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Troubleshooting Apache Kafka: Techniques with Python Flask & OpenTelemetry

With the prevailing shift towards real-time data processing, Apache Kafka has emerged as a cornerstone of many modern application architectures. Its power and versatility have made Kafka, a widely used data-streaming platform. But, no technology is without its share of challenges, and Kafka is no different. This blog post aims to explore the common pitfalls developers face with Kafka and offer proven troubleshooting techniques to resolve them. We’d round up with a live demonstration of how to connect, consume, and debug Kafka using a Python Flask app.

Kafka and its Challenges

Apache Kafka is a distributed event streaming platform capable of handling trillions of events daily. Its high-throughput nature makes Kafka a popular choice for real-time analytics and data processing tasks. Nevertheless, Kafka’s wide-ranging capabilities bring a set of complexities that developers often struggle with. These problems include difficulty troubleshooting issues, complex architecture, resource management, etc.

Troubleshooting Kafka: Techniques to Tackle the Challenges

Explicit knowledge of the challenges is the first step towards better management. The real effort, however, is in overcoming these challenges. Here, we break down some tried-and-tested troubleshooting strategies for Kafka.

Connecting, Consuming and Debugging Kafka using Python Flask

Python Flask, a lightweight Web Server Gateway Interface (WSGI) web application framework, is perfect for creating smaller scale applications. Leveraging Flask applications with a Kafka backend yields significant results. In an interesting live demonstration, we will highlight how to connect to a Kafka server, consume the streaming data, and debug common issues using Python Flask.

OpenTelemetry for Kafka: Extra Visibility

OpenTelemetry serves as an observability framework that yields crucial telemetry data for debugging and tracing. A brief discussion on this would provide understanding on how integrating OpenTelemetry can give you additional visibility into your Kafka-based workflows and help in better problem-solving.

Conclusion

In the field of real-time data processing, understanding Kafka’s quirks is critical for ensuring reliable deployments. Through this blog post, we aim not just to shine a light on Kafka’s problematic areas but to equip you with an arsenal of techniques to combat these challenges.

By providing a live demonstration of how Python Flask can interact with Kafka and discussing the role of OpenTelemetry in gaining additional visibility, we aspire to foster a better understanding of Kafka. The goal is to realize its full potential and apply it effectively to your next data streaming project.

Tags: #ApacheKafka, #PythonFlask, #OpenTelemetry, #TroubleshootingKafka

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Guide to Kafka Summit London 2023: Sessions, Networking, and More

As we inch closer to the next big event in the world of data – the Kafka Summit London 2023, it’s time to get our gears rolling! With five parallel tracks of intriguing sessions and a plethora of professionals from diverse industries, the event promises to be power-packed. This blog post will guide you through the different sessions, festivities, community activities, and more, to ensure you make the most out of your Kafka Summit experience.

How to Choose the Ideal Sessions?

We understand how overwhelming the event’s exhaustive schedule can be for attendees. To make things easier, here are some pointers that might help streamline your thought process:

Identify What Interests You – Reflect on your preferences and areas of interest related to Kafka.
Sector Specific Interests? – Are there any industries or companies you’re curious about? Companies like Mercedes-Benz, FREENOW, and Michelin will be sharing their insights at the summit.
Get Electrified with Lightning Talks – Keep room for the stimulating lightning sessions. If you’re one to savour crisp content packed in short time frames, these sessions are meant for you!

Need more help? Kick-start your Kafka Summit itinerary with some of the sessions that particularly caught my attention:

Apache Flink on Kafka: Reliable Data Pipelines Everyone Can Code – presented by Ela Demir
A Practical Guide to End-to-End Tracing in Event-Driven Architectures – shared by Roman Kolesnev
You’ve Got Mail! – led by Michael van der Haven and Chris Egerton
Exactly-Once, Again: Adding EOS Support for Kafka Connect Source Connectors

Please refer to our full agenda for more details.

Beyond the Sessions

Summit experience isn’t limited to just attending sessions; here are a few bonus activities to look forward to:

Pac-Man Rule – Make networking smoother by constricting to our ‘Pac-Man’ rule, ensuring inclusion for everyone attending the event.
Unofficial Kafka Summit 5K Fun Run – Break a sweat in our unofficial 5K run! For more details, watch out for the ‘Fun Run’ section in the agenda.
Community Meetup Hub and Birds-of-a-Feather Luncheons – Share ideas, experiences and form connections at the Community Meetup Hub.
Kafka Fundamentals Course – Want to learn more about Kafka? Don’t forget to sign up for the course on our registration page.
Kafka Summit Party – Relax and unwind with your fellow participants after a day full of learning.

