Kafka for Dummies ??

Nowadays in the hurry of learning more and more things led people choose the wrong path or wrong way to learn things. There must be only two things in your mind to learn something in the first place.

From my opinion to learn something we first need to learn what is that technology therefore what problem does the technology solves for us ?

Yes, that's it, only following these you will be able to learn that technology faster and more efficiently because all the other topics related to that topic must be linked with the Which problem does this solve problem.

So let's learn in that way ??

(??There will be hands on experience with kafka too with Nodejs and docker, stay tuned??)

What is Kafka ?

Kafka is an open-source distributed event streaming platform developed by the Apache Software Foundation. It's widely used for building real-time data pipelines and streaming applications. Kafka is particularly suited for handling large-scale, high-throughput, and low-latency real-time data feeds.

What problem Kafka Solves ?

Kafka solves several key problems related to real-time data processing, scalability, fault tolerance, and durability:

1. Real-time Data Streaming:

Kafka enables real-time data pipelines, allowing continuous data flow. Traditional systems often rely on batch processing, which introduces latency. Kafka supports immediate, low-latency data streaming crucial for applications like IoT, financial transactions, and monitoring systems.

2. Scalability and High Throughput:

Kafka handles high volumes of data by partitioning topics across multiple brokers. This allows horizontal scaling and supports millions of events per second, unlike legacy messaging systems which slow down under load.

3. Fault Tolerance and Durability:

Kafka ensures data resilience by replicating partitions across brokers. This protects against node failures, ensuring no data is lost. Kafka also persists messages on disk, allowing consumers to retrieve them later even after crashes.

4. Decoupling Producers and Consumers:

Kafka decouples producers from consumers, allowing them to operate independently. This prevents tight integration, allowing each to scale without impacting the other. Consumers process data at their own pace, enhancing system flexibility.

5. Log Aggregation and Event Sourcing:

Kafka helps centralize logs from multiple sources, making large-scale log aggregation efficient. It also supports event sourcing, allowing systems to track and replay events, which is essential for debugging and recovery.

6. Seamless Integration Across Systems:

With Kafka Connect, Kafka integrates with databases, cloud services, and more, simplifying the transfer of data across platforms.

7. Stream Processing:

Kafka’s Streams API enables real-time analytics and transformations, ideal for applications like fraud detection and recommendation systems.

Key Solutions Kafka Provides:

• Real-time data streaming for continuous processing.
• High-throughput messaging for massive data volumes.
• Fault tolerance to avoid data loss.
• Decoupling of producers and consumers for scalability.
• Log aggregation and event sourcing for tracking system events.
• Seamless integration with other systems.
• Stream processing for real-time analytics.

Kafka is widely used in areas like microservices, IoT, and financial systems where real-time, scalable data processing is critical.

Architecture of Kafka

Kafka’s architecture is designed for fault tolerance, scalability, and durability. Here's how:

• Producers send messages to Kafka brokers.
• Each message gets appended to a specific partition within a topic.
• Kafka brokers replicate partitions across different servers (nodes) to provide fault tolerance. This is controlled via replication factor, which specifies how many copies of the data are maintained.
• Consumers pull messages from Kafka topics, either by reading from a specific partition or from all partitions in a round-robin manner.
• Kafka provides offsets, which are numerical IDs used to track the position of the last-read message within a partition. This allows consumers to restart from where they left off.

Tools used by kafka:

There are some concepts that are often referred to as tools used by kafka so never confuse those two things.

Producer:

• A producer is any application or process that sends (or publishes) messages to Kafka.
• Producers write data to topics and are responsible for choosing which partition within the topic to write to.

Consumer:

• A consumer reads (or subscribes to) messages from Kafka topics.
• Consumers can either read from a specific partition or from multiple partitions in a round-robin or load-balanced manner.
• Consumers are usually part of consumer groups, where each consumer in the group processes a part of the message stream to achieve scalability.

Topic:

• A topic is a category or feed to which records are published. It acts as a virtual log where producers write and consumers read.
• Kafka stores records in topics, and each topic can have multiple partitions to allow scalability and parallel processing.

Partition:

• Each topic is split into partitions. A partition is a sequential log of records where new records are appended to the end.
• Partitioning allows Kafka to distribute messages across multiple nodes, enabling high throughput and fault tolerance.
• Kafka ensures that records within a partition are ordered, though messages across different partitions may not be in strict order.

Broker:

• A Kafka broker is a server that runs Kafka. Brokers handle the storage, management, and delivery of data.
• Kafka clusters can consist of multiple brokers, providing redundancy and allowing for horizontal scaling.

Zookeeper:

• Apache Kafka originally used Zookeeper to maintain metadata about topics, partitions, and brokers. Zookeeper is a distributed coordination service that helps in leader election for partitions and managing cluster membership.
• With newer versions of Kafka (2.8+), there is a transition towards removing Zookeeper dependency in favor of Kafka’s own internal metadata management.

