |-- name: string (nullable = true)

// Select only the "name" column
df.select("name").show();
// +-------+
// |   name|
// +-------+
// |Michael|
// |   Andy|
// | Justin|
// +-------+

// Select everybody, but increment the age by 1
df.select(col("name"), col("age").plus(1)).show();
// +-------+---------+
// |   name|(age + 1)|
// +-------+---------+
// |Michael|     null|
// |   Andy|       31|
// | Justin|       20|
// +-------+---------+

// Select people older than 21
df.filter(col("age").gt(21)).show();
// +---+----+
// |age|name|
// +---+----+
// | 30|Andy|
// +---+----+

// Count people by age
df.groupBy("age").count().show();
// +----+-----+
// | age|count|
// +----+-----+
// |  19|    1|
// |null|    1|
// |  30|    1|
// +----+-----+

• python

from pyspark.sql import SparkSession

# 構造SparkSession
spark = SparkSession \
    .builder \
    .appName("Python Spark SQL basic example") \
    .config("spark.some.config.option", "some-value") \
    .getOrCreate()

# 建立DataFrame
# spark is an existing SparkSession
df = spark.read.json("examples/src/main/resources/people.json")
# Displays the content of the DataFrame to stdout
df.show()
# +----+-------+
# | age|   name|
# +----+-------+
# |null|Michael|
# |  30|   Andy|
# |  19| Justin|
# +----+-------+

# DataFrame操做
# spark, df are from the previous example
# Print the schema in a tree format
df.printSchema()
# root
# |-- age: long (nullable = true)
# |-- name: string (nullable = true)

# Select only the "name" column
df.select("name").show()
# +-------+
# |   name|
# +-------+
# |Michael|
# |   Andy|
# | Justin|
# +-------+

# Select everybody, but increment the age by 1
df.select(df['name'], df['age'] + 1).show()
# +-------+---------+
# |   name|(age + 1)|
# +-------+---------+
# |Michael|     null|
# |   Andy|       31|
# | Justin|       20|
# +-------+---------+

# Select people older than 21
df.filter(df['age'] > 21).show()
# +---+----+
# |age|name|
# +---+----+
# | 30|Andy|
# +---+----+

# Count people by age
df.groupBy("age").count().show()
# +----+-----+
# | age|count|
# +----+-----+
# |  19|    1|
# |null|    1|
# |  30|    1|
# +----+-----+

DataSet建立及操做

  Datasets和RDD相似，但使用專門的Encoder編碼器來序列化須要通過網絡傳輸的數據對象，而不用RDD使用的Java序列化或Kryo庫。Encoder編碼器是動態生成的代碼，容許直接執行各類算子操做，而不用反序列化。

• scala

// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,
// you can use custom classes that implement the Product interface
case class Person(name: String, age: Long)

// Encoders are created for case classes
val caseClassDS = Seq(Person("Andy", 32)).toDS()
caseClassDS.show()
// +----+---+
// |name|age|
// +----+---+
// |Andy| 32|
// +----+---+

// Encoders for most common types are automatically provided by importing spark.implicits._
val primitiveDS = Seq(1, 2, 3).toDS()
primitiveDS.map(_ + 1).collect() // Returns: Array(2, 3, 4)

// DataFrames can be converted to a Dataset by providing a class. Mapping will be done by name
val path = "examples/src/main/resources/people.json"
val peopleDS = spark.read.json(path).as[Person]
peopleDS.show()
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+

• java

import java.util.Arrays;
import java.util.Collections;
import java.io.Serializable;

import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.Encoder;
import org.apache.spark.sql.Encoders;

public static class Person implements Serializable {
  private String name;
  private int age;

  public String getName() {
    return name;
  }

  public void setName(String name) {
    this.name = name;
  }

  public int getAge() {
    return age;
  }

  public void setAge(int age) {
    this.age = age;
  }
}

// Create an instance of a Bean class
Person person = new Person();
person.setName("Andy");
person.setAge(32);

// Encoders are created for Java beans
Encoder<Person> personEncoder = Encoders.bean(Person.class);
Dataset<Person> javaBeanDS = spark.createDataset(
  Collections.singletonList(person),
  personEncoder
);
javaBeanDS.show();
// +---+----+
// |age|name|
// +---+----+
// | 32|Andy|
// +---+----+

// Encoders for most common types are provided in class Encoders
Encoder<Integer> integerEncoder = Encoders.INT();
Dataset<Integer> primitiveDS = spark.createDataset(Arrays.asList(1, 2, 3), integerEncoder);
Dataset<Integer> transformedDS = primitiveDS.map(
    (MapFunction<Integer, Integer>) value -> value + 1,
    integerEncoder);
transformedDS.collect(); // Returns [2, 3, 4]

// DataFrames can be converted to a Dataset by providing a class. Mapping based on name
String path = "examples/src/main/resources/people.json";
Dataset<Person> peopleDS = spark.read().json(path).as(personEncoder);
peopleDS.show();
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+

SQL操做

• scala

// Register the DataFrame as a SQL temporary view
df.createOrReplaceTempView("people")
//df.createGlobalTempView("people")

val sqlDF = spark.sql("SELECT * FROM people")
sqlDF.show()
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+

• java

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

// Register the DataFrame as a SQL temporary view
df.createOrReplaceTempView("people");
//df.createGlobalTempView("people")

Dataset<Row> sqlDF = spark.sql("SELECT * FROM people");
sqlDF.show();
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+

• python

# Register the DataFrame as a SQL temporary view
df.createOrReplaceTempView("people")
# df.createGlobalTempView("people")

sqlDF = spark.sql("SELECT * FROM people")
sqlDF.show()
# +----+-------+
# | age|   name|
# +----+-------+
# |null|Michael|
# |  30|   Andy|
# |  19| Justin|
# +----+-------+

---

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