Crash course on Apache Flink
🎬S3:E08 Everyone seems to be using it these days
Hey, TechFlixers!
In the current tech race, companies are desperate to turn data into real-time decisions and personalized experiences. The need for scalable, low-latency data processing is at an all-time high. Apache Flink stands out as the go-to solution for these demands.
This episode highlights Airbnb's smart move to migrate their Flink architecture to Kubernetes, enhancing their developer experience and cutting costs. We'll also break down the essentials of Apache Flink, making sense of its powerful features and real-world uses. Let's jump in and explore how Flink is revolutionizing data processing!
🔦 Spotlight
📢 Airbnb: Apache Flink on Kubernetes
Airbnb migrated its Apache Flink streaming processing architecture from Hadoop Yarn to Kubernetes, resulting in a more streamlined and user-friendly experience for developers.
The new system provides standardized version control and continuous delivery, with features such as stopping jobs with savepoints and querying completed checkpoints on Amazon S3.
Each Flink job is deployed independently on Kubernetes with its own namespace and uses Zookeeper, ETCD, and Amazon S3 for fault tolerance and state storage.
This has led to improved developer velocity, increased job availability, reduced latency, and cost savings in infrastructure.
Future work includes improving job availability, enabling job autoscaling, and utilizing the Flink Kubernetes Operator for streamlined operation and deployment processes.
🚀 Power Up
Navigating the Apache Flink Universe
If you understood nothing from the above spotlight section, fear not. Here, we cover everything you need to know about Apache Flink and all the technical terms encountered.
What the Heck is Apache Flink?
It is a distributed processing engine for data streams. It's perfect for real-time analytics, complex event processing, and building data pipelines. Flink provides high throughput, low latency, and exactly-once state consistency, making it a powerhouse for live data applications.
After reading the above jargon, you might have some questions like this—
What the heck is a distributed processing engine?
A system designed to process data across multiple machines (nodes) simultaneously. This means that tasks like data analysis, transformation, or computation are split up and run in parallel across a network of computers, making the process much faster and more efficient than running on a single machine.What the heck is “exactly-once” state consistency?
It refers to a guarantee in data processing where an operation is processed only once, even in the face of failures. This is crucial in streaming applications, where data is continuously ingested and processed. If a system crashes or encounters an error, exactly-once consistency ensures that, upon recovery, no data is duplicated or lost.Where the heck is Apache Flink actually used?
Financial Services: For fraud detection and risk management by analyzing transactions in real time.
E-commerce: To provide real-time recommendations and personalized experiences based on user behavior.
Telecommunications: For monitoring network traffic and detecting anomalies or service issues instantly.
Social Media: To process large volumes of streaming data from user interactions, likes, shares, and comments.
IoT (Internet of Things): To analyze data from sensors and devices, enabling real-time insights and actions, such as predictive maintenance.
Flink vs Spark (in case you wondered)
Real-Time Focus: Flink excels at low-latency stream processing. Spark, while capable of streaming, is traditionally stronger in batch processing.
State Management: Flink offers advanced state management, crucial for applications that need to keep track of complex states over time.
Processing Models: Flink natively supports stream processing with batch as a special case, while Spark treats streaming as micro-batch processing.
Hadoop YARN and Airflow in a Nutshell
Hadoop YARN (Yet Another Resource Negotiator) manages resources in a Hadoop cluster, assigning computational resources to various applications. When Flink jobs run on YARN, it handles resource allocation, ensuring that Flink jobs get the required computational power.
Airflow is a workflow scheduler that automates the execution of tasks, manages dependencies, and ensures tasks run in the correct order. We will explore more about it in a future episode.
Keywords to know
Data Streams: Continuous flow of data, which Flink processes in real-time.
State Management: Flink's ability to maintain and manage state across data streams is critical for complex event processing.
Windowing: A technique to group data streams based on time or count, enabling aggregation and analysis over specific periods or amounts.
Checkpointing: A mechanism to provide fault tolerance, saving the state of a streaming application at regular intervals.
Event Time vs Processing Time: Event Time refers to the time when an event occurred, while Processing Time is when the event is processed. Flink primarily operates on Event Time for accuracy in processing.
Flink Clusters: Consists of a JobManager and TaskManagers, where JobManager coordinates the distributed execution, and TaskManagers handle the actual computation.
Building Blocks of Flink
Data Sources: These are the entry points where data comes into Flink. It can be from a message queue like Kafka, a file system, a database, or other data storage systems.
DataStream and DataSet APIs: Flink provides two primary APIs:
DataStream API for processing infinite streams of data.
DataSet API for processing finite data sets (batch processing).
Transformations: These are operations applied to the data to transform it. Examples include map, filter, reduce, and window operations.
State Management: Flink can manage the state across events, which is crucial for applications like counting, aggregating, or maintaining a session window.
Sinks: The final destination where processed data is output, such as databases, file systems, or another message queue.
Running a Flink Job
A Flink job is a program written to process data streams using Apache Flink's APIs. It defines how data is ingested, processed, and outputted. These jobs are designed to run in a distributed environment, taking advantage of Flink's powerful stream processing capabilities.
Here’s how they are run:
Set Up Flink Cluster: Flink jobs can run in various modes:
Local Mode: For development and testing on a local machine.
Standalone Cluster: A self-managed Flink cluster.
Cluster on YARN or Kubernetes: For running Flink jobs in a distributed environment.
Deploy the Job: Once the cluster is set up, deploy the job using the command line or a web interface.
Real-world examples to explore further
That's a wrap for this episode! We've explored Apache Flink and its impact on real-time data processing. Whether you're implementing real-time fraud detection, boosting e-commerce recommendations, monitoring IoT data, or any such use case where you want to take a data stream and do something with it in real-time, Flink's stateful computations and low-latency capabilities make it a standout choice for the same.