Skip to main content



Apache Kafka


Install Kafka:

  • Download tar file.
  • Extract it at location say /usr/local/kafka_2.11-0.8.2.2
  • Set variables in .bashrc

###Kafka
export KAFKA_HOME=/usr/local/kafka_2.11-0.8.2.2
export PATH=$PATH:$KAFKA_HOME/bin
###

With Kafka, we can create multiple types of clusters, such as the following:
  •  A single node—single broker cluster
  • A single node—multiple broker cluster
  • Multiple nodes—multiple broker clusters

A single node – a single broker cluster
·         
      Starting the ZooKeeper server:
>bin/zookeeper-server-start.sh config/zookeeper.properties

·         Starting the Kafka broker:
>bin/kafka-server-start.sh config/server.properties

·         Creating a Kafka topic:
>kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic kafkatopic

·         Get list of topics:
>kafka-topics.sh --list --zookeeper localhost:2181

·         Start console-based producer
>kafka-console-producer.sh --broker-list localhost:9092 --topic kafkatopic

type:
Welcome to Kafka DS
This is single node single broker cluster
Just started !! Jai Ganesh

·         Start command line consumer client
>kafka-console-consumer.sh --zookeeper localhost:2181 --topic kafkatopic --from-beginning

Output:
Welcome to Kafka DS
This is single node single broker cluster
Just started !! Jai Ganesh


A single node – multiple broker clusters
·         Starting the ZooKeeper server:
>bin/zookeeper-server-start.sh config/zookeeper.properties

·         Starting the Kafka broker:
For setting up multiple brokers on a single node, different server property files are required for each broker. Each property file will define unique, different values for the following properties: broker.id, port, log.dir

>bin/kafka-server-start.sh config/server-1.properties
>bin/kafka-server-start.sh config/server-2.properties

·         Creating a Kafka topic using the command line
>kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 2 --partitions 4 --topic replicated-kafkatopic

Note: - Replication factor should be in accordance with number of brokers. Else can cause below exception:
kafka.admin.AdminOperationException: replication factor: 3 larger than available brokers: 2
        at kafka.admin.AdminUtils$.assignReplicasToBrokers(AdminUtils.scala:70)
        at kafka.admin.AdminUtils$.createTopic(AdminUtils.scala:171)
        at kafka.admin.TopicCommand$.createTopic(TopicCommand.scala:93)
        at kafka.admin.TopicCommand$.main(TopicCommand.scala:55)
        at kafka.admin.TopicCommand.main(TopicCommand.scala)

·         Starting a producer to send messages
>kafka-console-producer.sh --broker-list localhost:9093, localhost:9094 --topic replicated-kafkatopic

If we have a requirement to run multiple producers connecting to different combinations of brokers, we need to specify the broker list for each producer as we did in the case of multiple brokers.

·         Starting a consumer to consume messages
>kafka-console-consumer.sh --zookeeper localhost:2181 --from-beginning --topic replicated-kafkatopic

Multiple node- multiple broker cluster

We should install Kafka on each node of the cluster, and all the brokers from the different nodes need to connect to the same ZooKeeper. Then follow the same step on every machine to start broker as followed above to start multiple broker on single machine.

Comments

Post a Comment

Popular posts

Spring MongoDB Rename field with derived Value of another field

Input Collection -  [ { 'k' : 'Troubleshooting' , 'hour' : '2024-10-10T16' , 'v' : [ 'WebPage, Login' ] }, { 'k' : 'TroubleshootingMe' , 'hour' : '2024-10-07T01' , 'v' : [ 'Accounts, etc' ] }  ] Expected Output -  [ { 'hour' : '2024-10-10T16' , 'Troubleshooting' : [ 'WebPage, Login' ] }, { 'hour' : '2024-10-07T01' , 'TroubleshootingMe' : [ 'Accounts, etc' ] }  ]   Above Can be achieved by  $replaceRoot / $replaceWith as follows - { $replaceWith : { $mergeObjects : [ { hour : "$hour" }, { "$arrayToObject" : [ [ { k : "$k" , v : "$v" } ] ] } ] } } or { $replaceRoo...
Post a Comment
Read more




What is Leadership

 
Post a Comment
Read more




Spark Error - missing part 0 of the schema, 2 parts are expected

 Exception -  Caused by: org.apache.spark.sql.AnalysisException : Could not read schema from the hive metastore because it is corrupted. (missing part 0 of the schema, 2 parts are expected).; Analysis -  ·          Check for table definition. In TBLProperties, you might find something like this – > spark.sql.sources.schema.numPartCols > 'spark.sql.sources.schema.numParts' 'spark.sql.sources.schema.part.0' > 'spark.sql.sources.schema.part.1' 'spark.sql.sources.schema.part.2' > 'spark.sql.sources.schema.partCol.0' > 'spark.sql.sources.schema.partCol.1' That’s what error seems to say that part1 is defined but part0 is missing.  Solution -  Drop & re-create table. If Table was partitioned  then all partitions  would have been removed. So do either of below -  ·          Msck repair table <db_name>.<table_name> ·    ...
Post a Comment
Read more




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...
Post a Comment
Read more




Read from a hive table and write back to it using spark sql

In context to Spark 2.2 - if we read from an hive table and write to same, we get following exception- scala > dy . write . mode ( "overwrite" ). insertInto ( "incremental.test2" ) org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; 1. This error means that our process is reading from same table and writing to same table. 2. Normally, this should work as process writes to directory .hiveStaging... 3. This error occurs in case of saveAsTable method, as it overwrites entire table instead of individual partitions. 4. This error should not occur with insertInto method, as it overwrites partitions not the table. 5. A reason why this happening is because Hive table has following Spark TBLProperties in its definition. This problem will solve for insertInto met...
Post a Comment
Read more