**1. Iam using Apache-pulsar to my kubernetes cluster.when i produce normal text messages , those are consumed by consumer properly. But some how when i produce json data , these data was not consumed by consumer . Is there a way to produce and consume json data as well ?
**
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I am looking to connect Azure data bricks to Event hub and read it through splunk . Initially I was able to send a test message and was able to receive the events in splunk(It was possible using scala as per https://learn.microsoft.com/en-us/azure/databricks/scenarios/databricks-stream-from-eventhubs --> Send tweets to event hubs). Now I am trying to implement the same using python using the reference from https://learn.microsoft.com/en-us/azure/event-hubs/event-hubs-python-get-started-send ---> Send Event . When I try to pass the object-parameters, it throws an error like unable to import from Azure Event hubs
Can anyone help me understand how can I connect Azure databricks with Azure Eventhubs and include sending object -parameters ?
PS : I have added the necessary libraries required to the cluster as below :
com.microsoft.azure:azure-eventhubs-spark_2.12:2.3.18
azure-eventhubs-spark_2.11
com.microsoft.azure:azure-eventhubs-spark_2.11:2.3.22
org.apache.spark:spark-avro_2.12:3.1.1
I have checked it with different versions of libraries too.
Can someone help me with the syntax part on how to pass the object parameters in the format of key value pairs ?
It's simple to connect to EventHubs from Azure Databricks - just follow official documentation, specifically the section Writing Data to Event Hubs (example is for batch write):
writeConnectionString = "SharedAccessSignatureFromAzurePortal"
ehWriteConf = {
'eventhubs.connectionString' :
sc._jvm.org.apache.spark.eventhubs.EventHubsUtils.encrypt(writeConnectionString)
}
# Write body data from a DataFrame to EventHubs.
# Events are distributed across partitions using round-robin model.
ds = df \
.select("body") \
.write \
.format("eventhubs") \
.options(**ehWriteConf) \
.save()
You need to construct the body column somehow - by encoding your data as JSON using the to_json(struct("*")), or encoding data as Avro...
But you also have a problem in your cluster configuration - specifically these two libraries: azure-eventhubs-spark_2.11 and com.microsoft.azure:azure-eventhubs-spark_2.11:2.3.22 - they are for Spark 2, but you use Spark 3. Uninstall them, and restart the cluster.
I am new with NiFi, I am trying to send data from NiFi to Spark or to establish a stream from NiFi output port to Spark according to this tutorial.
Nifi is running on Kubernetes and I am using Spark operator on the same cluster to submit my applications.
It seems like Spark is able to access the web NiFi and it starts a streaming receiver. However, data is not coming to the Spark app through output and I have empty rdds. I have not seen any warnings or errors in Spark logs
Any Idea or information which could help me to solve this issue is appreciated.
My code:
val conf = new SiteToSiteClient.Builder()
.keystoreFilename("..")
.keystorePass("...")
.keystoreType(...)
.truststoreFilename("..")
.truststorePass("..")
.truststoreType(...)
.url("https://...../nifi")
.portName("spark")
.buildConfig()
val lines = ssc.receiverStream(new NiFiReceiver(conf, StorageLevel.MEMORY_ONLY))
Got some fundamental problems, hope someone can clear them up.
So I want to use Apache Kafka and Apache spark for my application. I have gone through numerous tutorials and got the basic idea of what it is and how it will work.
Use case :
Data will be generated from a mobile device(multiple devices, lets say 1000) at an interval of 40 sec and I need to process that data and add values to the database which in turn will be reflected back in a dashboard.
What I wanted to do is to use Apache Streams and make a post request from android itself and then those data will be processed by the spark application and that's it.
Issues:
Apache Spark
I am following this tutorial to get it up and running.( Am using JAVA, not scala)
Link : https://www.santoshsrinivas.com/installing-apache-spark-on-ubuntu-16-04/
After everything is done, I execute spark-shell and it start. I have also installed zookeeper and kafka on my server and I have started the Kafka in the background, so that's not an issue.
When I run http://161.xxx.xxx.xxx:4040/jobs/ I get this page
In all the tutorial which I have gone through, there is a page like this : https://i.stack.imgur.com/gF1fN.png but I don't get this. Is it that spark is not properly installed?
