How to "Do" Stream Processing with Apache Flink®

A Beginner's Guide to Real-Time Data Processing

Using Ververica's Unified Streaming Data Platform enhances this by providing enterprise-grade capabilities for simplified deployment, monitoring, and scaling. Ververica is powered by VERA, the engine that revolutionizes Flink by boosting Flink's performance. In addition, the Ververica ecosystem includes Apache Paimon, which enables real-time updates on data lakes, and Apache Fluss, which provides unified, low-latency storage for analytical queries on streaming data. 

Whether from website clicks or sensor readings, financial transactions or social media updates, data is constantly being generated. To meet modern business demands, the ability to process this continuous stream of information in real time and extract immediate insights has become a necessity. This is where stream processing comes in and Apache Flink stands as a leading stream processing framework at its forefront.

This guide will introduce you to Apache Flink, explain how to do stream processing with Apache Flink, and highlight the key differences and advantages of using Ververica's Unified Streaming Data Platform enterprise-grade capabilities, including the VERA engine, Apache Paimon, and Apache Fluss, to build a robust real-time data and stream processing solution.

What is Apache Flink?

At its core, Apache Flink is an open-source, distributed stream processing framework designed for high-performing, fault-tolerant, and stateful computations over unbounded (continuous) and bounded (batch) data streams. Unlike traditional systems that process data in fixed chunks or "batches," Flink processes data as it arrives, enabling real-time responses to dynamic events.

Here's what makes Apache Flink a unique and powerful choice for data stream processing:

• Unified Batch and Stream Processing: Flink famously treats batch data as a finite stream. This "stream-first" approach means you can use the same APIs and programming model for both real-time streams and historical datasets, simplifying development and maintenance.
• Stateful Computations: Many real-world applications need to remember information about past events to process current ones (e.g., counting unique users over time, tracking session activity). Flink offers robust, fault-tolerant state management, ensuring that your application's memory of past events is reliable, even if something goes wrong.
• Fault Tolerance: Flink guarantees that your data processing will be accurate even in the face of machine failures. It achieves this through a mechanism called "checkpointing," which periodically saves the state of your application. If a failure occurs, the job can restart from the last successful checkpoint, ensuring exactly-once processing semantics, meaning each event is processed precisely once, avoiding duplicates or data loss.
• Event Time Processing: Data events often don't arrive in the exact order they occurred. Flink's "event time" processing and "watermarks" allow it to correctly process events based on their actual occurrence time, even if they arrive late or out of order, which is critical for accurate real-time analytics.

Introduction to Apache Flink Stream Processing (for Beginners)

To understand how to "do" stream processing with Apache Flink, let's look at its basic building blocks.

At the most simplistic level, a Flink stream processing application consists of three main parts:

1) Sources: These are where data streams originate. Flink can connect to a wide variety of data sources that produce continuous streams, such as:

• Message Queues: Apache Kafka, Apache Pulsar, AWS Kinesis.
• Databases: Using Change Data Capture (CDC) connectors to read real-time changes from databases like MySQL, PostgreSQL, or Oracle.
• File Systems: Reading new files as they appear in directories.

2) Transformations: This is the core logic where you process your data. Flink provides a rich set of operators to transform, filter, aggregate, and enrich your data streams. Some common transformations include:

• Map: Apply a function to each individual element in the stream (e.g., convert temperature from Kilograms to Pounds).
• Filter: Selectively keep or discard elements based on a condition (e.g., only process transactions over $10).
• KeyBy: Group elements by a specific key. This is essential for stateful operations or aggregations on specific groups (e.g., count of sales per product ID).
• Windowing: Process data within specific time boundaries.
  • Tumbling Windows: Fixed-size, non-overlapping windows (e.g., sum sales every 5 minutes).
  • Sliding Windows: Fixed-size, overlapping windows (e.g., sum sales over the last 5 minutes, updated every 1 minute).
• Joins: Combine data from two or more streams (e.g., join user click data with user profile data).
• Aggregations: Compute sums, counts, averages, min/max over windows or groups.

3) Sinks: These are where the processed data streams are sent. Flink can write data to various destinations, including:

• Message Queues: Sending processed events back to Kafka or other queues.
• Databases: Updating real-time dashboards or operational databases.
• File Systems/Object Storage: Storing processed data for later analysis (e.g., on S3, HDFS).
• Dashboards/Monitoring Tools: Directly feeding data to visualization tools.

