I have issue with the performance when reading data from HBase in spark streaming. It is taking more than 5 mins just to read data from HBase for 3 records. Below is the logic that I used in mapPartitions.
val messages = KafkaUtils.createDirectStream[String, Array[Byte], StringDecoder, DefaultDecoder](ssc, kafkaParams, topicSet)
messages.mapPartitions(iter => {
val context = TaskContext.get
logger.info((s"Process for partition: ${context.partitionId} "))
val hbaseConf = HBaseConfiguration.create()
//hbaseConf.addResource(new File("/etc/hbase/conf/hbase-site.xml").toURI.toURL)
//val connection: Connection = hbaseConnection.getOrCreateConnection(hbaseConf)
val connection = ConnectionFactory.createConnection(hbaseConf)
val hbaseTable = connection.getTable(TableName.valueOf("prod:CustomerData"))
.......
})
I have used BulkGet. It is taking around 5 seconds to process 90K messages(may be because the API is using HBaseContext and we dont have create any HBaseConnection). But I cannot use this as the output of BulkGet is RDD, and I have to do leftouterjoin to join the RDD of BulkGet with the actual RDD from Kafka. I assume this is not correct approach as it involves the below. Moreover I have to process all the 90K messages in 1 second.
Fetch distinct Cusotmer Id from the RDD read from Kafka before passing it to BulkGet
Also, it involves shuffling as I have to do leftOuterJoin the main RDD (from Kafka) with the BulkGet RDD (I only see the option of join as the BulkGet output is an RDD)
Can anyone please help me what is the issue with performance when I try to create HBaseConnection in mapPartitions. I have also tried setting driver-class-path.
Thanks
Related
I have a Spark Structured Streaming application. The application receives data from kafka, and should use these values as a parameter to process data from a cassandra database. My question is how do I use the data that is in the input dataframe (kafka), as "where" parameters in cassandra "select" without taking the error below:
Exception in thread "main" org.apache.spark.sql.AnalysisException: Queries with streaming sources must be executed with writeStream.start();
This is my df input:
val df = spark
.readStream
.format("kafka")
.options(
Map("kafka.bootstrap.servers"-> kafka_bootstrap,
"subscribe" -> kafka_topic,
"startingOffsets"-> "latest",
"fetchOffset.numRetries"-> "5",
"kafka.group.id"-> groupId
))
.load()
I get this error whenever I try to store the dataframe values in a variable to use as a parameter.
This is the method I created to try to convert the data into variables. With that the spark give the error that I mentioned earlier:
def processData(messageToProcess: DataFrame): DataFrame = {
val messageDS: Dataset[Message] = messageToProcess.as[Message]
val listData: Array[Message] = messageDS.collect()
listData.foreach(x => println(x.country))
val mensagem = messageToProcess
mensagem
}
When you need to use data in Kafka to query data in Cassandra, then such operation is a typical join between two datasets - you don't need to call .collect to find entries, you just do the join. And it's quite typical thing - to enrich data in Kafka with data from the external dataset, and Cassandra provides low-latency operations.
Your code could look as following (you'll need to configure so-called DirectJoin, see link below):
import spark.implicits._
import org.apache.spark.sql.cassandra._
val df = spark.readStream.format("kafka")
.options(Map(...)).load()
... decode data in Kafka into columns
val cassdata = spark.read.cassandraFormat("table", "keyspace").load
val joined = df.join(cassdata, cassdata("pk") === df("some_column"))
val processed = ... process joined data
val query = processed.writeStream.....output data somewhere...start()
query.awaitTermination()
I have detailed blog post on how to perform efficient joins with data in Cassandra.
As the error message suggest, you have to use writeStream.start() in order to execute a Structured Streaming query.
You can't use the same actions you use for batch dataframes (like .collect(), .show() or .count()) on streaming dataframes, see the Unsupported Operations section of the Spark Structured Streaming documentation.
In your case, you are trying to use messageDS.collect() on a streaming dataset, which is not allowed. To achieve this goal you can use a foreachBatch output sink to collect the rows you need at each microbatch:
streamingDF.writeStream.foreachBatch { (microBatchDf: DataFrame, batchId: Long) =>
// Now microBatchDf is no longer a streaming dataframe
// you can check with microBatchDf.isStreaming
val messageDS: Dataset[Message] = microBatchDf.as[Message]
val listData: Array[Message] = messageDS.collect()
listData.foreach(x => println(x.country))
// ...
