I have the following stream dataframe.
+----------------------------------
|______value______________________|
| I am going to school 😀 |
| why are you crying 🙁 😞 |
| You are not very good my friend |
I have created the above dataframe using below code
val readStream = existingSparkSession
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", hostAddress)
.option("failOnDataLoss", false)
.option("subscribe", "myTopic.raw")
.load()
I want to store the same stream dataframe into spark dataframe. is that possible to convert so in scala and spark? because at the end I want to convert the spark dataframe into a list of sentences. Issue with stream dataframe is i am unable to convert it directly into a list that I can iterate and do some data processing actions.
You should be able to do many of standard operations on the stream that you're getting from Kafka, but you need to take into account the differences in semantics between batch and streaming processing - refer to the Spark docs for that.
Also, when you're getting data from Kafka, the set of columns is fixed, and you get a binary payload that you need to cast the value column to string, or something like this (see docs):
val df = readStream.select($"value".cast("string").alias("sentences"))
after that you'll get a dataframe with actual payload, and start processing. Depending on the complexity of processing, you may need to revert to the foreachBatch functionality, but that may not be necessary - you need to provide more details on what kind of processing you need to do.
Related
I am using Kafka and Spark 2.4.5 Structured Streaming.I am doing the average operation.but i am facing issues due to getting duplicate records from the Kafka topic in a current batch.
For example ,Kafka topic message received on 1st batch batch on update mode
car,Brand=Honda,speed=110,1588569015000000000
car,Brand=ford,speed=90,1588569015000000000
car,Brand=Honda,speed=80,15885690150000000000
here the result is average on car brand per timestamp
i.e groupby on 1588569015000000000 and Brand=Honda , the result we got
110+90/2 = 100
now second message received late data with the duplicate message with same timestamp
car,Brand=Honda,speed=50,1588569015000000000
car,Brand=Honda,speed=50,1588569015000000000
i am expecting average should update to 110+90+50/3 = 83.33
but result update to 110+90+50+50/4=75,which is wrong
val rawDataStream: DataFrame = spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", bootstrapServers)
.option("subscribe", "topic1") // Both topics on same stream!
.option("startingOffsets", "latest")
.load()
.selectExpr("CAST(value AS STRING) as data")
group by timestamp and brand
write to kafka with checkpoint
How to use Spark Structured Streaming to do this or anything wrong on code?
Spark Structured Streaming allows deduplication on a streaming dataframe using dropDuplicates. You would need to specify fields to identify a duplicate record and across batches, spark will consider only the first record per combination and records with duplicate values will be discarded.
Below snippet will deduplicate your streaming dataframe on Brand, Speed and timestamp combination.
rawDataStream.dropDuplicates("Brand", "speed", "timestamp")
Refer to spark documentation here
I am using twitter stream function which gives a stream. I am required to use Spark writeStream function like:writeStream function link
// Write key-value data from a DataFrame to a specific Kafka topic specified in an option
val ds = df
.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.writeStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("topic", "topic1")
.start()
The 'df' needs to be a streaming Dataset/DataFrame. If df is a normal DataFrame, it will give error showing 'writeStream' can be called only on streaming Dataset/DataFrame;
I have already done:
1. get stream from twitter
2. filter and map it to get a tag for each twitt (Positive, Negative, Natural)
The last step is to groupBy tag and count for each and pass it to Kafka.
Do you guys have any idea how to transform a Dstream into a streaming Dataset/DataFrame?
Edited: ForeachRDD function does change Dstream to normal DataFrame.
But 'writeStream' can be called only on streaming
Dataset/DataFrame. (writeStream link is provided above)
org.apache.spark.sql.AnalysisException: 'writeStream' can be called only on streaming Dataset/DataFrame;
how to transform a Dstream into a streaming Dataset/DataFrame?
DStream is an abstraction of a series of RDDs.
A streaming Dataset is an "abstraction" of a series of Datasets (I use quotes since the difference between streaming and batch Datasets is a property isStreaming of Dataset).
It is possible to convert a DStream to a streaming Dataset to keep the behaviour of the DStream.
I think you don't really want it though.
All you need is to take tweets using DStream and save them to a Kafka topic (and you think you need Structured Streaming). I think you simply need Spark SQL (the underlying engine of Structured Streaming).
A pseudo-code would then be as follows (sorry it's been a longer while since I used the old-fashioned Spark Streaming):
val spark: SparkSession = ...
val tweets = DStream...
tweets.foreachRDD { rdd =>
import spark.implicits._
rdd.toDF.write.format("kafka")...
