
Coding Blocks
Intro to Apache Kafka
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Quick takeaways
- Apache Kafka enables high-performance data pipelines and streaming analytics for mission-critical applications.
- Kafka's core capabilities include high throughput, scalability, permanent storage, and high availability for distributed event streaming.
- Kafka ecosystem offers built-in stream processing and connects to various event sources and sinks for data integration.
- Transition away from Zookeeper in Kafka configuration signals a shift to more modern and efficient cluster management alternatives.
Deep dives
Examples of Use Cases for Kafka
Kafka is used for processing payments and financial transactions in real time, IoT devices, tracking automobiles and shipments in real time, connecting and sharing data across different divisions of a company, and even Amazon deliveries for real-time tracking.
The Evolution of the Kafka Platform
Kafka is a distributed system composed of servers and clients communicating using a high-performance TCP protocol. The servers include a cluster of one or more servers running in various data centers or cloud regions. The brokers within Kafka are responsible for storage, data persistence, and scaling out to handle large volumes of data.
Kafka Connect Servers and Single Message Transforms (SMTs)
Kafka Connect servers allow for importing and exporting data from Kafka topics. They offer optional configurations for single message transforms (SMTs) that enable data transformations and modifications such as field selection, data extraction, formatting, snapshotting, and more. These configurations are declarative and can be version controlled.
Usage of Zookeeper in Kafka
Zookeeper is no longer the default configuration in Kafka, signaling a shift in the Kafka platform. The move away from zookeeper has been a slow process over the years, making way for more modern and efficient alternatives for managing Kafka clusters. While zookeeper was a standard for ensuring distributed coordination, the new configurations offer improved performance and scalability without zookeeper.
Kafka Kubernetes Scaling with Strimzi
Using Kafka in Kubernetes with Strimzi can make scaling down your cluster difficult. Strimzi doesn't allow easy scaling down and prefers to keep the cluster running. By setting the 'pause reconciliation' annotation on the cluster to 'true' or removing it, you can inform Strimzi to pause reconciliation, enabling you to pause the cluster's functionality. To shut down a Kafka cluster, use specific commands to manage the Strimzi Podsets and avoid immediate restarts by Strimzi.
Wi-Fi Signal Monitoring with Android App
Consider using a Wi-Fi analyzer app on Android to monitor Wi-Fi signals in your home. This app allows you to detect signal strength, identify the best channels for minimal interference, and locate weak spots in your Wi-Fi coverage. By analyzing signal strength using this app, you can fine-tune your Wi-Fi setup for optimal coverage and performance.
Local Router for Cloud Bypass in Networking
To avoid cloud-based management of your network and instead use a local device, try a router that allows you to bypass the cloud for network management. By using this device, you can ensure that all network routing and management is done on-premises, enhancing security and control over your network infrastructure.
Kafka Cluster Shutdown and Reconciliation with Strimzi
For managing Kafka clusters in Kubernetes with Strimzi, utilize annotations to control the shutdown and reconciliation processes. By setting specific annotations such as 'pause reconciliation=true' or removing them, you can control how Strimzi responds to cluster changes and allow for effective maintenance procedures while ensuring cluster stability and operation.


We finally start talking about Apache Kafka! Also, Allen is getting acquainted with Aesop, Outlaw is killing clusters, and Joe was paying attention in drama class.
The full show notes are available on the website at https://www.codingblocks.net/episode235
News
- Atlanta Dev Con is coming up, on September 7th, 2024 (www.atldevcon.com)
Intro to Apache Kafka
What is it?
Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
Core capabilities
- High throughput – Deliver messages at network-limited throughput using a cluster of machines with latencies as low as 2ms.
- Scalable – Scale production clusters up to a thousand brokers, trillions of messages per day, petabytes of data, and hundreds of thousands of partitions. Elastically expand and contract storage and processing
- Permanent storage – Store streams of data safely in a distributed, durable, fault-tolerant cluster.
- High availability – Stretch clusters efficiently over availability zones or connect separate clusters across geographic regions.
Ecosystem
- Built-in stream processing – Process streams of events with joins, aggregations, filters, transformations, and more, using event-time and exactly-once processing.
- Connect to almost anything – Kafka’s out-of-the-box Connect interface integrates with hundreds of event sources and event sinks including Postgres, JMS, Elasticsearch, AWS S3, and more.
- Client libraries – Read, write, and process streams of events in a vast array of programming languages
- Large ecosystem of open source tools – Large ecosystem of open source tools: Leverage a vast array of community-driven tooling.
