I am trying to run simple variations of examples from official spark tutorial and a book "spark streaming in action".
The content of exceptions are strange. What is wrong with my code?
First of all I start kafka zookeeper, server, producer and 2 consumers. Then I run following code:
// read from kafka
val df = sparkService.sparkSession
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", topic1)
.load()
// write to kafka
import sparkService.sparkSession.implicits._
val query = df.selectExpr("CAST(key as STRING)", "CAST(value as STRING)")
.writeStream
.outputMode(OutputMode.Append())
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("topic", topic2)
.option("checkpointLocation", "/home/pt/Dokumenty/tmp/")
.option("failOnDataLoss", "false") // only when testing
.start()
query.awaitTermination(30000)
Error occurs on writting to kafka:
Exception in thread "main" org.apache.spark.sql.streaming.StreamingQueryException: Expected e.g. {"topicA":{"0":23,"1":-1},"topicB":{"0":-2}}, got 1
1609627750463
Related
I am not being to stream my data on multiple hdfs location , which is filtered by key. So below code is not working. Please help me to find the correct way to write this code
val ER_stream_V1 = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", configManager.getString("Kafka.Server"))
.option("subscribe", "Topic1")
.option("startingOffsets", "latest")
.option("failOnDataLoss", "false")
.load()
val ER_stream_V2 = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", configManager.getString("Kafka.Server"))
.option("subscribe", "Topic1")
.option("startingOffsets", "latest")
.option("failOnDataLoss", "false")
.load()
ER_stream_V1.toDF()
.select(col("key"), col("value").cast("string"))
.filter(col("key")==="Value1")
.select(functions.from_json(col("value").cast("string"), Value1Schema.schemaExecution).as("value")).select("value.*")
.writeStream
.format("orc")
.option("metastoreUri", configManager.getString("spark.datasource.hive.warehouse.metastoreUri"))
.option("checkpointLocation", "/tmp/teststreaming/execution/checkpoint2005")
.option("path", "/tmp/test/value1")
.trigger(Trigger.ProcessingTime("5 Seconds"))
.partitionBy("jobid")
.start()
ER_stream_V2.toDF()
.select(col("key"), col("value").cast("string"))
.filter(col("key")==="Value2")
.select(functions.from_json(col("value").cast("string"), Value2Schema.schemaJobParameters).as("value"))
.select("value.*")
.writeStream
.format("orc")
.option("metastoreUri", configManager.getString("spark.datasource.hive.warehouse.metastoreUri"))
.option("checkpointLocation", "/tmp/teststreaming/jobparameters/checkpoint2006")
.option("path", "/tmp/test/value2")
.trigger(Trigger.ProcessingTime("5 Seconds"))
.partitionBy("jobid")
.start()
You should not need two readers. Create one and filter twice. You might also want to consider startingOffsets as earliest to read existing topic data
For example.
val ER_stream = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", configManager.getString("Kafka.Server"))
.option("subscribe", "Topic1")
.option("startingOffsets", "latest") // maybe change?
.option("failOnDataLoss", "false")
.load()
.toDF()
.select(col("key").cast("string").as("key"), col("value").cast("string"))
val value1Stream = ER_stream
.filter(col("key") === "Value1")
.select(functions.from_json(col("value"), Value1Schema.schemaExecution).as("value"))
.select("value.*")
val value2Stream = ER_stream
.filter(col("key") === "Value2")
.select(functions.from_json(col("value"), Value2Schema.schemaJobParameters).as("value"))
.select("value.*")
value1Stream.writeStream.format("orc")
...
.start()
value2Stream.writeStream.format("orc")
...
.start()
I am trying to write to streams parallel, but just the last one is working.
Here is my code.
val trump_topic = filteredQueryTrump.select($"ID".as("key"), $"title".as("value"))
.writeStream.format("kafka")
.trigger(Trigger.ProcessingTime("10 seconds"))
.option("kafka.security.protocol", "SASL_SSL")
.option("kafka.sasl.mechanism", "SCRAM-SHA-256")
.option("kafka.sasl.jaas.config", """org.apache.kafka.common.security.scram.ScramLoginModule required username="" password="";""")
.option("kafka.bootstrap.servers", "rocket-01.srvs.cloudkafka.com:9094")
.option("topic", "trump")
.option("checkpointLocation", "/tmp").start()
val biden_topic = filteredQueryBiden.select($"ID".as("key"), $"title".as("value"))
.writeStream.format("kafka")
.trigger(Trigger.ProcessingTime("4 seconds"))
.option("kafka.security.protocol", "SASL_SSL")
.option("kafka.sasl.mechanism", "SCRAM-SHA-256")
.option("kafka.sasl.jaas.config", """org.apache.kafka.common.security.scram.ScramLoginModule required username="" password="";""")
.option("kafka.bootstrap.servers", "rocket-01.srvs.cloudkafka.com:9094")
.option("topic", "biden")
.option("checkpointLocation", "/tmp").start()
I tried spark.streams.awaitAnyTermination() but it didn't help either.
