问题
I am trying to use Spark Streaming application in Java. My Spark application reads continuous feed from Hadoop
directory using textFileStream() at interval of each 1 Min.
I need to perform Spark aggregation(group by) operation on incoming DStream. After aggregation, I am joining aggregated DStream<Key, Value1>
with RDD<Key, Value2>
with RDD<Key, Value2>
created from static dataset read by textFile() from hadoop directory.
Problem comes when I enable checkpointing. With empty checkpoint directory, it runs fine. After running 2-3 batches I close it using ctrl+c and run it again. On second run it throws spark exception immediately: "SPARK-5063"
Exception in thread "main" org.apache.spark.SparkException: RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063
Following is the Block of Code of spark application:
private void compute(JavaSparkContext sc, JavaStreamingContext ssc) {
JavaRDD<String> distFile = sc.textFile(MasterFile);
JavaDStream<String> file = ssc.textFileStream(inputDir);
// Read Master file
JavaRDD<MasterParseLog> masterLogLines = distFile.flatMap(EXTRACT_MASTER_LOGLINES);
final JavaPairRDD<String, String> masterRDD = masterLogLines.mapToPair(MASTER_KEY_VALUE_MAPPER);
// Continuous Streaming file
JavaDStream<ParseLog> logLines = file.flatMap(EXTRACT_CKT_LOGLINES);
// calculate the sum of required field and generate group sum RDD
JavaPairDStream<String, Summary> sumRDD = logLines.mapToPair(CKT_GRP_MAPPER);
JavaPairDStream<String, Summary> grpSumRDD = sumRDD.reduceByKey(CKT_GRP_SUM);
//GROUP BY Operation
JavaPairDStream<String, Summary> grpAvgRDD = grpSumRDD.mapToPair(CKT_GRP_AVG);
// Join Master RDD with the DStream //This is the block causing error (without it code is working fine)
JavaPairDStream<String, Tuple2<String, String>> joinedStream = grpAvgRDD.transformToPair(
new Function2<JavaPairRDD<String, String>, Time, JavaPairRDD<String, Tuple2<String, String>>>() {
private static final long serialVersionUID = 1L;
public JavaPairRDD<String, Tuple2<String, String>> call(
JavaPairRDD<String, String> rdd, Time v2) throws Exception {
return masterRDD.value().join(rdd);
}
}
);
joinedStream.print(10);
}
public static void main(String[] args) {
JavaStreamingContextFactory contextFactory = new JavaStreamingContextFactory() {
public JavaStreamingContext create() {
// Create the context with a 60 second batch size
SparkConf sparkConf = new SparkConf();
final JavaSparkContext sc = new JavaSparkContext(sparkConf);
JavaStreamingContext ssc1 = new JavaStreamingContext(sc, Durations.seconds(duration));
app.compute(sc, ssc1);
ssc1.checkpoint(checkPointDir);
return ssc1;
}
};
JavaStreamingContext ssc = JavaStreamingContext.getOrCreate(checkPointDir, contextFactory);
// start the streaming server
ssc.start();
logger.info("Streaming server started...");
// wait for the computations to finish
ssc.awaitTermination();
logger.info("Streaming server stopped...");
}
I know that block of code which joins static dataset with DStream is causing error, But that is taken from spark-streaming page of Apache spark website (sub heading "stream-dataset join" under "Join Operations"). Please help me to get it working even if there is different way of doing it. I need to enable checkpointing in my streaming application.
Environment Details:
- Centos6.5 :2 node Cluster
- Java :1.8
- Spark :1.4.1
- Hadoop :2.7.1*
来源:https://stackoverflow.com/questions/32378296/spark-checkpointing-error-when-joining-static-dataset-with-dstream