Skip to main content



Apache Kafka: Producer with Custom Partitioner

Below sample code depicts to write Producer with Custom Partitioner

PartitionProducer
package ds.kafka;

import java.util.Date;
import java.util.Properties;
import java.util.Random;


import kafka.producer.ProducerConfig;
import kafka.producer.KeyedMessage;
import kafka.javaapi.producer.Producer;

public class PartitionProducer {

       Producer<String, String> producer;

       public PartitionProducer() {
              Properties props = new Properties();
              props.put("metadata.broker.list", "192.168.56.101:9092, 192.168.56.101:9093, 192.168.56.101:9094");
              props.put("serializer.class", "kafka.serializer.StringEncoder");

              // determine the partition in the topic where message needs to be sent
              props.put("partitioner.class", "ds.SimplePartitioner");
              props.put("request.required.acks", "1");

              ProducerConfig config = new ProducerConfig(props);
              producer = new Producer<String, String>(config);

       }

       public static void main(String[] args) {

              if (args.length != 2) {
                     throw new IllegalArgumentException(
                                  "Please provide topic name and Message count as arguments");
              }

              PartitionProducer partitionProducer = new PartitionProducer();
              try {
                     partitionProducer
                                  .publishMessage(args[0], Integer.parseInt(args[1]));
              } finally {
                     partitionProducer.close();
              }

       }

       private void close() {
              producer.close();
       }

       private void publishMessage(String topic, int msgCount) {
              Random random = new Random();

              for (int i = 0; i < msgCount; i++) {
                     String clientIP = "192.168.56." + random.nextInt(256);
                     String accessTime = new Date().toString();
                     String message = accessTime + ",kafka.apache.org," + clientIP;
                     System.out.println(message);
                     KeyedMessage<String, String> keyedMessage = new KeyedMessage<String, String>(
                                  topic, clientIP, message);
                     // Publish the message
                     producer.send(keyedMessage);
              }

       }

      
}

Partitioner
package ds.kafka;

import kafka.producer.Partitioner;
import kafka.utils.VerifiableProperties;

public class SimplePartitioner implements Partitioner {
                 /*if this method is not written it may result in exception: java.lang.NoSuchMethodException: learning.kafka.SimplePartitioner.<init>(kafka.utils.VerifiableProperties)*/
                public SimplePartitioner(VerifiableProperties properties) {
                                System.out.println("Creating Object of Simple Partitioner");
                }

                @Override
                public int partition(Object key, int a_numPartitions) {
                                int part = 0;
                                String partitionKey = (String) key;

                                int offset = partitionKey.lastIndexOf('.');

                                if (offset > 0) {
                                                part = Integer.parseInt(partitionKey.substring(offset + 1))% a_numPartitions;
                                }

                                return part;
                }

}


If partiton() returns an integer that is greater than the actual number of topic partition then above code will not able to produce message to topic and will fail with below exception trace:

Exception in thread "main" kafka.common.FailedToSendMessageException: Failed to send messages after 3 tries.
at kafka.producer.async.DefaultEventHandler.handle(DefaultEventHandler.scala:90)
at kafka.producer.Producer.send(Producer.scala:77)
at kafka.javaapi.producer.Producer.send(Producer.scala:33)
at learning.kafka.PartitionProducer.publishMessage(PartitionProducer.java:62)

Comments

Popular posts

Python [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: Missing Authority Key Identifier

  Error requests.exceptions.SSLError: HTTPSConnectionPool  Max retries exceeded with url:  (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: Missing Authority Key Identifier (_ssl.c:1028)'))). Analysis & Solution Recently, we updated from Python 3.11 to 3.13, which resulted in error above. We did verify AKI = SKI in chain of certificates. Also, imported chain of certificates into certifi. Nothing worked for us. Seemingly, it is a bug with Python 3.13. So, we downgraded to Python 3.12 and it started working. Other problems and solution -  '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1006)'))) solution  pip install pip-system-certs [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired  (_ssl.c:1006) solution  1# openssl s_client -showcerts -connect  signin.aws.amazon.com:443  </dev/...




Spring MongoDB Rest API not returning response in 90 seconds which is leading to client timeout

  We have Spring Boot  Rest API deployed in Kubernetes cluster which integrates with MongoDB to fetch the data.  MongoDB is fed with data by a real time Spark & NiFi job.  Our clients complained that for a request what they send they don't have response within 90 seconds. Consider it like an OMS ( Order ManagEment System).  On further analysis, we found that Spark & NiFi processing is happenning within 10 seconds after consuming response data from Kafka. Thus, initally out thought was that it due to delay from upstream to produce data in to Kafka.  Thankfully, our data had create / request  timestamp, and when response was received, and when response was inserted into MongoDB. Subtracting response insert time from request time seemed to be well within 90 seconds. But, still client did timeout on not seeing a response within 90 seconds. This led to confusion on our side.  But, then we realized it was due to Read Preference . We updated this...




MongoDB Regex Query taking more time in Production but same query perform well in UAT

   We came across a situation where-in, MongoDB Query was taking more time in Production like 10 seconds and 4.2 seconds but same query performed well in UAT taking under 400 ms. The very first thought that was evident to us that it is because of amount of data which differed in UAT and Production. Then we ran following to see the execution plan -   db.collection.aggregate(<queries>).explain() This gave us Winning and Rejected Plans. Under which, we analyzed that although it was using 'IXSCAN.' But, it was incorrect index- as we had one compound index built on time field and other fields, and there was other index just on time field for TTL purposes. Winning plan picked TTL index rather than compound index. Thus, we dropped TTL index and built TTL index on a different time field.  That got our query performance time from 10 seconds to 726 ms. Also, for other query the performance came down from 8 seconds to 4.3 seconds. Then, we ran following -  ...




What is Leadership

 




Spark MongoDB Connector Not leading to correct count or data while reading

  We are using Scala 2.11 , Spark 2.4 and Spark MongoDB Connector 2.4.4 Use Case 1 - We wanted to read a Shareded Mongo Collection and copy its data to another Mongo Collection. We noticed that after Spark Job successful completion. Output MongoDB did not had many records. Use Case 2 -  We read a MongoDB collection and doing count on dataframe lead to different count on each execution. Analysis,  We realized that MongoDB Spark Connector is missing data on bulk read as a dataframe. We tried various partitioner, listed on page -  https://www.mongodb.com/docs/spark-connector/v2.4/configuration/  But, none of them worked for us. Finally, we tried  MongoShardedPartitioner  this lead to constant count on each execution. But, it was greater than the actual count of records on the collection. This seems to be limitation with MongoDB Spark Connector. But,  MongoShardedPartitioner  seemed closest possible solution to this kind of situation. But, it per...