Now when I want to deploy a standalone jar to spark, (Using this link : http://data-scientist-in-training.blogspot.in/2015/03/apache-spark-cluster-deployment-part-1.html ) am able to run it.
i.e with the command : spark-submit --class SimpleApp.SimpleApp --master spark://http://161.xxx.xxx.xxx:7077 --name "try" /opt/spark/bin/try-0.0.1-SNAPSHOT.jar , I get the output.
Do I need to submit the application everytime if I want to use it?
This is my Program :
package SimpleApp;
/* SimpleApp.java */
import org.apache.spark.api.java.*;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.Function;
public class SimpleApp {
public static void main(String[] args) {
String logFile = "/opt/spark/README.md"; // Should be some file on your system
SparkConf conf = new SparkConf().setAppName("Simple Application").setMaster("local[*]");
JavaSparkContext sc = new JavaSparkContext(conf);
//System.setProperty("hadoop.home.dir", "C:/winutil");
sc.setLogLevel("ERROR"); // Don't want the INFO stuff
JavaRDD<String> logData = sc.textFile(logFile).cache();
long numAs = logData.filter(new Function<String, Boolean>() {
public Boolean call(String s) { return s.contains("a"); }
}).count();
long numBs = logData.filter(new Function<String, Boolean>() {
public Boolean call(String s) { return s.contains("b"); }
}).count();
System.out.println("Lines with a: " + numAs + ", lines with b: " + numBs);
System.out.println("word count : "+logData.first());
sc.stop();
}
}
Now how do I integrate Kafka into it?
How to configure the app in such a way that it get executed everytime
kafka receives a message?
Moreover, do I need to make a REST API through which I need to send
the data to kafka i.e the REST api will be used as producer?
Something like spark Java framework? http://sparkjava.com/
If yes, again the bottleneck will happen at REST api level i.e how
many request it can handle or not because everywhere I read that
Kafka has a very high throughput.
Is the final structure going to be like SPARK JAVA -> KAFKA -> APACHE
SPARK ?
Lastly how to do I set up the development structure on my local
device? I have kafka/apache spark installed. And am using Eclipse.
Thanks
Well,
You are facing some problems to understand how Spark works with Kafka.
First let's understand somethings:
Kafka is a Stream process platform for low latency and high throughput. This will allow you to store and read lot's of data really fast.
Spark has two types of processing, Spark Batch and Spark Streaming. What you are studying is batch, for your problem I suggest you to see apache streaming.
What is Streaming?
Streaming is a way to transport and transform your data in real time or near real time. It will not be necessary to create a process that you need to call every 10 minutes or every 10 seconds. You will start the job and it will consume the source and will post in the sink.
Kafka is a passive platform, so Kafka can be a source or a sink of a stream process.
In your case, what I suggest is:
Create a streaming producer for your Kafka, you will read the log of your mobile application in your web server. So, you need to plug something at your web server to start the consumption of the data. What I suggest you is FluentdIs a really strong application for streaming, this is in Ruby but is really easy to use. If you want something more robust and more focused in BigData I suggest Apache Nifi This is hard to work, that is not easy but you can create pipelines of data flow to transfer your information to your cluster. And something REALLY SIMPLE and that will solve your problem is Apache Flume.
Start your Kafka, you can use Docker to use it. This will hold your data for a period, and will allow you to take your data when you need really fast and with a lot of information. Please read the docs to understand how it works.
Spark Streaming - That will not make sense to use a Kafka if you don't have a stream process, your solution of Rest to produce the data at Kafka is slow and if is batch doesn't make sense. So if you are writing as streaming, you should analyse as streaming too. I suggest you to read about Spark Streaming here. And how integrate the Spark with Kafka here.
So, as you asked:
Do I need a REST API?
The answer is No.
The architecture will be like this:
Web Server -> Fluentd -> Apache Kafka -> Spark Streaming -> Output
I hope that will help
I am currently designing a fast data aggregation module which receives events and publish them to Kafka cluster. Then we have an integration of Kafka and Spark Streaming. Spark Streaming reads stream from Kafka and executes some computation. When the computation is done, we need to send the result to another application. This application could be a web service or a Kafka cluster.
I am wondering how we can do this? From what I've read, Spark Stream pushes the data to downstream like Databases and file systems.
How would you go with design such an application? Should I replace Spark Stream with Storm to be able to publish the results to another application?
Please refer to dstream.foreachRDD, which is a powerful primitive that allows data to be sent out to external systems.
