I've a Spark application that receives data in a dataframe:
Dataset<Row> df = spark.readStream().format("kafka").option("kafka.bootstrap.servers", "localhost:9092").option("subscribe", "topic").load().selectExpr("CAST(key AS STRING) as key");
String my_key = df.select("key").first().toString();
if (my_key == "a")
{
do_stuff
}
Basically I will need to in case of value a then I apply some transformations on the dataframe otherwise I apply other transformations.
However, I am dealing with streaming queries and when I tried to apply my code above I got:
Queries with streaming sources must be executed with writeStream.start()
The error happens when I make the first operation.
Anyone have any ideas?
Thanks in advance :)
I was able to sole my problem using:
Dataset<Row> df = spark.readStream().format("kafka").option("kafka.bootstrap.servers", "localhost:9092").option("subscribe", "topic").load().selectExpr("CAST(key AS STRING) as key").filter(functions.col("key").contains("a"));
Related
I have a problem similar to one in
Spark java.lang.NullPointerException Error when filter spark data frame on inside foreach iterator
String_Lines.foreachRDD{line ->
line.foreach{x ->
// JSON to DF Example
val sparkConfig = SparkConf().setAppName("JavaKinesisWordCountASL").setMaster("local[*]").
set("spark.sql.warehouse.dir", "file:///C:/tmp")
val spark = SparkSession.builder().config(sparkConfig).orCreate
val outer_jsonData = Arrays.asList(x)
val outer_anotherPeopleDataset = spark.createDataset(outer_jsonData, Encoders.STRING())
spark.read().json(outer_anotherPeopleDataset).createOrReplaceTempView("jsonInnerView")
spark.sql("select name, address.city, address.state from jsonInnerView").show(false)
println("Current String #"+ x)
}
}
#thebluephantom did explain it to the point. I have my code in foreachRDD now, but still it doesn't work. This is Kotlin and I am running it in my local laptop with IntelliJ. Somehow it's not picking sparksession as I understand after reading all blogs. If I delete "spark.read and spark.sql", everything else works OK. What should I do to fix this?
If I delete "spark.read and spark.sql", everything else works OK
If you delete those, you're not actually making Spark do anything, only defining what Spark actions should happen (Spark actions are lazy)
Somehow it's not picking sparksession as I understand
It's "picking it up" just fine. The error is happening because it's picking up a brand new SparkSession. You should already have defined one of these outside of the forEachRDD method, but if you try to reuse it, you might run into different issues
Assuming String_Lines is already a Dataframe. There's no point in looping over all of its RDD data and trying to create brand new SparkSession. Or if it's a DStream, convert it to Streaming Dataframe instead...
That being said, you should be able to immediately select data from it
// unclear what the schema of this is
val selected = String_Lines.selectExpr("name", "address.city", "address.state")
selected.show(false)
You may need to add a get_json_object function in there if you're trying to parse strings to JSON
I am able to solve it finally.
I modified code like this.... Its clean and working.
This is String_Lines data type
val String_Lines: JavaDStream<String>
String_Lines.foreachRDD { x ->
val df = spark.read().json(x)
df.printSchema()
df.show(2,false)
}
Thanks,
Chandra
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))
// ...
}
I use Spark 2.3.2.
I'm receiving data from Kafka. I must do multiple aggregations on the same data. Then all aggregations results will go to the same database (columns or tables may be changed). For example:
val kafkaSource = spark.readStream.option("kafka") ...
val agg1 = kafkaSource.groupBy().agg ...
val agg2 = kafkaSource.groupBy().mapgroupswithstate() ...
val agg3 = kafkaSource.groupBy().mapgroupswithstate() ...
But when I try call writeStream for each aggregation result:
aggr1.writeStream().foreach().start()
aggr2.writeStream().foreach().start()
aggr3.writeStream().foreach().start()
Spark receives data independently in each writeStream. Is this way efficient?
Can I do multiple aggregations with one writeStream? If it is possible, this way is efficient?
Every “writestream” operation results in a new streaming query. Every streaming query will read from the source and execute the entire query plan. Unlike DStream, there is no cache/persist option available.
In spark 2.4, a new API “forEachBatch” has been introduced to solve these kind of scenarios in a more efficient manner.
Caching can be used to avoid multiple reads:
kafkaSource.writeStream.foreachBatch((df, id) => {
df.persist()
val agg1 = df.groupBy().agg ...
val agg2 = df.groupBy().mapgroupswithstate() ...
val agg3 = df.groupBy().mapgroupswithstate() ...
df.unpersist()
}).start()
How can I store a dataframe value to a scala variable ?
I need to store values from the below dataframe (assuming column "timestamp" producing same values) to a variable and later I need to use this variable somewhere
i have tried following
val spark =SparkSession.builder().appName("micro").
enableHiveSupport().config("hive.exec.dynamic.partition", "true").
config("hive.exec.dynamic.partition.mode", "nonstrict").
config("spark.sql.streaming.checkpointLocation", "hdfs://dff/apps/hive/warehouse/area.db").
getOrCreate()
val xmlSchema = new StructType().add("id", "string").add("time_xml", "string")
val xmlData = spark.readStream.option("sep", ",").schema(xmlSchema).csv("file:///home/shp/sourcexml")
val xmlDf_temp = xmlData.select($"id",unix_timestamp($"time_xml", "dd/mm/yyyy HH:mm:ss").cast(TimestampType).as("timestamp"))
val collect_time = xmlDf_temp.select($"timestamp").as[String].collect()(0)
its thorwing error saying following:
org.apache.spark.sql.AnalysisException: Queries with streaming sources must be executed with writeStream.start()
Is there any way i can store some dataframe values to a variable and use later?
is there any way i can store some dataframe values to a variable and use later ?
That's not possible in Spark Structured Streaming since a streaming query never ends and so it is not possible to express collect.
and later I need to use this variable somewhere
This "later" has to be another streaming query that you could join together and produce a result.
How to do stateless aggregations in spark using Structured Streaming 2.3.0 without using flatMapsGroupWithState or Dstream API? looking for a more declarative way
Example:
select count(*) from some_view
I want the output to just count whatever records are available in each batch but not aggregate from the previous batch
To do stateless aggregations in spark using Structured Streaming 2.3.0 without using flatMapsGroupWithState or Dstream API, you can use following code-
import spark.implicits._
def countValues = (_: String, it: Iterator[(String, String)]) => it.length
val query =
dataStream
.select(lit("a").as("newKey"), col("value"))
.as[(String, String)]
.groupByKey { case(newKey, _) => newKey }
.mapGroups[Int](countValues)
.writeStream
.format("console")
.start()
Here what we are doing is-
We added one column to our datastream - newKey. We did this so that we can do a groupBy over it, using groupByKey. I have used a literal string "a", but you can use anything. Also, you need to select anyone column from the available columns in datastream. I have selected value column for this purpose, you can select anyone.
We created a mapping function - countValues, to count the values aggregated by groupByKey function by writing it.length.
So, in this way, we can count whatever records are available in each batch but not aggregating from the previous batch.
I hope it helps!