I need to execute some functions based on the values that I receive from topics. I'm currently using ForeachWriter to convert all the topics to a List. Now, I want to pass this List as a parameter to the methods.
This is what I have so far
def doA(mylist: List[String]) = { //something for A }
def doB(mylist: List[String]) = { //something for B }
Ans this is how I call my streaming queries
//{"s":"a","v":"2"}
//{"s":"b","v":"3"}
val readTopics = spark.readStream.format("kafka").option("kafka.bootstrap.servers", "localhost:9092").option("subscribe", "myTopic").load()
val schema = new StructType()
.add("s",StringType)
.add("v",StringType)
val parseStringDF = readTopics.selectExpr("CAST(value AS STRING)")
val parseDF = parseStringDF.select(from_json(col("value"), schema).as("data"))
.select("data.*")
parseDF.writeStream
.format("console")
.outputMode("append")
.start()
//fails here
val listOfTopics = parseDF.select("s").map(row => (row.getString(0))).collect.toList
//unable to call the below methods
for (t <- listOfTopics ){
if(t == "a")
doA(listOfTopics)
else if (t == "b")
doB(listOfTopics)
else
println("do nothing")
}
spark.streams.awaitAnyTermination()
Questions:
If you want to be able to collect data to a local Spark driver/executor, you need to use parseDF.write.foreachBatch
, ie using a ForEachWriter
It's unclear what you need the SparkSession for within your two methods, but since they are working on non-Spark datatypes, you probably shouldn't be using a SparkSession instance, anyway
Alternatively, you should .select()
and filter your topic column, then apply the functions to two "topic-a" and "topic-b" dataframes, thus parallelizing the workload. Otherwise, you would be better off just using regular KafkaConsumer
from kafka-clients
or kafka-streams
rather than Spark
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.