}
1 streaming and Kafka broker version 0.8.2.1, I have separate servers for spark and kafka on AWS.
Using val directKafkaStream = KafkaUtils.createDirectStream direct approach. StreamingContext(conf, Seconds(300)), I am expecting to get 30 string from streaming but actual receiving only 15-25 in range . Cross check kafka consumer on same topic showing 30 string during 300 seconds. And stream.foreachRDD { rdd => giving 15to 20 strings.
What is wrong behind getting uneventual data. I am using sparksession creating sc and ssc.
Thank You.
add auto.offset.reset to smallest in kafka param
val kafkaParams = Map[String, String](
"auto.offset.reset" -> "smallest", ......)
I am working on a spark application which needs to read data from Kafka. I created a Kafka topic where producer was posting messages. I verified from console consumer that messages were successfully posted .
I wrote a short spark application to read data from Kafka, but it is not getting any data.
Following is the code i used:
def main(args: Array[String]): Unit = {
val Array(zkQuorum, group, topics, numThreads) = args
val sparkConf = new SparkConf().setAppName("SparkConsumer").setMaster("local[2]")
val ssc = new StreamingContext(sparkConf, Seconds(2))
val topicMap = topics.split(",").map((_, numThreads.toInt)).toMap
val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap).map(_._2)
process(lines) // prints the number of records in Kafka topic
ssc.start()
ssc.awaitTermination()
}
private def process(lines: DStream[String]) {
val z = lines.count()
println("count of lines is "+z)
//edit
lines.foreachRDD(rdd => rdd.map(println)
// <-- Why does this **not** print?
)
Any suggestions on how to resolve this issue?
******EDIT****
I have used
lines.foreachRDD(rdd => rdd.map(println)
as well in actual code but that is also not working. I set the retention period as mentioned in post : Kafka spark directStream can not get data . But still the problem exist.
Your process is a continuation of a DStream pipeline with no output operator that gets the pipeline executed every batch interval.
You can "see" it by reading the signature of count operator:
count(): DStream[Long]
Quoting the count's scaladoc:
Returns a new DStream in which each RDD has a single element generated by counting each RDD of this DStream.
So, you have a dstream of Kafka records that you transform to a dstream of single values (being the result of count). Not much to have it outputed (to a console or any other sink).
You have to end the pipeline using an output operator as described in the official documentation Output Operations on DStreams:
Output operations allow DStream’s data to be pushed out to external systems like a database or a file systems. Since the output operations actually allow the transformed data to be consumed by external systems, they trigger the actual execution of all the DStream transformations (similar to actions for RDDs).
(Low-Level) Output operators register input dstreams as output dstreams so the execution can start. Spark Streaming's DStream by design has no notion of being an output dstream. It is DStreamGraph to know and be able to differentiate between input and output dstreams.
I built a SparkStreaming App that fetches content from A Kafka Queue and intends to put the data into a MySQL table after some pre-processing and structuring.
I call the 'foreachRDD' method on the SparkStreamingContext. The issue that I'm facing is that there's dataloss between when I call saveAsTextFile on the RDD and DataFrame's write method with format("csv"). I can't seem to pin point why this is happening.
val ssc = new StreamingContext(spark.sparkContext, Seconds(60))
ssc.checkpoint("checkpoint")
val topicMap = topics.split(",").map((_, numThreads.toInt)).toMap
val stream = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap).map(_._2)
stream.foreachRDD {
rdd => {
rdd.saveAsTextFile("/Users/jarvis/rdds/"+new SimpleDateFormat("hh-mm-ss-dd-MM-yyyy").format(new Date)+"_rdd")
import spark.implicits._
val messagesDF = rdd.map(_.split("\t")).map( w => { Record ( w(0), autoTag( w(1),w(4) ) , w(2), w(3), w(4), w(5).substring(w(5).lastIndexOf("http://")), w(6).split("\n")(0) )}).toDF("recordTS","tag","channel_url","title","description","link","pub_TS")
messagesDF.write.format("csv").save(dumpPath+new SimpleDateFormat("hh-mm-ss-dd-MM-yyyy").format(new Date)+"_DF")
}
}
ssc.start()
ssc.awaitTermination()
There's data loss ie Many rows don't make it to the DataFrame from the RDD.
There's also replication: Many rows that do reach the Dataframe are replicated many times.
Found the error. Actually there was a wrong understanding about the ingested data format.
The intended data was "\t\t\t..." and hence the Row was supposed be split at "\n".
