整合Kafka
官網介紹
http://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html
●Creating a Kafka Source for Streaming Queries
// Subscribe to 1 topic
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
// Subscribe to multiple topics(多個topic)
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1,topic2")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
// Subscribe to a pattern(訂閱通配符topic)
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribePattern", "topic.*")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
●Creating a Kafka Source for Batch Queries(kafka批處理查詢)
// Subscribe to 1 topic
//defaults to the earliest and latest offsets(默認爲最早和最新偏移)
val df = spark
.read
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1")
.load()df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
// Subscribe to multiple topics, (多個topic)
//specifying explicit Kafka offsets(指定明確的偏移量)
val df = spark
.read
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1,topic2")
.option("startingOffsets", """{"topic1":{"0":23,"1":-2},"topic2":{"0":-2}}""")
.option("endingOffsets", """{"topic1":{"0":50,"1":-1},"topic2":{"0":-1}}""")
.load()df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
// Subscribe to a pattern, (訂閱通配符topic)at the earliest and latest offsets
val df = spark
.read
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribePattern", "topic.*")
.option("startingOffsets", "earliest")
.option("endingOffsets", "latest")
.load()df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
●注意:讀取後的數據的Schema是固定的,包含的列如下:
Column |
Type |
說明 |
key |
binary |
消息的key |
value |
binary |
消息的value |
topic |
string |
主題 |
partition |
int |
分區 |
offset |
long |
偏移量 |
timestamp |
long |
時間戳 |
timestampType |
int |
類型 |
●注意:下面的參數是不能被設置的,否則kafka會拋出異常:
- group.id:kafka的source會在每次query的時候自定創建唯一的group id
- auto.offset.reset :爲了避免每次手動設置startingoffsets的值,structured streaming在內部消費時會自動管理offset。這樣就能保證訂閱動態的topic時不會丟失數據。startingOffsets在流處理時,只會作用於第一次啓動時,之後的處理都會自動的讀取保存的offset。
- key.deserializer,value.deserializer,key.serializer,value.serializer 序列化與反序列化,都是ByteArraySerializer
- enable.auto.commit:Kafka源不支持提交任何偏移量
上代碼演示!!!
package cn.itcast.structedstreaming
import org.apache.spark.SparkContext
import org.apache.spark.sql.streaming.Trigger
import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
object KafkaStructuredStreamingDemo {
def main(args: Array[String]): Unit = {
//1.創建SparkSession
val spark: SparkSession =
SparkSession.builder().master("local[*]").appName("SparkSQL").getOrCreate()
val sc: SparkContext = spark.sparkContext
sc.setLogLevel("WARN")
import spark.implicits._
//2.連接Kafka消費數據
val dataDF: DataFrame = spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "node01:9092")
.option("subscribe", "spark_kafka")
.load()
//3.處理數據
//注意:StructuredStreaming整合Kafka獲取到的數據都是字節類型,所以需要按照官網要求,
//轉成自己的實際類型
val dataDS: Dataset[String] = dataDF.selectExpr("CAST(value AS STRING)").as[String]
val wordDS: Dataset[String] = dataDS.flatMap(_.split(" "))
val result: Dataset[Row] = wordDS.groupBy("value").count().sort($"count".desc)
result.writeStream
.format("console")
.outputMode("complete")
.trigger(Trigger.ProcessingTime(0))
.option("truncate",false)//超過長度的列不截斷顯示,即完全顯示
.start()
.awaitTermination()
}
}
整合MySQL
簡介
●需求
我們開發中經常需要將流的運算結果輸出到外部數據庫,例如MySQL中,但是比較遺憾Structured Streaming API不支持外部數據庫作爲接收器
如果將來加入支持的話,它的API將會非常的簡單比如:
format("jdbc").option("url","jdbc:mysql://...").start()
但是目前我們只能自己自定義一個JdbcSink,繼承ForeachWriter並實現其方法
上代碼演示!!!
package cn.itcast.structedstreaming
import java.sql.{Connection, DriverManager, PreparedStatement}
import org.apache.spark.SparkContext
import org.apache.spark.sql._
import org.apache.spark.sql.streaming.Trigger
object JDBCSinkDemo {
def main(args: Array[String]): Unit = {
//1.創建SparkSession
val spark: SparkSession =
SparkSession.builder().master("local[*]").appName("SparkSQL").getOrCreate()
val sc: SparkContext = spark.sparkContext
sc.setLogLevel("WARN")
import spark.implicits._
//2.連接Kafka消費數據
val dataDF: DataFrame = spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "node01:9092")
.option("subscribe", "spark_kafka")
.load()
//3.處理數據
//注意:StructuredStreaming整合Kafka獲取到的數據都是字節類型,所以需要按照官網要求,轉成自己的實際類型
val dataDS: Dataset[String] = dataDF.selectExpr("CAST(value AS STRING)").as[String]
val wordDS: Dataset[String] = dataDS.flatMap(_.split(" "))
val result: Dataset[Row] = wordDS.groupBy("value").count().sort($"count".desc)
val writer = new JDBCSink("jdbc:mysql://localhost:3306/bigdata?characterEncoding=UTF-8", "root", "root")
result.writeStream
.foreach(writer)
.outputMode("complete")
.trigger(Trigger.ProcessingTime(0))
.start()
.awaitTermination()
}
class JDBCSink(url:String,username:String,password:String) extends ForeachWriter[Row] with Serializable{
var connection:Connection = _ //_表示佔位符,後面會給變量賦值
var preparedStatement: PreparedStatement = _
//開啓連接
override def open(partitionId: Long, version: Long): Boolean = {
connection = DriverManager.getConnection(url, username, password)
true
}
/*
CREATE TABLE `t_word` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`word` varchar(255) NOT NULL,
`count` int(11) DEFAULT NULL,
PRIMARY KEY (`id`),
UNIQUE KEY `word` (`word`)
) ENGINE=InnoDB AUTO_INCREMENT=26 DEFAULT CHARSET=utf8;
*/
//replace INTO `bigdata`.`t_word` (`id`, `word`, `count`) VALUES (NULL, NULL, NULL);
//處理數據--存到MySQL
override def process(row: Row): Unit = {
val word: String = row.get(0).toString
val count: String = row.get(1).toString
println(word+":"+count)
//REPLACE INTO:表示如果表中沒有數據這插入,如果有數據則替換
//注意:REPLACE INTO要求表有主鍵或唯一索引
val sql = "REPLACE INTO `t_word` (`id`, `word`, `count`) VALUES (NULL, ?, ?);"
preparedStatement = connection.prepareStatement(sql)
preparedStatement.setString(1,word)
preparedStatement.setInt(2,Integer.parseInt(count))
preparedStatement.executeUpdate()
}
//關閉資源
override def close(errorOrNull: Throwable): Unit = {
if (connection != null){
connection.close()
}
if(preparedStatement != null){
preparedStatement.close()
}
}
}
}