Don’t forget to share moments from your Kafka Summit experience on social media with the hashtags #KafkaSummit and #StreamingSelfie.

Ticked with excitement? Then why wait? Register for Kafka Summit London today.

Related Blogs

Confluent Public Sector Summit Demonstrates Government’s Passion for Data – Nov 6, 2023
Public Sector Summit to Feature Data Innovators Sharing Best Practices – Oct 16, 2023

Conclusion

Anticipation is running high as we gear up for the Kafka Summit London 2023. Whether you’re a seasoned Kafka expert or a curious newcomer, this event is your chance to delve deeper into the world of Kafka, form valuable connections, and most importantly, have fun! See you there!

Tags: KafkaSummit, Apache Kafka, Data Streaming, Event

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Cloudflare’s Effective Use of Apache Kafka & Connector Framework for Streamlined & Simplified Data Processing

Cloudflare, a leading internet security, CDN, and DNS provider, faced several challenges with their growing business needs. In this blog post, we will discuss how Apache Kafka emerged as an effective solution for various issues and how the team formed a Connector Framework to streamline the data flow.

Cloudflare’s Operational Challenges

As business requirements expanded, ensuring the operation of both public and private clouds and managing the interconnection between teams became a daunting task for Cloudflare. Matthew Boyle, who leads the team, recognized that implementing the message bus pattern would serve to systematize and harmonize operations.

Choosing Apache Kafka

After evaluating various options, Apache Kafka was identified as an efficient implementation of the message bus pattern. Apache Kafka, an open-source stream-processing software, facilitates handling of real-time data and works particularly well for big data and transactional applications. It offers high-throughput capabilities and is specifically designed to handle real-time data feeds.

Building a Connector Framework

With the increasing adoption of Apache Kafka by various teams across Cloudflare, the need to develop a Connector Framework became evident. Consequently, a universal Connector Framework was designed to simplify the streaming of data between Apache Kafka and other systems while transforming the messages in the process. This facilitated easier integration and communication across different teams.

The Role of JSON and Protobuf

JSON, a widely accepted data interchange format, and Protobuf, a Google-developed language-neutral, platform-neutral mechanism for serializing structured data, have played significant roles in enhancing the performance and interoperability of Apache Kafka at Cloudflare.

Key Lessons Learned

Andrea Medda, a notable figure at Cloudflare, distilled some valuable lessons from their experience with Apache Kafka. They included:

The importance of balancing between highly configurable and simple standardized methods when providing developer tooling for Apache Kafka.
Selecting a straightforward and strict 1:1 contract interface to ensure maximum visibility into the workings of topics and their usage.
Investing in metrics on development tooling to identify problems easily and promptly.
Prioritizing clear, accessible documentation to facilitate consistent adoption and use of Apache Kafka among application developers.

Gaia: A New Internal Product

Matthew Boyle introduced a new internal product, Gaia, that allows one-click creation of services based on Cloudflare’s best practices. Gaia is expected to further streamline the management of services and accelerate development efforts.

About the Author

This blog post is authored by Nsikan Essien, an Engineering Manager at Field Energy best known for his interest in cloud architectures, platform services, and effective team management. Nsikan is based in London.

Tags: #Cloudflare #ApacheKafka #ConnectorFramework #Gaia

Acknowledgement: This blog post is based on the experiences and insights shared by Andrea Medda and Matthew Boyle at Cloudflare.

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JMS vs Apache Kafka: A Detailed Comparison for Better Message Brokering Choices

Last Updated: September 20, 2023

Message brokers have become an integral part of modern-day distributed computing architecture, thanks to their indispensable role in ensuring seamless communication and data transfer among different applications. At the core of this discourse, we often find two major platforms: Java Message Service (JMS) and Apache Kafka. The objective of this article is to offer a comparative analysis of these two platforms, to guide developers in making the best selection based on their unique project needs.