Programming Flink: You can write Apache Flink applications using its DataStream API (in Java or Scala for fine-grained control) or Flink SQL, which allows you to process streams using standard SQL queries, making stream processing more accessible to a wider audience familiar with relational databases.

Apache Flink Use Cases and Best Practices

Apache Flink excels in scenarios where real-time responsiveness, high throughput, and data accuracy are paramount.

Real-World Apache Flink Use Cases:

• Real-Time Fraud Detection: Flink can analyze financial transactions as they happen, using Complex Event Processing (CEP) to detect suspicious patterns (e.g., multiple transactions from different locations within seconds) and trigger immediate alerts or blocks. Ververica provides detailed insights into real-time fraud detection using Flink and CEP.
• Customer 360 & Personalization: By processing customer interactions like clicks, purchases, and browsing history in real-time, Flink helps build a unified, continuously updated view of each customer, enabling personalized recommendations and dynamic pricing. An example of this is seen in real-time feature engineering for e-commerce, like KartShoppe's use case.
• IoT Analytics & Predictive Maintenance: Flink can ingest massive streams of sensor data from industrial machinery or smart devices, detect anomalies, and predict equipment failures before they occur, optimizing maintenance schedules.
• Real-Time ETL (Extract, Transform, Load): Modern ETL pipelines are moving from batch to stream. Flink performs continuous transformations on data as it moves from source to destination, eliminating batch delays and providing fresh data for analytics.
• Security Information and Event Management (SIEM): Flink is used to process security logs in real-time detecting threats, policy violations, and unusual activities as they unfold, enabling immediate incident response.
• Airlines & Logistics: Optimizing operations by correlating real-time events like flight delays, gate changes, and baggage handling. Flink enables real-time insights for airlines with Complex Event Processing.

Best Practices for Apache Flink Applications:

• Understand Event Time: Design your application around event time and watermarks for accurate results, especially with out-of-order data.
• Manage State Efficiently: For stateful jobs, understand how Flink manages state, choose appropriate state backends, and optimize state serialization to ensure performance and fault tolerance.
• Parallelism Tuning: Configure the parallelism of your Flink job to match your available resources and the volume of your data. Too little means bottlenecks, too much can waste resources.
• Monitor Backpressure: Regularly monitor for backpressure indicators, as this signals that parts of your pipeline are slowing down, which impacts latency and throughput.
• Leverage Flink SQL: For simpler transformations and aggregations, Flink SQL often provides a faster development cycle and is more accessible to data analysts.

Open Source Apache Flink vs. Ververica’s Unified Streaming Data Platform Enterprise-Grade Capabilities

While open-source Apache Flink is incredibly powerful and “free” to use, running it at scale in a production enterprise environment comes with its own set of challenges.

Challenges with Open Source Apache Flink:

• Operational Complexity: Deploying, upgrading, scaling, and managing Flink clusters, especially in cloud environments, requires significant DevOps expertise.
• Monitoring & Debugging: Setting up comprehensive monitoring, alerting, and debugging tools for distributed Flink applications can be complex and time-consuming.
• State Management at Scale: While Flink handles state, optimizing its performance for very large, highly concurrent stateful applications can be challenging without deep expertise.
• High Availability & Disaster Recovery: Ensuring seamless failover and disaster recovery in self-managed Flink setups requires careful planning and robust infrastructure.
• Security & Governance: Implementing enterprise-grade security features, access controls, and data governance policies across Flink deployments can be intricate.
• Total Cost of Ownership (TCO): The operational overhead and the need for specialized engineering teams can lead to a higher TCO than initially perceived.

Ververica Platform Capabilities for Stream Processing

Ververica's Unified Streaming Data Platform addresses these challenges head-on, providing an integrated, enterprise-ready solution for stream processing with Apache Flink. In addition to being 100% compatible with open-source Flink, it also transforms the power of Flink into a managed, production-ready offering with features designed to simplify operations and accelerate value, including:

• Application Lifecycle Management: Simplifies the deployment, versioning, and scaling of Flink applications. You can quickly deploy jobs, manage updates, and scale resources up or down with ease.
• Monitoring and Observability: Provides a user-friendly interface for tracking job metrics, resource utilization, and debugging, offering deep insights into your stream processing pipelines.
  
