HARDCODE

  Confluent has built some useful tools that rely on Kafka . One of these tools is Kafka Connect . And as I have shown you before  one of the ways to use Kafka Connect as a consumer of Kafka Topics, I will show you how to use Kafka Connect as a producer to Kafka topics Kafka Connect offers a very powerful feature which is connecting to databases using certain connectors as JDBC (Java Database Connectivity).  The Kafka Connect JDBC source connector allows you to import data from any relational database with a JDBC driver into an Apache Kafka topic. This connector supports a wide variety of databases. Data is loaded by periodically executing a SQL query and creating an output record for each row in the result set. It enables you to pull data (source) from a database into Kafka , and also push data (sink) from a Kafka topic to a database. In this tutorial we'll be focusing on pulling data from a SQL database. I will be reusing the same setup as my p...

 Suppose you need to store all the messages published on a certain topic. Normally you would create a consumer that listens to that topic, receives the message then stores it in the dataset that you want. What if I told you that Confluent has created a shortcut. Instead of doing this, you can just create a Kafka connector using Confluent Kafka Connect and this connector will automatically store any published message in your database. Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other data systems. It makes it simple to quickly define connectors that move large collections of data into and out of Kafka . Kafka Connect can ingest entire databases or collect metrics from all your application servers into Kafka topics, making the data available for stream processing with low latency. It can also deliver data from Kafka topics into secondary indexes like Elasticsearch or batch systems such as Hadoop for offline analysis. In this tutorial we...

 There are two keywords that you must understand when publishing a message: latency and throughput.  Throughput is a measure of how much data can be processed in a given amount of time. It's usually measured in bits per second (bit/s), or data packets per second. High throughput means the system can process a large amount of data quickly, which is often desirable in high-load scenarios. For example, if a Kafka producer can send 1000 messages per second to a broker, the throughput is 1000 messages per second. Latency, on the other hand, is a measure of time delay experienced in a system, the time it takes for a bit of data to travel from one point to another in a network. It is usually measured in milliseconds. Low latency means that data can be transferred quickly from source to destination. For example, if a message takes 10 milliseconds from the time it's sent by a Kafka producer until it's received by a broker, the latency is 10 milliseconds. In all systems, there's ...

 People say that Kafka is a dumb broker. It just holds data under some defined topics and forward messages from producers to consumers. It doesn't do much processing on the messages, doesn't route them based on content, and doesn't transform them. But that actually isn't entirely true. Kafka does much more than just storing messages. It keeps count of which consumer group consumed which message by tracking its offset. The consumer group is the group Id you give your consumer once you initialize it. Having more than one consumer helps you avoid consumer failures and scale better. If a message is consumed by one consumer in a group, no other consumers in the same group will receive it. However, all other consumers with other group Ids will receive the message. It's important to note that the offset of each message is basically the id of the message related to the group. So, if "message A" was published on a brand-new group its offset will be 0. But if anoth...

When we talk about Kafka it's important to mention Avro serialization. Like I mentioned in my Kafka introductory post : as a message broker, Kafka is quite known for being complex as opposed to some other brokers such as SQS (which is literally called Simple Queue Service) or RabbitMQ which calls its producers Basic Produce and consumers Basic Consume . And although it requires a little more effort than the other brokers, it offers much more for services which need to trim down every inch of latency they can, due to the criticality of the business of the sheer volume of data it requires to stream. And Confluent Kafka can help with that by transporting Avro serialized messages. But in order to do that, Kafka relies on Confluent   Schema Registry . Because after it gets serialized to bytes, it needs to look for the schema that can help these bytes go back to the model that it initially was. Confluent Schema Registry is basically a store that keeps track of the schemas of the mess...

 Message brokers come in all colors. Each broker has its own edge. There are some brokers which are aimed to be simple and direct such as Amazon SQS (which is literally called Simple Queue Service). Some of them can be used in a simple fashion, but can also be used to implement complex patterns like RabbitMQ which is a topic I talked about in my previous posts . And then there is a broker that was designed to: handle heavy-duty streaming, maximize efficiency, allow you to scale up, serialize messages to bytes, partition messages and much more. That broker is indeed Apache Kafka . Due to its wide array of features, Kafka can be overwhelming. And sometimes it feels too exhausting to start comprehending its principles and what the broker can provide. So, why not strip down the broker from all of its extra shining features and just start from its core features like publishing and consuming simple JSON serialized string messages. What we're going to explore isn't just Apache Kafk...