Introduction to Message Brokers

Message brokers can be understood as software systems or components that aid in the transmission of messages between different applications across a distributed system. They serve an intermediary function, taking charge of efficient and reliable delivery of messages from senders to receivers. Message brokers enable asynchronous communication, decoupling sender and receiver systems, and guaranteeing that messages are processed in a scalable and fault-tolerant manner.

Getting to Know Apache Kafka

Apache Kafka is a distributed streaming platform designed to facilitate messaging between different points in a system. It maintains a stream of records in a cluster of servers, providing a robust logging mechanism for distributed systems. Kafka allows users to publish and subscribe to streams of records, process records in real-time and store streams of records. This platform is excellent for creating streaming data applications and pipelines.

Discovering JMS: Java Message Service

Java Message Service, commonly referred to as JMS, is an Application Programming Interface (API) designed to facilitate communication between Java applications and other software components. JMS supports predefined messaging protocols, catering to the Java programming language. This messaging standard enables users to create, send, receive, and read messages between computers within a network. JMS allows developers to make software applications written in different programming languages communicate with each other.

Apache Kafka and JMS: Spotting the Similarities

Despite distinct design and architecture, Kafka and JMS share certain similarities, including:

Function as messaging middleware solutions
Existence of message brokers
Support for common messaging patterns
Capability to integrate with different programming languages and frameworks
Scalability to handle increased message volumes
Acknowledgment mechanisms

JMS and Kafka: Spotting the Differences

Despite these similarities, JMS and Kafka differ significantly in several ways, including:

Programming Style: JMS follows an imperative programming style while Kafka adopts a reactive style.
Content Segregation: JMS separates content using queues and topics, while Kafka uses topics for this purpose.
Message Format: JMS typically deals with messages in text or binary format, while Kafka supports messages in various formats.
Filtering Method: JMS provides message selectors for filtering messages, while Kafka offers robust filtering capabilities through Kafka Streams or consumer group subscriptions.
Routing System: JMS offers both point-to-point and publish-subscribe routing mechanisms, while Kafka employs a publish-subscribe model with topic-based routing.
Message Storage: JMS typically does not retain messages beyond their delivery, while Kafka provides durable message storage with configurable retention periods.

Making the Choice between JMS and Kafka

The preference between these two platforms depends on various parameters, including the use case, the necessity of scalability, the importance of message persistence, the preferred programming paradigm, and integration requirements. Your choice between JMS and Kafka should be influenced by your project’s specific needs and goals.

Conclusion: JMS and Kafka – Unique in Their Ways

In conclusion, the decision between JMS and Kafka is contingent on your specific needs and objectives. If your project demands a well-structured, predictable and ordered messaging service, JMS could be your go-to choice. Conversely, if your applications necessitate real-time data streams, processing large data volumes in a dynamic, event-driven environment, then Kafka seems to fit the bill. Regardless of your choice, both JMS and Kafka serve as reliable conduits for facilitating seamless communication between your applications.

Authors: Ritvik Gupta

Tags: #JMS #ApacheKafka #MessageBrokers #DistributedSystems

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Kafka’s Revolutionary Leap: Transitioning from ZooKeeper to KRaft for Enhanced Real-Time Data Processing

In the realm of real-time data processing, Kafka, developed by Confluent, has garnered a stronghold with a sprawling presence in over 150,000 organizations. However, with rapidly growing data and throughput requirements, the Kafka platform has been facing the heat, primarily due to its dependence on Apache Zookeeper for managing its crucial system metadata. On the quest for a more nimble solution, the architecture now embarks on a transformational journey from Zookeeper to KRaft.

The Achilles Heel: Apache Zookeeper

Where does the problem lie? Critics have identified a significant part of the problem in how Zookeeper operates. According to the Java expertise site Baeldung, ZooKeeper functions entirely independently of Kafka, which exacerbates the system admin’s management dilemmas. It also retards the system’s overall responsiveness.

Distinctively, other distributed systems, like Elasticsearch, have internalized the synchronization aspect. Kafka, however, is unable to monitor the event log and this results in a lag between the controller memory and the ZooKeeper’s state.

As explained by Colin McCabe from Confluent, ZooKeeper stores metadata about the system itself, such as information about partitions. Over time, the number of partitions that users manage has significantly increased, causing a lag in the system’s responsiveness. When a new controller is elected, the partition metadata fed to the nodes also takes more time, slowing down the entire system.