It's use cases

Kafka is used for managing and scaling large architectures which are loosely coupled, such as Netflix, Uber and other architectures leveraging the power of EDA (Event Driven Architecture)

Use cases which are considered to look into with the help of Kafka

• Real-time data pipelines for continuous processing.
• Event sourcing for state tracking and replay.
• Log aggregation for centralized logging.
• Real-time analytics for immediate insights.
• Messaging queue for high-throughput message delivery.
• Stream processing for data transformation and filtering.
• Data integration between systems.
• Metrics and monitoring for real-time performance tracking.
• Microservice communication via asynchronous messaging.
• Data lake ingestion for long-term storage and analysis.

Why Kafka is so fast ??

Kafka is fast due to its sequential disk writes and log-based architecture, which minimize random access and optimize data writing and reading. It efficiently uses disk I/O by writing data in batches, reducing the overhead of frequent writes. Kafka's partitioning allows for parallel processing across multiple brokers, enhancing throughput. Data is compressed and sent in bulk, reducing network overhead. Additionally, Kafka’s consumers pull data, reducing pressure on brokers, and its zero-copy mechanism in Linux allows data to be transferred directly from disk to network without extra memory copies, further improving performance.

What is sequential data write ?

Sequential disk writes refer to the method of writing data to a disk in a continuous, linear fashion, as opposed to randomly accessing various locations on the disk. This approach has several advantages:

Benefits of Sequential Disk Writes:

1. Increased Throughput: Writing data in a sequential manner maximizes the throughput of disk operations because the read/write head of the disk doesn’t have to move around as much. This results in faster data writing and reading speeds.
2. Reduced Latency: Since data is written in a linear path, there is less seek time (the time it takes for the read/write head to position itself over the correct track on the disk). This reduces the overall latency when accessing data.
3. Efficient Disk Usage: Sequential writing is more efficient in utilizing disk space, allowing for better overall performance, especially for applications that require frequent data writing, like logging or event streaming.
4. Optimized Performance for Batch Operations: When data is written in batches (common in systems like Kafka), sequential writes allow for larger amounts of data to be written at once, further enhancing speed and efficiency.

Context in Kafka:

In Kafka, data is stored in a log file, and new messages are appended to the end of this log sequentially. This design choice allows Kafka to handle high write and read loads efficiently, making it a suitable choice for high-throughput data streaming applications.

Here are the factors contributing to Kafka's high performance and speed, presented in an unordered manner:

• Log-Based Architecture: Messages are appended sequentially in a log structure, allowing efficient reading and writing.
• Replication: Data is replicated across multiple brokers for fault tolerance, using an efficient leader-follower model.
• Efficient Consumer Model: Consumers pull messages at their own pace, reducing broker load and enabling independent scaling.
• Partitioning: Topics are divided into partitions, allowing parallel processing across multiple brokers and consumers.
• Zero-Copy Mechanism: This Linux feature allows data to be sent directly from disk to the network socket without extra memory copies, reducing CPU overhead.
• Batch Processing: Producers can send messages in batches, minimizing network call overhead and optimizing bandwidth use.
• Asynchronous I/O: Non-blocking operations allow producers and consumers to send and receive messages efficiently, improving throughput.
• Compression: Supports message compression (e.g., Gzip, Snappy, LZ4) to reduce data size for transmission, leading to lower latency.
• In-Memory Caching: Frequently accessed data can be cached in memory, speeding up read operations and minimizing disk access.
• Configuration and Tuning: Offers various configuration options to optimize performance parameters based on specific use cases.

These combined factors make Kafka a robust and high-performing system for handling high-throughput data streams.

Let's do something !!

Enough of the talk and let's create a basic command line based message producer and consumer service which will leverage the power of kafka with Node.js and Docker (To run the kafka broker and zookeeper)

Setting up containers ??

First things first, to literally run the kafka broker or server we will need linux, and for testing purposes we are not gonna setup a linux environment for that, we will be using docker for that, so let's jump into the coding the compose file

Now let's configure the broker or the server !!

• Initialize a nodejs project with npm or yarn, the command will be `npm init -y` or `yarn init -y`
• Install dependencies (only 1 ??) - `npm install kafkajs` or `yarn add kafkajs`
• Create a file named `admin.js`
• and another named `client.js`

Add these into client.js and admin.js respectively

And setup the producer to produce the messages

Therefore setup the consumer inside `consumer.js`

Let's see this in action, shall we ??

Conclusion & key takeaways

Kafka's high performance is the result of its intelligent design choices, including a log-based architecture, partitioning for parallelism, and efficient handling of data with batching, compression, and zero-copy. It leverages fault tolerance through replication and allows for scalable, real-time data processing. Its asynchronous I/O and consumer-driven model further optimize performance, making it ideal for handling large-scale streaming data.

Key Takeaways:

• Log-based architecture ensures efficient sequential writes and reads.
• Partitioning enables parallel processing, enhancing scalability.
• Batch processing and compression optimize network and storage efficiency.
• Zero-copy minimizes CPU overhead during data transmission.
• Replication ensures fault tolerance without sacrificing performance.
• Asynchronous I/O and consumer-driven pull model enhance throughput and flexibility.
• Tunable configurations allow for further performance optimization.

Kafka's design makes it one of the fastest and most scalable platforms for real-time streaming data pipelines and event-driven architectures.