Spark结构化流到kudu上下文 [英] Spark structured stream to kudu context

问题描述

我想阅读kafka主题，然后通过Spark Streaming将其写入kudu表.

I want to read kafka topic then write it to kudu table by spark streaming.

// sessions and contexts
val conf = new SparkConf().setMaster("local[2]").setAppName("TestMain")
val sparkSession = SparkSession.builder().config(conf).getOrCreate()
val sparkContext = sparkSession.sparkContext
val kuduContext = new KuduContext("...", sparkContext);

// structure
val schema: StructType = StructType(
  StructField("userNo", IntegerType, true) ::
  StructField("bandNo", IntegerType, false) ::
  StructField("ipv4", StringType, false) :: Nil);

// kudu - prepare table
kuduContext.deleteTable("test_table");
kuduContext.createTable("test_table", schema, Seq("userNo"), new CreateTableOptions()
  .setNumReplicas(1)
  .addHashPartitions(List("userNo").asJava, 3))

// get stream from kafka
val parsed = sparkSession
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "...")
  .option("startingOffsets", "latest")
  .option("subscribe", "feed_api_band_get_popular_post_list")
  .load()
  .select(from_json(col("value").cast("string"), schema).alias("parsed_value"))

// write it to kudu
kuduContext.insertRows(parsed.toDF(), "test_table");

现在它抱怨

Exception in thread "main" org.apache.spark.sql.AnalysisException: Queries with streaming sources must be executed with writeStream.start();;
kafka
at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$.org$apache$spark$sql$catalyst$analysis$UnsupportedOperationChecker$$throwError(UnsupportedOperationChecker.scala:297)
at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$$anonfun$checkForBatch$1.apply(UnsupportedOperationChecker.scala:36)

我的第二种方法

似乎我更改了代码以使用传统的KafkaUtils.createDirectStream

My second approach

It seems I change my code to use traditional KafkaUtils.createDirectStream

KafkaUtils.createDirectStream[String, String](
  ssc,
  PreferConsistent,
  Subscribe[String, String](topics, kafkaParams)
).foreachRDD(rdd => {
  rdd.foreach(record => {
    // write to kudu.............
    println(record.value());
  })
});

ssc.start();
ssc.awaitTermination();

那么，哪种方法是正确的?还是有什么方法可以使其从第一种方法运行?

So, which one is right approach? or is there any way to make it run from first approach?

Spark版本是2.2.0.

Spark version is 2.2.0.

推荐答案

两种方法似乎都是正确的.第一个使用Spark结构化流处理方式，其中以表格形式附加数据.第二种方法是通过传统的DStream做事方式

Both the approaches seem right. First one uses the Spark Structured streaming way of doing things wherein the data is appended on a tabular basis. Second method does it via traditional DStream way of doing things

这篇关于Spark结构化流到kudu上下文的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！