Dissolving the Dependence: The Advent of KRaft

The solution comes in the form of KRaft. Kafka deployments can now maintain hot standbys with KRaft, eliminating the need for a controller to load all the partition data. Underpinning Kafka’s architecture, KRaft is based on a stream metaphor that houses an inflow of changes. This makes it possible to monitor the stream, identify the current position, and effectively catch up if there’s any lag.

The exploration doesn’t end here. Looking to minimize metadata divergence, the idea is to manage metadata itself through this stream process. In simpler terms, a log will be employed to manage streaming changes to the metadata. This ensures a clear ordering of events and the maintenance of a single timeline.

The outcome? KRaft has successfully managed to lower the latency of metadata reads by a factor of 14, meaning that Kafka can recover 14 times faster from any problem. The platform can now store and maintain up-to-date metadata on as many as 2 million partitions.

Stepping Stones: Towards Full KRaft Implementation

The maiden steps to KRaft implementation have been made with Kafka 3.3, but the journey towards fully ditching Zookeeper is a measured one, expected to culminate with version 4 release. By then, users still reliant on ZooKeeper will have to transition to a Bridge Release.

KIP-833, designating Kafka 3.5 as a bridge release, facilitates the migration from ZooKeeper without downtime. The upgrade process involves accentuating new controller nodes and adding functionality to the existing ones. The new KRaft controller will lead the ZooKeeper nodes.

As explained by McCabe, the system will run on the old mode for a while during the transition, allowing for gradual enrollment of brokers. When all brokers are in KRaft mode, the system will function in dual write mode, making it easier to revert to ZooKeeper if required.

With widespread expectations of enhanced performance and streamlined management, the move from ZooKeeper to KRaft is indeed a significant milestone in Kafka’s evolution. The glowing prospects of Confluent’s Kafka are indeed heartening to observe.

Tags: #Kafka, #Confluent, #ZooKeeper, #KRaft, #RealTimeProcessing

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Enhancing Stream Processing with Apache Kafka in Kestra Application Development

Apache Kafka is a revered name in the realm of distributed event store and stream-processing platforms. It is highly recognized for its robust functionality in handling voluminous data at a compelling speed. To further augment Kafka’s capabilities, there’s Kafka Streams – intricately designed to simplify the creation of data pipelines and conduct high-level operations like aggregation and joining.

In this blog, we will dive deep into understanding the nuances of working with Kafka while building Kestra and leveraging its strengths in stream processing, navigating through its limitations, and customizing it to suit our specific requirements.

Why Apache Kafka?

Faced with the challenge of choosing a persistent queue for our application without any additional dependencies, we crossed paths with numerous candidates like RabbitMQ, Apache Pulsar, Redis, etc. However, Apache Kafka was the one that stood out, efficiently catering to all our project needs.

One major advantage of Kafka is that it allows us to embed the application directly within our Java application, removing the need for managing a separate platform, quite literally taking microservices to the next level.

Working with Kafka Topics

Kafka comes with its own set of constraints as it isn’t a database. It may seem confusing at first to use the same Kafka topic for source and destination.

Consider this example of a topology, which has the topic as the source, some branching logic, and two separate processes writing to the same destination. Here, the risk of overwriting the previous value becomes evident, ultimately resulting in data loss.

The Custom Joiner for Kafka Streams

To combat this issue, we came up with a customized joiner for Kafka Streams. This alternative was designed to process the executions and split the microservice into multiple topics such as:

A topic with the executions (multiple tasks)
A topic with task results

Our custom joiner needed to manually create a store, incorporate merge function, and get the last value. This ensured that regardless of the number of task results entering in parallel, the execution state is always the latest version.

Dealing with Distributed Workload Between Multiple Backends

In our application, Kestra, a scheduler with scheduled execution or long-polling mechanism (detecting files on S3 or SFTP) looks up all flows. To avoid a single point of failure on this service, we needed to split the flows between all instances of schedulers.

We did it by relying on Kafka’s consumer groups that handled complexities of a distributed system for us. Kafka undertakes all the heavy parts of the distributed systems. In case of a thousand flows, every consumer will have ~500 flows, thanks to Kafka’s phenomenal handling of:

Heartbeat to detect consumer failure
Notifications for rebalancing
Ensuring exactly-once semantic for a topic

Monitoring and Debugging

While working with Kafka streams, monitoring and debugging can be a real challenge due to the lag in streams. To alleviate this, we chose to deal with only one topic at a specific time.