}
I have a dataframe which reads streams from kafka as a source and it is then converted to a dataset after applying schema, now how to get that particular field value from the dataset to work with it?
case class Fruitdata(id:Int, name:String, color:String, price:Int)
//say this function reads streams from kafka and gives me the dataframe
val df = readFromKafka(sparkSession,inputTopic)
//say this converts dataframe to a dataset with schema defined accordingly
val ds: Dataset[Fruitdata] = getDataSet[Fruitdata](df,schema)
//and say the incoming stream data is -
//"{"id":1,"name":"Grapes","color":"Green","price":15}"
//Now how to get a particular field like name, price and so on
//this doesn't works, it says "Queries with streaming sources must be executed with writeStream.start()"
ds.first()
//same here
ds.show
//also can i get the complete string as input,this gives me Dataset[String]
val ds2 = ds.flatMap((f: Fruitdata)=>List(s"${f.id},${f.name}"))
I think it's because you're trying to read from kafka.
When you run with Spark streaming, I think you cannot run few of the commands as they are related to streaming sources. For example, if you are reading from kafka, there is nothing like first, because it is a micro batch and first refers to each micro batch. Please, try something like "console" sink to output your records to console. Also make sure to read few sample records and not real kafka feed.
Some backstory: For a homework project for university we are tasked to implement an algorithm of choice in a scalable way. We chose to use Scala, Spark, MongoDB and Kafka as these were recommended during the course. To read data from our MongoDB, we opted to use MongoSpark as it allows for easy and scalable operations on data. We also use Kafka to simulate streaming from an outside source. We need to perform multiple operations on every entry that Kafka produces. The issue comes from saving the result of this data back to MongoDB.
We have the following code:
val streamDF = sparkSession
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "aTopic")
.load
.selectExpr("CAST(value AS STRING)")
From here on, we're at a loss. We cannot use a .map as MongoSpark only operates on DataFrames, Datasets and RDDs and is not serializable, and using MongoSpark.save does not work on streaming DataFrames like the one specified. We also cannot use the default MongoDB Scala driver as this conflicts with MongoSpark upon adding the dependency. Note that the rest of the algorithm heavily relies on joins and groupbys.
How can we get the data from here to our MongoDB?
Edit:
For an easy to reproduce example, one could try the following:
val streamDF = sparkSession
.readStream
.format("rate")
.load
Adding a .write to that, which is required for MongoSpark.save, will cause an exception because write cannot be called on a streaming DataFrame.
Adding a .write to that, which is required for MongoSpark.save, will cause an exception because write cannot be called on a streaming DataFrame.
The save() method for MongoDB Connector for Spark accepts RDD (as of current version 2.2). When utilising DStream with MongoSpark, you need to fetch the 'batches' of RDDs in the stream to write.
wordCounts.foreachRDD({ rdd =>
import spark.implicits._
val wordCounts = rdd.map({ case (word: String, count: Int)
=> WordCount(word, count) }).toDF()
wordCounts.write.mode("append").mongo()
})
See also:
Design Patterns for using foreachRDD
MongoDB: Spark Streaming
I am developing a spark streaming job(using structured streaming not using DStreams). I get a message from kafka and that will contain many fields with comma separated value out of which the first column will be a filename. Now based on that filename i will have to read the file from HDFS and create a dataframe and operate further on the same. This seems to be simple, but spark is not allowing me to run any actions before the start is called. Spark Documentation also quotes the same.
In addition, there are some Dataset methods that will not work on
streaming Datasets. They are actions that will immediately run queries
and return results, which does not make sense on a streaming Dataset.
Below is what i have tried.
object StructuredStreamingExample {
case class filenameonly(value:String)
def main(args:Array[String])
{
val spark = SparkSession.builder.appName("StructuredNetworkWordCount").master("local[*]").getOrCreate()
spark.sqlContext.setConf("spark.sql.shuffle.partitions", "5")
import spark.implicits._
val lines = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "strtest")
.load()
val values=lines.selectExpr("CAST(value AS STRING)").as[String]
val filename = values.map(x => x.split(",")(0)).toDF().select($"value")
//Here how do i convert the filename which is a Dataframe to string and apply that to spark.readtextfile(filename)
datareadfromhdfs
.writeStream
.trigger(ProcessingTime("10 seconds"))
.outputMode("append")
.format("console")
.start()
.awaitTermination()
Now in the above code after i get the filename which is a Dataframe how do i convert that to a String so that i can do spark.readtextfile(filename) to read the file in HDFS.
I'm not sure it's the best use for spark streaming but in a case like this, I would call filename.foreachRDD and read hdfs files from in there and do whatever you need after.
(Keep in mind that when running inside a foreachRDD, you cannot use global spark session but need to getOrCreate it from the builder like that: val sparkSession = SparkSession.builder.config(myCurrentForeachRDD.sparkContext.getConf).getOrCreate())
You seems to rely on a stream to tell you where to look and load files.. Have you tried simply using a file stream on that folder and let spark monitor and read new files automatically for you?
It is sure not the best use case to use spark structured streaming. If you understand the spark structured streaming correctly all the data transformations/aggregations should happen on the query that generates result table. However you can still implement some workarounds where you can write the code to read data from HDFS in (falt)mapWithGroupState. But, again it is not advisable to do so.