Trust and Ease of Use
- Mission critical – Support mission-critical use cases with guaranteed ordering, zero message loss, and efficient exactly-once processing.
- Trusted by thousands of organizations – Thousands of organizations use Kafka, from internet giants to car manufacturers to stock exchanges. More than 5 million unique lifetime downloads.
- Vast user community – Kafka is one of the five most active projects of the Apache Software Foundation, with hundreds of meetups around the world.
What is it?
- Getting data in real-time from event sources like databases, sensors, mobile devices, cloud services, applications, etc. in the form of streams of events. Those events are stored “durably” (in Kafka) for processing, either in real-time or retrospectively, and then routed to various destinations depending on your needs. It’s this continuous flow and processing of data that is known as “streaming data”
How can it be used? (some examples) - Processing payments and financial transactions in real-time
- Tracking automobiles and shipments in real time for logistical purposes
- Capture and analyze sensor data from IoT devices or other equipment
- To connect and share data from different divisions in a company
Apache Kafka as an event streaming platform?
- It contains three key capabilities that make it a complete streaming platform
- Can publish and subscribe to streams of events
- Can store streams of events durably and reliably for as long as necessary (infinitely if you have the storage)
- To process streams of events in real-time or retrospectively
- Can be deployed to bare metal, virtual machines or to containers on-prem or in the cloud
- Can be run self-managed or via various cloud providers as a managed service
How does Kafka work?
- A distributed system that’s composed of servers and clients that communicate using a highly performant TCP protocol
Servers
- Kafka runs as a cluster of one or more servers that can span multiple data centers or cloud regions
- Brokers – these are a portion of the servers that are the storage layer
- Kafka Connect – these are servers that constantly import and export data from existing systems in your infrastructure such as relational databases
- Kafka clusters are highly scalable and fault-tolerant
Clients
- Allows you to write distributed applications that allow to read, write and process streams of events in parallel that are fault-tolerant and scale
- These clients are available in many programming languages – both the ones provided by the core platform as well as 3rd party clients
Concepts
Events
- It’s a record of something that happened – also called a “record” in the documentation
- Has a key
- Has a value
- Has an event timestamp
- Can have additional metadata
Producers and Consumers
- Producers – these are the client applications that publish/write events to Kafka
- Consumers – these are the client applications that read/subscribe to events from Kafka
- Producers and consumers are completely decoupled from each other
Topics
- Events are stored in topics
- Topics are like folders on a file system – events would be the equivalent of files within that folder
- Topics are mutli-producer and multi-subscriber
- There can be zero, one or many producers or subscribers to a topic that write to or read from that topic respectively
- Unlike many message queuing systems, these events can be read from as many times as necessary because they are not deleted after being consumed
- Deleting of messages is handled on a per topic configuration that determines how long events are retained
- Kafka’s performance is not dependent on the amount of data nor the duration of time data is stored, so storing for longer periods is not a problem
Resources we Like
- Why Strimzi moved away from statefulsets (github.com)
Tip of the Week
- Flipper Zero is a multi-functional interaction device mixed with a Tamagotchi. It has a variety of IO options built in, RFID, NFC, GPIO, Bluetooth, USB, and a variety of low-voltage pins like you’d see on an Arduino. Using the device upgrades the dolphin, encouraging you to try new things…and it’s all open-source with a vibrant community behind it. (shop.flipperzero.one)
- Kafka Tui?! Kaskade is a cool-looking Kafka TUI that has got to be better than using the scripts in the build folder that comes with Kafka. (github.com/sauljabin/kaskade)
- Microstudio is a web-based integrated development environment for making simple games and it’s open source! (microstudio.dev)
- Bing Copilot has a number of useful prompts (bing.com)
- Designer (photos)
- Vacation Planner
- Cooking assistant
- Fitness trainer
- Sharing metrics between projects in GCP, Azure, and maybe AWS???
- GCP (projects): (cloud.google.com)
- Azure (resource groups or subscriptions): (learn.microsoft.com)
- AWS (multiple accounts): (docs.aws.amazon.com)
- Checking wifi in your home – Android Only (play.google.com)
- Powering POE without running cables (Amazon)
- Omada specific – cloud vs local hardware (Amazon)
- How to “shutdown” a Kafka cluster in Kubernetes:
kubectl annotate kafka my-kafka-cluster strimzi.io/pause-reconciliation="true" --context=my-context --namespace=my-namespace
kubectl delete strimzipodsets my-kafka-cluster --context=my-context --namespace=my-namespace
- Then to “restart” the cluster:
kubectl annotate kafka my-kafka-cluster strimzi.io/pause-reconciliation- --context=my-context --namespace=my-namespace