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Kafka

Dec 20 2022

A Comprehensive Overview Of Apache Kafka

Overview

Apache Kafka is an open-source, distributed event-streaming platform, or message queuing system. Kafka provides real-time data analysis that runs on servers and clients, either locally or in the cloud, on Linux, Windows, or Mac platforms. Kafka’s messages are persisted on disk and replicated within the cluster to prevent data loss.

Some typical Kafka use cases are stream processing, log aggregation, data ingestion to Spark or Hadoop, error recovery, etc.

In Kyle Pollack’s Lightning Talk session, we will breaking down the following topics:

  • Overview
  • Basic Architecture
  • Benefits
  • Advantages Of Apache Kafka
  • Use Cases For Kafka
  • Closing Thoughts

Basic Architecture

There are four main components:

  • The Producer – The client apps that write their Events, or Topics, to the Kafka queue
  • The Topic – Topics are the Events that Kafka stores. They are multi-producer, multi-subscriber (Consumer), decoupled, and can have any number of subscribers or none at all
  • The Broker – Each Broker is a Kafka server that organizes and sequentially stores incoming Events by Topic and stores them on disk in Segmented Partitions
  • Consumer – The apps that subscribe to Kafka Topics

A Kafka cluster is made of one or more servers, called Brokers. Topics live in one or more Partitions on one or more Brokers. 

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As Producers write events to the Topic queues, the Brokers store the message in Segments within their Partitions according to Topic ID. Kafka always writes Event messages into any Partition configured for that Topic ID, on any Broker. Because the save is spread across all Brokers that service that Topic ID and the data is written non-sequentially into Segments within those Partitions, there is no single Broker or Partition that contains the full, sequential list of Events for that Topic. Each Partition only holds a subset of Event records in its Segments.

Kafka Producers

Producers are client applications writing Topics to the Kafka Cluster. 

Kafka Brokers

Brokers receive event streams from Producers and store them sequentially by Topic ID in one or more Partitions across one or more Brokers. Each Broker can handle many Partitions in its storage. All received messages are stored with an Offset ID.

For example, when receiving three events on a given Broker having three partitions, the Broker could store those Events to Partitions in this order 2, 1, and 3, while another Broker in the cluster could store them to 3, 2, and1. Because the writes to Partitions within Brokers are ad hoc, the individual Segments in any one Partition do not contain a sequential string of events. However, on retrieval, Kafka provides those records in their correct order by using their Broker-assigned Offset ID. 

Additionally, you can configure the Event retention as suitable for the application.

The Topic

Kafka organizes events by Topic and may store a Topic in multiple Partitions on multiple Brokers. This provides reliability and also enhances performance by avoiding the I/O bottlenecks that using a single Broker might entail, by spreading the store action across multiple computers.Topics are assigned Topic IDs.

Kafka Consumers

Consumers are apps that read Topic information from Kafka queues. Consumers automatically retrieve new messages as they arrive in the queue.

Benefits

  • I/O Performance – Non-sequentially writing Event records to multiple Brokers/Partitions avoids I/O bottlenecks that could occur if they were written sequentially into a single Partition.
  • Scalability – Kafka scales horizontally by increasing the number of Brokers in the cluster.
  • Data Redundancy – You can configure Kafka to write each event to multiple brokers.
  • High-Concurrency, low-latency, high-throughput
  • Fault-Tolerant
  • Message Broker Capabilities
  • Batch Handling Capability (providing ETL-like functionality)
  • Persistent by default
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Advantages Of Apache Kafka

Real-time data analysis provides faster insights into your data allowing faster response times. For example, to make predictions about what should be stocked, promoted, or pulled from the shelves, based on the most up-to-date information possible.

Even on very large systems, Kafka operates very quickly. You can stream all data in real time to make decisions based on current information, rather than waiting until the data has been obtained, aggregated, and analyzed, which is the case for many companies with large datasets.

Kafka is written in Java, so it is easier to learn.

