Stream Processing 101

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Stream Processing: An Introduction

This blog post explores stream processing, a data processing paradigm that works on data streams - continuous flows of information that may be unbounded. We'll discuss some key concepts, real-world applications, the evolution of stream processing, and how it compares to batch processing.

Understanding Key Concepts

Let's take an example of a ride hailing service like Uber, Lyft, Ola etc; and understand what some basic concepts mean.

Windows: This is a core concept when it comes to processing an infinite stream. Windows split a stream into buckets of finite size, over which we can apply transformations. Windows can be of different types like Tumbling, Sliding, Session window etc. We will go deeper into each in a separate blog. To take an example: Imagine calculating average ride requests per minute; one minute is the window in this case.
Aggregates: Aggregates are statistical measures that summarize or give insight into a large set of data points. In the realm of a ride-hailing service, aggregates can help understand overall operational efficiency, customer behavior, and service impact. For example, calculating the total number of ride requests within a specific window or the average ride duration helps in gauging service demand and operational smoothness.
- Types of Aggregates: Common aggregates include sums (total number of rides), averages (average fare per ride), counts (number of cancellations), and more complex statistics like percentiles (e.g., the 90th percentile of ride duration).
- Utility: Aggregates are used for reporting key performance indicators (KPIs), financial forecasting, capacity planning, and understanding user engagement levels. They transform raw data into actionable insights, enabling decision-makers to devise strategic plans based on concrete figures.
Joins: In today's world, we have a ton of data sources, like IOT sensors, web/mobile apps, social media feeds, financial transactions, GPS trackers and so on. But, these are all disconnected, so having a unified view across these data streams is a huge challenge. Joins are really powerful as they can help us in combining different data sources based on a shared attribute. For ex: For a ride-hailing service, you can join user data with ride data to get a comprehensive view.

Real-World Applications

Dynamic Car Trip Pricing: Prices can change based on real-time factors like traffic, demand, and location.

Credit Card Fraud Detection: Financial institutions use stream processing to monitor transactions and flag fraudulent activity in realtime.

Predictive Analytics in Retail: Businesses can analyze customer website behavior to understand trends and predict future actions, to manage inventory efficiently all in realtime.

The Evolution of Stream Processing

Stream processing isn't new, having been around for over 20 years. It has significantly evolved alongside advancements in databases and distributed systems. Today, there are powerful streaming databases like RisingWave, and stream processing is becoming increasingly common. Modern stream processing focuses on fault tolerance and large-scale data stream processing.

Comparing Batch and Stream Processing

Feature	Batch Processing	Stream Processing
Nature of Data Processing	Processes data in chunks or batches	Processes data continuously as it's received
Latency	Inherently high due to data collection and batch processing	Low latency; data is processed as soon as it's available
Processing	Scheduled (hourly, daily, weekly)	Continuous and always on
Infrastructure Requirements	Significant resources needed for large data volumes	Systems must be always on and resilient to failures
Throughput	Good for handling high data volumes regardless of peak periods	Good for real-time data with high throughput
Complexity	Simpler; data is readily available in chunks	More complex due to fault tolerance, continuous processing, and potential out-of-orderness or consistency issues
Use Cases	ETL jobs, monthly reports	Real-time analytics, fraud detection, live dashboards
Error Handling	After the batch is processed completely	Requires immediate error handling, in some cases corrections maybe required later.
Tools	Hadoop, Apache Spark	Apache Flink, Kafka Streams, RisingWave

Choosing Between Batch and Stream Processing

The choice depends on your workload and business requirements. Here are some key factors to consider:

Latency Requirements: If low latency is critical, stream processing can be really powerful.
Data Volume: Batch processing might be better suited for very high data volumes, that can be processed periodically and not in realtime.
Existing Infrastructure: Consider if your current infrastructure can support stream processing.