我想我今天对你们所有人都有一个有趣的问题。在下面的代码中，您会注意到我有两个SparkContexts用于SparkStreaming，另一个用于普通的SparkContext。根据最佳实践，您应该在Spark应用程序中只有一个SparkContext，即使它可以通过配置中的allowMultipleContexts绕过它。
问题是，我需要从hive和Kafka主题检索数据来做一些逻辑，每当我提交我的应用程序时，它显然会返回“不能在JVM上运行2个Spark上下文”。
我的问题是，有没有一种正确的方法来做到这一点，而不是我现在正在做什么？
public class MainApp {
private final String logFile= Properties.getString("SparkLogFileDir");
private static final String KAFKA_GROUPID = Properties.getString("KafkaGroupId");
private static final String ZOOKEEPER_URL = Properties.getString("ZookeeperURL");
private static final String KAFKA_BROKER = Properties.getString("KafkaBroker");
private static final String KAFKA_TOPIC = Properties.getString("KafkaTopic");
private static final String Database = Properties.getString("HiveDatabase");
private static final Integer KAFKA_PARA = Properties.getInt("KafkaParrallel");
public static void main(String[] args){
//set settings
String sql="";
//START APP
System.out.println("Starting NPI_TWITTERAPP...." + new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
System.out.println("Configuring Settings...."+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
SparkConf conf = new SparkConf()
.setAppName(Properties.getString("SparkAppName"))
.setMaster(Properties.getString("SparkMasterUrl"));
//Set Spark/hive/sql Context
JavaSparkContext sc = new JavaSparkContext(conf);
JavaStreamingContext jssc = new JavaStreamingContext(conf, new Duration(5000));
JavaHiveContext HiveSqlContext = new JavaHiveContext(sc);
//Check if Twitter Hive Table Exists
try {
HiveSqlContext.sql("DROP TABLE IF EXISTS "+Database+"TWITTERSTORE");
HiveSqlContext.sql("CREATE TABLE IF NOT EXISTS "+Database+".TWITTERSTORE "
+" (created_at String, id String, id_str String, text String, source String, truncated String, in_reply_to_user_id String, processed_at String, lon String, lat String)"
+" STORED AS TEXTFILE");
}catch(Exception e){
System.out.println(e);
}
//Check if Ivapp Table Exists
sql ="CREATE TABLE IF NOT EXISTS "+Database+".IVAPPGEO AS SELECT DISTINCT a.LATITUDE, a.LONGITUDE, b.ODNCIRCUIT_OLT_CLLI, b.ODNCIRCUIT_OLT_TID, a.CITY, a.STATE, a.ZIP FROM "
+Database+".T_PONNMS_SERVICE B, "
+Database+".CLLI_LATLON_MSTR A WHERE a.BID_CLLI = substr(b.ODNCIRCUIT_OLT_CLLI,0,8)";
try {
System.out.println(sql + new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
HiveSqlContext.sql(sql);
sql = "SELECT LATITUDE, LONGITUDE, ODNCIRCUIT_OLT_CLLI, ODNCIRCUIT_OLT_TID, CITY, STATE, ZIP FROM "+Database+".IVAPPGEO";
JavaSchemaRDD RDD_IVAPPGEO = HiveSqlContext.sql(sql).cache();
}catch(Exception e){
System.out.println(sql + new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
}
//JavaHiveContext hc = new JavaHiveContext();
System.out.println("Retrieve Data from Kafka Topic: "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
Map<String, Integer> topicMap = new HashMap<String, Integer>();
topicMap.put(KAFKA_TOPIC,KAFKA_PARA);
JavaPairReceiverInputDStream<String, String> messages = KafkaUtils.createStream(
jssc, KAFKA_GROUPID, ZOOKEEPER_URL, topicMap);
JavaDStream<String> json = messages.map(
new Function<Tuple2<String, String>, String>() {
private static final long serialVersionUID = 42l;
@Override
public String call(Tuple2<String, String> message) {
return message._2();
}
}
);
System.out.println("Completed Kafka Messages... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
System.out.println("Filtering Resultset... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
JavaPairDStream<Long, String> tweets = json.mapToPair(
new TwitterFilterFunction());
JavaPairDStream<Long, String> filtered = tweets.filter(