However the actual data was :
"\t\t\t...\n\t\t\t...\n"
So the rdd.map(...) operation needed another map for splitting at every "\n"
Intent
I'm receiving data from Kafka via direct stream and would like to enrich the messages with data from Cassandra. The Kafka messages (Protobufs) are decoded into DataFrames and then joined with a (supposedly pre-filtered) DF from Cassandra. The relation of (Kafka) streaming batch size to raw C* data is [several streaming messages to millions of C* rows], BUT the join always yields exactly ONE result [1:1] per message. After the join the resulting DF is eventually stored to another C* table.
Problem
Even though I'm joining the two DFs on the full Cassandra primary key and pushing the corresponding filter to C*, it seems that Spark is loading the whole C* data-set into memory before actually joining (which I'd like to prevent by using the filter/predicate pushdown). This leads to a lot of shuffling and tasks being spawned, hence the "simple" join takes forever...
def main(args: Array[String]) {
val conf = new SparkConf()
.setAppName("test")
.set("spark.cassandra.connection.host", "xxx")
.set("spark.cassandra.connection.keep_alive_ms", "30000")
.setMaster("local[*]")
val ssc = new StreamingContext(conf, Seconds(10))
ssc.sparkContext.setLogLevel("INFO")
// Initialise Kafka
val kafkaTopics = Set[String]("xxx")
val kafkaParams = Map[String, String](
"metadata.broker.list" -> "xxx:32000,xxx:32000,xxx:32000,xxx:32000",
"auto.offset.reset" -> "smallest")
// Kafka stream
val messages = KafkaUtils.createDirectStream[String, MyMsg, StringDecoder, MyMsgDecoder](ssc, kafkaParams, kafkaTopics)
// Executed on the driver
messages.foreachRDD { rdd =>
// Create an instance of SQLContext
val sqlContext = SQLContextSingleton.getInstance(rdd.sparkContext)
import sqlContext.implicits._
// Map MyMsg RDD
val MyMsgRdd = rdd.map{case (key, MyMsg) => (MyMsg)}
// Convert RDD[MyMsg] to DataFrame
val MyMsgDf = MyMsgRdd.toDF()
.select(
$"prim1Id" as 'prim1_id,
$"prim2Id" as 'prim2_id,
$...
)
// Load DataFrame from C* data-source
val base_data = base_data_df.getInstance(sqlContext)
// Left join on prim1Id and prim2Id
val joinedDf = MyMsgDf.join(base_data,
MyMsgDf("prim1_id") === base_data("prim1_id") &&
MyMsgDf("prim2_id") === base_data("prim2_id"), "left")
.filter(base_data("prim1_id").isin(MyMsgDf("prim1_id"))
&& base_data("prim2_id").isin(MyMsgDf("prim2_id")))
joinedDf.show()
joinedDf.printSchema()
// Select relevant fields
// Persist
}
// Start the computation
ssc.start()
ssc.awaitTermination()
}
Environment
Spark 1.6
Cassandra 2.1.12
Cassandra-Spark-Connector 1.5-RC1
Kafka 0.8.2.2
SOLUTION
From discussions on the DataStax Spark Connector for Apache Cassandra ML
Joining Kafka and Cassandra DataFrames in Spark Streaming ignores C* predicate pushdown
How to create a DF from CassandraJoinRDD
I've learned the following:
Quoting Russell Spitzer
This wouldn't be a case of predicate pushdown. This is a join on a partition key column. Currently only joinWithCassandraTable supports this direct kind of join although we are working on some methods to try to have this automatically done within Spark.
Dataframes can be created from any RDD which can have a schema applied to it. The easiest thing to do is probably to map your joinedRDD[x,y] to Rdd[JoinedCaseClass] and then call toDF (which will require importing your sqlContext implicits.) See the DataFrames documentation here for more info.
So the actual implementation now resembles something like
// Join myMsg RDD with myCassandraTable
val joinedMsgRdd = myMsgRdd.joinWithCassandraTable(
"keyspace",
"myCassandraTable",
AllColumns,
SomeColumns(
"prim1_id",
"prim2_id"
)
).map{case (myMsg, cassandraRow) =>
JoinedMsg(
foo = myMsg.foo
bar = cassandraRow.bar
)
}
// Convert RDD[JoinedMsg] to DataFrame
val myJoinedDf = joinedMsgRdd.toDF()
Have you tried joinWithCassandraTable ? It should pushdown to C* all keys you are looking for...