Design Patterns for using foreachRDD
Below is my kafka integration code for your reference(not optimized, just for POC, KafkaProducer object could be re-used in foreachRDD):
DStream.foreachRDD(rdd => {
rdd.foreachPartition { partitionOfRecords =>
val kafkaProps = new Properties()
kafkaProps.put("bootstrap.servers", props("bootstrap.servers"))
kafkaProps.put("client.id", "KafkaIntegration Producer");
kafkaProps.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
kafkaProps.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
val producer = new KafkaProducer[String, String](kafkaProps);
partitionOfRecords.foreach(record => {
val message = new ProducerRecord[String, String]("hdfs_log_test", record.asInstanceOf[String])
producer.send(message)
})
producer.close()
}
})
I am wondering how we can do this? From what I've read, Spark Stream pushes the data to downstream like Databases and file systems.
Spark is not limited to HDFS or Databases, you're free to initialize a connection to any external resource which is available. It can be back to Kafka, RabbitMQ or a WebService.
If you're doing simple transformation like map, filter, reduceByKey etc, then using DStream.foreachRDD will do fine. If you'll be doing stateful computations like DStream.mapWithState, then once you're done processing the state you can you can simply send the data to any external service.
For example, we're using Kafka as an input stream of data, and RabbitMQ and an output after doing some stateful computations.
I want to create a system where I can read logs in real time, and use apache spark to process it. I am confused if I should use something like kafka or flume to pass the logs to spark stream or should I pass the logs using sockets. I have gone through a sample program in the spark streaming documentation- Spark stream example. But I will be grateful if someone can guide me a better way to pass logs to spark stream. Its kind of a new turf to me.
Apache Flume may help to read the logs in real time.
Flume provides logs collection and transport to the application where Spark Streaming is used to analyze required information.
1. Download Apache Flume from official site or follow the instructions from here
2. Setup and run Flume
modify flume-conf.properties.template from the directory where Flume is installed (FLUME_INSTALLATION_PATH\conf), here you need to provide logs source, channel and sinks (output). More details about setup here
There is an example of launching flume which collects log information from ping comand running on windows host and writes it to a file:
flume-conf.properties
agent.sources = seqGenSrc
agent.channels = memoryChannel
agent.sinks = loggerSink
agent.sources.seqGenSrc.type = exec
agent.sources.seqGenSrc.shell = powershell -Command
agent.sources.seqGenSrc.command = for() { ping google.com }
agent.sources.seqGenSrc.channels = memoryChannel
agent.sinks.loggerSink.type = file_roll
agent.sinks.loggerSink.channel = memoryChannel
agent.sinks.loggerSink.sink.directory = D:\\TMP\\flu\\
agent.sinks.loggerSink.serializer = text
agent.sinks.loggerSink.appendNewline = false
agent.sinks.loggerSink.rollInterval = 0
agent.channels.memoryChannel.type = memory
agent.channels.memoryChannel.capacity = 100
To run the example go to FLUME_INSTALLATION_PATH and execute
java -Xmx20m -Dlog4j.configuration=file:///%CD%\conf\log4j.properties -cp .\lib\* org.apache.flume.node.Application -f conf\flume-conf.properties -n agent
OR you may create your java application that has flume libraries in a classpath and call org.apache.flume.node.Application instance from the application passing corresponding arguments.
How to setup Flume to collect and transport logs?
You can use some script for gathering logs from the specified location
agent.sources.seqGenSrc.shell = powershell -Command
agent.sources.seqGenSrc.command = your script here
instead of windows script you also can launch java application (put 'java path_to_main_class arguments' in field) which provides smart logs collection. For example, if the file is modified in real-time you can use Tailer from Apache Commons IO.
To configure the Flume to transport the log infromation read this article
3. Get the Flume stream from your source code and analyze it with Spark.
Take a look on a code sample from github https://github.com/apache/spark/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
You can use Apache Kafka as queue system for your logs. The system that generated your logs e.g websever will send logs to Apache KAFKA. Then you can use apache storm or spark streaming library to read from KAFKA topic and process logs at real time.
You need to create stream of logs , which you can create using Apache Kakfa. There are integration available for kafka with storm and apache spark. both has its pros and cons.
For Storm Kafka Integration look here
For Apache Spark Kafka Integration take a look here
Although this is a old question, posting a link from Databricks, which has a great step by step article for log analysis with Spark considering many areas.
https://databricks.gitbooks.io/databricks-spark-reference-applications/content/logs_analyzer/index.html
Hope this helps.