new Function<Tuple2<Long, String>, Boolean>() {
private static final long serialVersionUID = 42l;
@Override
public Boolean call(Tuple2<Long, String> tweet) {
return tweet != null;
}
}
);
JavaDStream<Tuple2<Long, String>> tweetsFiltered = filtered.map(
new TextFilterFunction());
tweetsFiltered = tweetsFiltered.map(
new StemmingFunction());
System.out.println("Finished Filtering Resultset... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
System.out.println("Processing Sentiment Data... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
//calculate postive tweets
JavaPairDStream<Tuple2<Long, String>, Float> positiveTweets =
tweetsFiltered.mapToPair(new PositiveScoreFunction());
//calculate negative tweets
JavaPairDStream<Tuple2<Long, String>, Float> negativeTweets =
tweetsFiltered.mapToPair(new NegativeScoreFunction());
JavaPairDStream<Tuple2<Long, String>, Tuple2<Float, Float>> joined =
positiveTweets.join(negativeTweets);
//Score tweets
JavaDStream<Tuple4<Long, String, Float, Float>> scoredTweets =
joined.map(new Function<Tuple2<Tuple2<Long, String>,
Tuple2<Float, Float>>,
Tuple4<Long, String, Float, Float>>() {
private static final long serialVersionUID = 42l;
@Override
public Tuple4<Long, String, Float, Float> call(
Tuple2<Tuple2<Long, String>, Tuple2<Float, Float>> tweet)
{
return new Tuple4<Long, String, Float, Float>(
tweet._1()._1(),
tweet._1()._2(),
tweet._2()._1(),
tweet._2()._2());
}
});
System.out.println("Finished Processing Sentiment Data... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
System.out.println("Outputting Tweets Data to flat file "+Properties.getString("HdfsOutput")+" ... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
JavaDStream<Tuple5<Long, String, Float, Float, String>> result =
scoredTweets.map(new ScoreTweetsFunction());
result.foreachRDD(new FileWriter());
System.out.println("Outputting Sentiment Data to Hive... "+ new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
jssc.start();
jssc.awaitTermination();
}
}
答案 0 :(得分：1)
创建SparkContext
您可以创建SparkContext实例，无论是否首先创建SparkConf对象。
获取现有或创建新的SparkContext（getOrCreate方法）
getOrCreate(): SparkContext
getOrCreate(conf: SparkConf): SparkContext
SparkContext.getOrCreate方法允许您获取现有的SparkContext或创建一个新的SparkContext。
import org.apache.spark.SparkContext
val sc = SparkContext.getOrCreate()
// Using an explicit SparkConf object
import org.apache.spark.SparkConf
val conf = new SparkConf()
.setMaster("local[*]")
.setAppName("SparkMe App")
val sc = SparkContext.getOrCreate(conf)
请参阅此处 - https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-sparkcontext.html
答案 1 :(得分：0)
显然，如果我在执行JavaStreaming Context之前使用sc.close（）关闭原始的SparkContext，它可以正常工作，没有错误或问题。
答案 2 :(得分：0)
您可以使用单个对象ContextManager来处理要提供的上下文。
public class ContextManager {
private static JavaSparkContext context;
private static String currentType;
private ContextManager() {}
public static JavaSparkContext getContext(String type) {
if(type == currentType && context != null) {
return context;
}
else if (type == "streaming"){
.. clean up the current context ..
.. initialize the context to streaming context ..
currentType = type;
}
else {
..clean up the current context..
... initialize the context to normal context ..
currentType = type;
}
return context;
}
}
在项目中存在一些问题，您可以非常快速地切换上下文，但开销会非常大。
答案 3 :(得分：0)
您可以从JavaStreamingSparkContext访问SparkContext，并在创建其他上下文时使用那个引用。
SparkConf sparkConfig = new SparkConf().setAppName("foo");
JavaStreamingContext jssc = new JavaStreamingContext(sparkConfig, Duration.seconds(30));
SqlContext sqlContext = new SqlContext(jssc.sparkContext());