• High Availability and Fault Tolerance: Streamlines the setup and management of resilient Flink clusters, ensuring your critical data stream processing applications are always available.
• Enterprise Security: Offers end-to-end encryption, flexible access control, and integrates with cloud-native security tools, aligning with zero-trust principles.
• Cost Efficiency & Performance at Scale: Complements Flink's high-performance runtime with autoscaling and capacity planning capabilities, ensuring optimal resource utilization and reducing TCO, sometimes reporting 40% lower TCO than alternative Flink solutions as mentioned in the VERA whitepaper.
• Turn-Key Solution: Comes with all required dependencies for cloud and on-premise deployments, providing a highly available stream processing runtime and an integrated development environment for Flink SQL.

Enhancing Performance and Capabilities: VERA, Apache Paimon, and Apache Fluss

Ververica's Unified Streaming Data Platform extends Apache Flink's capabilities even further with innovative components like the VERA engine, Apache Paimon, and Apache Fluss, creating a comprehensive ecosystem for advanced data stream processing.

VERA Engine: Revolutionizing Apache Flink's Performance

The VERA engine (Ververica Runtime Assembly) is Ververica's cloud-native, ultra-high-performance runtime that sits at the core of the Unified Streaming Data Platform. VERA optimizes open-source Apache Flink to deliver unprecedented speed and efficiency:

• Ultra-High Performance: VERA is designed for extreme throughput and low latency, capable of processing billions of events per second with millisecond-level delays. This makes stream processing flink significantly faster than open source Flink and any alternatives.
• Optimized Stateful Computations: VERA includes an advanced state engine ("Gemini") that replaces standard Flink state backends like RocksDB, delivering superior performance in state-intensive, large-scale stream processing environments. This is crucial for applications that manage vast amounts of continuous state.
• Cloud-Native & Elastic Scale: VERA separates compute and storage, allowing for effortless scaling of large stateful applications, ensuring consistent performance even as data volumes fluctuate. 

Apache Paimon: The Stream Native Data Lakehouse

For persisting and querying streaming data efficiently, Apache Paimon is a game-changer. As a stream-native data lakehouse format, Paimon enables Apache Flink to continuously write and update data directly on a cost-effective data lake (like S3), forming the foundation of Ververica's Streamhouse concept.

• Real-Time Updates: Paimon allows Flink jobs to perform real-time upserts and updates on data lake tables, ensuring that analytical queries always reflect the freshest data with low latency.
• Unified Batch and Stream Access: Paimon tables can be accessed by both stream processing jobs (for continuous updates) and batch queries (for historical analysis), creating a single source of truth for all data.
• Cost-Efficient Storage: It leverages affordable object storage while offering data warehouse-like properties (ACID transactions, schema evolution), making large-scale data stream processing more economical. 

Apache Fluss: Unified Streaming Storage for Next-Gen Analytics

Apache Fluss is a groundbreaking unified streaming storage layer designed specifically for high-performance, low-latency analytical access to streaming data. It integrates deeply with Apache Flink to simplify analytical pipelines:

• Sub-Second Latency Analytics: Fluss offers sub-second latency for both writing streaming data and querying it, making it ideal for real-time dashboards and interactive analytics.
• Eliminates Intermediaries: Fluss can replace the need for separate message queues (like Kafka) and traditional OLAP systems for analytical workloads, simplifying your architecture and reducing costs.
• Columnar Reads & Projection Pushdown: Its columnar format and intelligent "projection pushdown" feature optimize read performance, fetching only necessary data and significantly boosting analytical query throughput.

Conclusion

Stream processing with Apache Flink represents the modern approach to handling data, moving beyond the limitations of traditional batch processing to embrace real-time insights and event-driven architecture. For beginners looking to dive into this exciting field, Apache Flink offers a powerful and flexible stream processing framework.

While open-source Flink provides immense capabilities Ververica's Unified Streaming Data Platform transforms it into an enterprise-ready solution, simplifying operations, ensuring reliability, and providing advanced tooling. When combined with the performance acceleration of the VERA engine, the unified streaming storage capabilities of Apache Paimon (for your Streamhouse), and the low-latency analytical power of Apache Fluss, Ververica's Unified Streaming Data Platform offers a comprehensive ecosystem to build, deploy, and manage the most demanding data stream processing applications, truly unlocking the value of real-time data for any enterprise.