Use Cases For Kafka

Kafka is used for: 

  • Stream processing
  • Website activity tracking
  • Metrics collection and monitoring
  • Log aggregation
  • Real-time analytics
  • Common Extensibility Platform support (CEP)
  • Ingesting data into Spark
  • Ingesting data into Hadoop
  • Command Query Responsibility Segregation support (CQRS)
  • Replay messages
  • Error recovery
  • Guaranteed distributed commit log for in-memory computing (microservices)
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Closing Thoughts

Apache Kafka is a distributed streaming platform capable of handling trillions of events a day. Kafka provides low-latency, high-throughput, fault-tolerant publish and subscribe pipelines and is able to process streams of events.

Happy coding! To learn more about the implementation of Apache Kafka and to experience Kyle Pollack’s full Lightning Talk session, watch here.

Written by Kaela Coppinger · Categorized: Code Lounge, DevOps, Java Engineering, Product Development, Software Engineering, Web Engineering · Tagged: Apache, best practices, INRHYTHMU, JavaScript, Kafka, learning and growth, software engineering, web engineering

Sep 07 2022

Integrations For Apache – Flink, NiFi, And Kafka

Overview 

Apache is one of the go-to web servers for website owners and developers, with more than a 50% share in the commercial web server market. 

Apache HTTP Server is a free and open-source server that delivers web content through the internet. It is one of the oldest and most reliable web server software maintained by the Apache Software Foundation, with the first version released in 1995. It is commonly referred to as Apache and after development, it quickly became the most popular HTTP client on the web.

It is a modular, process-based web server application that creates a new thread with each simultaneous connection. It supports a number of features; many of them are compiled as separate modules and extend its core functionality, and can provide everything from server side programming language support to authentication mechanisms. Virtual hosting is one such feature that allows a single Apache Web Server to serve a number of different websites.

In Tim Spann’s Lightning Talk session, we will breaking down the following topics:

  • What Is An Apache Integration?
  • Apache Flink
  • Apache Nifi 
  • Apache Kafka
  • Live Demonstrations
  • Closing Thoughts

What Is An Apache Integration?

A software integration is the process of bringing together various types of software subsystems so that they create a unified single system. The integration should be carefully coordinated to result in a seamless connection of the separate parts. When done skillfully, the increased efficiency is a tremendous benefit to the Apache developer.

Implementing an integration that works best for an individual development team’s workflow is essential to the future success of the project. Taking time to explore and walk through a number of layout features will only stand to benefit the user-experience and streamlining of efficient tasking. 

Apache Flink

Apache Flink is a real-time processing framework which can process streaming data. It is an open source stream processing framework for high-performance, scalable, and accurate real-time applications. It has a true streaming model and does not take input data as batch or micro-batches.

Example: The below framework diagrams the different layers that run as part of the Apache Flink ecosystem. 

Apache Nifi

Apache NiFi is a visual data flow based system which performs data routing, transformation and system mediation logic on data between sources or endpoints.

Apache Kafka

Apache Kafka is a distributed streaming platform that: Publishes and subscribes to streams of records, similar to a message queue or enterprise messaging system. Stores streams of records in a fault-tolerant durable way. Processes streams of records as they occur.

Apache Kafka was built with the vision to become the central nervous system that makes real-time data available to all the applications that need to use it, with numerous use cases like stock trading and fraud detection, to transportation, data integration, and real-time analytics.

Example: The below framework diagrams the operational flow of information using the Apache Kafka plug-in. 

Live Demonstrations

Tim Spann has crafted an intuitive demonstration to help guide you through these different Apache integrations in practicum: 

Be sure to follow Tim’s entire Lightning Talk to view this impressive demonstration in real time.

Closing Thoughts 

All programs should always be designed with performance and the user experience in mind. The properties explored above are the primary stepping stones to exploring the basic components needed to test how Apache Integrations can improve your personal data application. Be sure to explore, have fun, and match up the components that work best for your project!

Happy coding!

To learn more about Apache Integrations in application development and to experience Tim Spann’s full Lightning Talk session, watch here. 

Written by Kaela Coppinger · Categorized: InRhythmU, Learning and Development, Product Development, Web Engineering · Tagged: Apache, devops, Flink, INRHYTHMU, Kafka, learning and growth, NiFi, ux, Web Development, web engineering

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