You need to have two different checkpoint locations for each output stream.
Also, do add awaitAnyTermination as you already have tried out to ensure the jobs are running continously.
spark.streams.awaitAnyTermination()
As an alternative you could combine those two queries into one by doing
val combinedDf = filteredQueryTrump.select($"ID".as("key"), $"title".as("value"), lit("trump").as("topic"))
.union(filteredQueryBiden.select($"ID".as("key"), $"title".as("value"), lit("biden").as("topic")))
The content of the column topic will be interpreted by the spark-sql-kafka-0-10 library to decide into which topic the row should be written to. Theoretically, you can use that technique to write to multiple Kafka topics within one stream. When doing this it is important to not use topic option when calling writeStream. However, in that way you will not be able to have two different Trigger durations but only one for the overall stream.
An example you code could look like this:
val outputQuery = combinedDf.select($"key", $"value", $"topic")
.writeStream
.format("kafka")
.trigger(Trigger.ProcessingTime("4 seconds"))
.option("kafka.security.protocol", "SASL_SSL")
.option("kafka.sasl.mechanism", "SCRAM-SHA-256")
.option("kafka.sasl.jaas.config", """org.apache.kafka.common.security.scram.ScramLoginModule required username="" password="";""")
.option("kafka.bootstrap.servers", "rocket-01.srvs.cloudkafka.com:9094")
.option("checkpointLocation", "/tmp")
outputQuery.start()
outputQuery.awaitTermination()
I read lines from a kafka source and I want to build a kafka consumer... in spark structured streaming
I know how to tell spark that the incoming lines are json type... how do I do the same with from_csv ?
val lines = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "topic2")
.option("startingOffsets", "earliest")
.load()
.selectExpr("CAST(value AS STRING)")
.select(functions.from_json($"value", retailDataSchema).as("data"))
lines.printSchema()
The schema is:
val retailDataSchema = new StructType()
.add("InvoiceNo", IntegerType)
.add("Quantity", IntegerType)
.add("Country", StringType)
Thank you!
The input data looks like this:
You could do this work around:
val lines = spark
.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "topic2")
.option("startingOffsets", "earliest")
.load()
.select(col("value").cast("string")).as("data").select("data.*").selectExpr("cast(split(value,',')[0] as DataTypes.IntegerType) as InvoiceNo"
,"cast(split(value,',')[1] as DataTypes.IntegerType) as Quantity"
,"cast(split(value,',')[2] as DataTypes.StringType) as Country" );
lines.printSchema();
Or you could use the built-in function from_csv Since Apache spark 3.0.0
val lines = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "topic2")
.option("startingOffsets", "earliest")
.load()
.selectExpr("CAST(value AS STRING)")
.select(functions.from_csv($"value", retailDataSchema).as("data"))
lines.printSchema()
Apache Spark Docs for from_csv built-in function
In kafka I get new topics dynamically and I have to process it using spark streaming from a specific offset. Is there a possibility to pass the json value from a variable. For example consider the below code
val df = spark
.read
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribePattern", "topic.*")
.option("startingOffsets", """{"topic1":{"0":23,"1":-2},"topic2":{"0":-2}}""")
.load()
In this I want to dynamically update value for startingOffsets... I tried to pass the value in string and called it but it did not work... If I am giving the same value in startingOffsets it is working. How to use a variable in this scenario?
val start_offset= """{"topic1":{"0":23,"1":-2},"topic2":{"0":-2}}"""
val df = spark
.read
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribePattern", "topic.*")
.option("startingOffsets", start_offset)
.load()
java.lang.IllegalArgumentException: Expected e.g. {"topicA":{"0":23,"1":-1},"topicB":{"0":-2}}, got """{"topicA":{"0":23,"1":-1},"topicB":{"0":-2}}"""
def main(args: Array[String]) {
val conf = new SparkConf().setMaster("local[*]").setAppName("ReadSpecificOffsetFromKafka");
val spark = SparkSession.builder().config(conf).getOrCreate();
spark.sparkContext.setLogLevel("error");
import spark.implicits._;
val start_offset = """{"first_topic" : {"0" : 15, "1": -2, "2": 6}}"""
val fromKafka = spark.readStream.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092, localhost:9093")
.option("subscribe", "first_topic")
// .option("startingOffsets", "earliest")
.option("startingOffsets", start_offset)
.load();
val selectedValues = fromKafka.selectExpr("cast(value as string)", "cast(partition as integer)");
selectedValues.writeStream
.format("console")
.outputMode("append")
// .trigger(Trigger.Continuous("3 seconds"))
.start()
.awaitTermination();
}
This is the exact code to fetch specific offset from kafka using spark structured streaming and scala
Looks like your job is check pointing the Kafka offsets onto some
persistent storage. Try cleaning those. and Re run your Job.