This approach helped us minimize network transit and group all streams by source topics.

Throughout this process, we learned some notable tips that helped us navigate our challenges. We were able to adapt our code efficiently to Kafka and make it work well for our use case.

In the end, the experiences and learnings derived from working closely with Apache Kafka and Kestra have been immensely rewarding. If you’re interested in our work and want to learn more, you can find us on GitHub, Twitter, or join our discussions on Slack.

Message us if you found this article helpful or if you have any questions about Apache Kafka.

Tags: #ApacheKafka #Kestra #StreamProcessing #Microservices

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Maximizing Real-Time Streaming with Apache Kafka Consumer Groups

Apache Kafka is an open source distributed event streaming platform, giving teams power and precision in handling real-time data. Understanding the ins and outs of Kafka and its concepts, such as consumer groups, can help organizations harness the full potential of their real-time streaming applications and services.

Understanding Kafka Consumers and Consumer Groups

Kafka consumers are typically arranged within a consumer group, comprising multiple consumers. This design allows Kafka to process messages in parallel, providing notable processing speed and efficiency.

Despite this, a lone consumer can read all messages from a topic independently, or doubly, several consumer groups are capable of reading from a single Kafka topic. The setup largely relies on your specific requirements and use case.

Distributing Messages to Kafka Consumer Groups

Kafka uses an organized system of distributing messages. Topics in Kafka include partitions for this precise purpose.

Given a consumer group with a singular consumer, it will get messages from all partitions of a topic:

In the case of a consumer group with two consumers, each will receive messages from half of the topic partitions:

Consumer groups make a point to balance their consumers across partitions until the 1:1 ratio is satisfied:

However, if there are more consumers compared to partitions, any surplus consumers will not receive messages:

Exploring Consumer Group IDs, Offsets, and Commits

Each consumer group features a unique group identifier, known as a group ID. Consumers configured with various group IDs essentially belong to different groups. And instead of an explicit method keeping track of reading messages, a Kafka consumer employs an offset – referring to each message’s position in the queue that is read.

Users are given the choice to store these offsets by themselves, or Kafka can manage them. If Kafka handles it, the consumer will publish them to a unique internal topic named __consumer_offsets.

Consumer Dynamics in a Kafka Consumer Group

A new consumer within a Kafka consumer group will look for the most recent offset and join the action, consuming the messages that were formerly assigned to a different consumer. The same occurs if a consumer leaves the group or crashes – a remaining consumer will cover its tasks and consume from the partitions previously assigned to the absent consumer.

This effectively helpful process is called “rebalancing” and can be triggered under a variety of circumstances, providing a fluid system designed to ensure maximum efficiency.

In Conclusion

Understanding Kafka’s method of data streaming down to its internal systems, such as consumer groups, is crucial for any organizations looking to leverage its power. By utilizing Apache Kafka’s sophisticated design, they can ensure maximum efficiency in real-time streaming applications and services for their operations.

Tags: #ApacheKafka #ConsumerGroups #BigData #DataStreaming

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Efficient Stream Processing with Apache Kafka, Apache Flink in Confluent Cloud

In today’s vast digital landscape, big data concepts have revolutionized the methods we use to handle, process and analyze information. Streams of data generated every second provides invaluable insights about various aspects of our online lives. Apache Kafka and Apache Flink are two major contributors in this realm. Confluent, which offers a fully managed streaming service based on Apache Kafka, embraces the advantages of Kafka in unison with the capabilities of Apache Flink.

Apache Flink on Confluent Cloud

Recently, Apache Flink is made available on Confluent Cloud, initially for preview in select regions on AWS. Flink is re-architected as a cloud-native service on the Confluent Cloud which further enhances the capabilities offered by this platform.

Event-Driven Architectures with Confluent and AWS Lambda

When adopting the event-driven architectures in AWS Lambda, integrating Confluent can provide multiple benefits. To get the most out of this combination, understanding the best practices are crucial.

To Be Continued…

Tags: #ApacheKafka, #ApacheFlink, #ConfluentCloud, #StreamProcessing

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