Also try renaming your job and running it.
Spark can read the stream via readStream. So try with an offset displayed in the error message to get rid of the error.
spark
.readStream
.format("kafka")
.option("subscribePattern", "topic.*")
I have a Spark Structured Streaming:
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("startingOffsets", "earliest")
.option("endingOffsets", "latest")
.option("subscribe", "topic")
.load()
I want to write data to FileSystem using DataStreamWriter,
val query = df
.writeStream
.outputMode("append")
.format("parquet")
.start("data")
But zero files are getting created in data folder. Only _spark_metadata is getting created.
However, I can see the data on console when format is console:
val query = df
.writeStream
.outputMode("append")
.format("console")
.start()
+--------------------+------------------+------------------+
| time| col1| col2|
+--------------------+------------------+------------------+
|49368-05-11 20:42...|0.9166470338147503|0.5576946794171861|
+--------------------+------------------+------------------+
I cannot understand the reason behind it.
Spark - 2.1.0
I had a similar problem but for different reasons, posting here in case someone has the same issue. When writing your output stream to file in append mode with watermarking, structured streaming has an interesting behavior where it won't actually write any data until a time bucket is older than the watermark time. If you're testing structured streaming and have an hour long water mark, you won't see any output for at least an hour.
I resolved this issue. Actually when I tried to run the Structured Streaming on spark-shell, then it gave an error that endingOffsets are not valid in streaming queries, i.e.,:
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("startingOffsets", "earliest")
.option("endingOffsets", "latest")
.option("subscribe", "topic")
.load()
java.lang.IllegalArgumentException: ending offset not valid in streaming queries
at org.apache.spark.sql.kafka010.KafkaSourceProvider$$anonfun$validateStreamOptions$1.apply(KafkaSourceProvider.scala:374)
at org.apache.spark.sql.kafka010.KafkaSourceProvider$$anonfun$validateStreamOptions$1.apply(KafkaSourceProvider.scala:373)
at scala.Option.map(Option.scala:146)
at org.apache.spark.sql.kafka010.KafkaSourceProvider.validateStreamOptions(KafkaSourceProvider.scala:373)
at org.apache.spark.sql.kafka010.KafkaSourceProvider.sourceSchema(KafkaSourceProvider.scala:60)
at org.apache.spark.sql.execution.datasources.DataSource.sourceSchema(DataSource.scala:199)
at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo$lzycompute(DataSource.scala:87)
at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo(DataSource.scala:87)
at org.apache.spark.sql.execution.streaming.StreamingRelation$.apply(StreamingRelation.scala:30)
at org.apache.spark.sql.streaming.DataStreamReader.load(DataStreamReader.scala:124)
... 48 elided
So, I removed endingOffsets from streaming query.
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("startingOffsets", "earliest")
.option("subscribe", "topic")
.load()
Then I tried to save streaming queries' result in Parquet files, during which I came to know that - checkpoint location must be specified, i.e.,:
val query = df
.writeStream
.outputMode("append")
.format("parquet")
.start("data")
org.apache.spark.sql.AnalysisException: checkpointLocation must be specified either through option("checkpointLocation", ...) or SparkSession.conf.set("spark.sql.streaming.checkpointLocation", ...);
at org.apache.spark.sql.streaming.StreamingQueryManager$$anonfun$3.apply(StreamingQueryManager.scala:207)
at org.apache.spark.sql.streaming.StreamingQueryManager$$anonfun$3.apply(StreamingQueryManager.scala:204)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.streaming.StreamingQueryManager.createQuery(StreamingQueryManager.scala:203)
at org.apache.spark.sql.streaming.StreamingQueryManager.startQuery(StreamingQueryManager.scala:269)
at org.apache.spark.sql.streaming.DataStreamWriter.start(DataStreamWriter.scala:262)
at org.apache.spark.sql.streaming.DataStreamWriter.start(DataStreamWriter.scala:206)
... 48 elided
So, I added checkPointLocation:
val query = df
.writeStream
.outputMode("append")
.format("parquet")
.option("checkpointLocation", "checkpoint")
.start("data")
After doing these modifications, I was able to save streaming queries' results in Parquet files.
But, it is strange that when I ran the same code via sbt application, it didn't threw any errors, but when I ran the same code via spark-shell these errors were thrown. I think Apache Spark should throw these errors when run via sbt/maven app too. It is seems to be a bug to me !