Stream Processing Demystified: Stateless vs. Stateful

While some real-time use cases can be handled effectively with stateless stream processing, these cases are the exception rather than the rule. As business needs become more complex, the limitations of stateless processing become apparent. Most non-trivial use cases require a deeper understanding of the context in which events occur, making stateful stream processing a necessity.

Stateful stream processing allows businesses to maintain and update a memory of past events, enabling them to handle more complex and valuable use cases. These tasks involve recognizing patterns, correlating events, and making informed decisions based on accumulated knowledge, all of which are impossible without stateful processing. As businesses increasingly rely on real-time analytics to drive critical decisions, stateful stream processing becomes indispensable, empowering them to harness the full potential of their data streams and respond to the ever-changing demands of the market.

In this article, we explore the differences between stateful and stateless stream processing, highlighting the unique advantages of stateful processing. We'll also guide you on how to implement stateful stream processing with ease, so you can enable the next generation of data-driven innovation.

Comparing Stateless and Stateful Stream Processing

Put simply, stateful stream processing is a method of processing continuous data streams where the system maintains and updates a state, or memory, of past events as it processes new data. This state is used to make decisions or computations based on both current and previous data, allowing the system to recognize patterns, track changes, and aggregate information over time. With stateful stream processing:

• Data is remembered from previous messages/events/records
• Data is persisted in both memory (for fast access) and storage (for retention of large context)
• Requires data to be de-duplicated
• The order of events is important

For example, in a fraud detection system, stateful stream processing can keep track of a user's transaction history to detect unusual behavior. This ability to maintain context makes stateful stream processing crucial for applications that require complex event processing, real-time analytics, and decision-making based on cumulative data.

In contrast, stateless processing doesn't maintain any context or state information from past events. This makes it simpler, as it doesn't require the overhead of storing and managing state. For example, in a real-time filtering system that only needs to discard events that don't meet a certain criterion, stateless processing is sufficient, as each event can be processed in isolation. With stateless stream processing:

• Processing is done without relying on previously processed data
• The order of events doesn’t matter and arbitrary events can be processed in parallel
• Best suited for use cases where the current event matters most 

Putting Stateless Stream Processing to Work

Stateless stream processing is well-suited for a variety of workloads that involve straightforward, event-by-event processing. Some examples of workloads that it handles particularly well include:

• Data Filtering of unwanted messages or events based on predefined criteria. For example, efficiently discarding invalid or incomplete transactions in a financial data stream.
• Data Transformation where each data point needs to be modified independently. This can include tasks like converting units, normalizing data, or enriching data with additional static information.
• Routing and Partitioning to different destinations based on specific attributes. For instance, directing events to different processing pipelines based on event type or severity.
• Simple Alerting based on specific conditions in individual events. For example, if a temperature sensor reading exceeds a certain threshold, a stateless system can immediately trigger an alert.

These workloads highlight the strength of stateless stream processing in scenarios where the simplicity, speed, and scalability of independent event processing are key advantages. However, stateless processing greatly limits what you can do with the data. Let’s have a look.

Use Cases for Stateful Stream Processing

While stateless processing can be useful for specific tasks, there are many cases when you need the power of stateful stream processing, which excels in workloads that require the system to maintain state across multiple events or over time. This capability is essential for more complex and dynamic data processing applications, including:

• Real-time Analytics and Aggregation that need to maintain running counts, averages, or sums of data points, such as tracking the total number of website visits in the last hour or calculating the average temperature from a stream of sensor data over a day. This is essential for dashboards, monitoring systems, or any application requiring continuous, real-time insights.
• Pattern Detection and Complex Event Processing is crucial for recognizing patterns or sequences of events that occur over time. For instance, in cybersecurity it can detect potential threats by analyzing the sequence of network events and identifying patterns indicative of an attack.
• Session-based Analysis, where events are grouped into sessions based on user activity or time. For example, in an e-commerce application, it can track a user's actions (e.g., clicks, views, purchases) within a session to analyze behavior, recommend products, or personalize content. Stateful systems can maintain session context, handling events that span multiple interactions or time periods.
• Windowed Operations over sliding or tumbling windows, such as calculating the top products sold in the last 15 minutes or detecting the highest temperature in the last 24 hours. Stateful processing allows the system to retain and update the state of these windows as new data arrives, making it possible to perform such time-based computations.
• Event Correlation by joining different sources or streams to derive meaningful insights. For instance, in a smart city application, data from traffic sensors, weather stations, and public transportation systems can be correlated to optimize traffic flow or predict delays.

These workloads demonstrate the fundamental need for stateful stream processing in handling complex, context-dependent tasks, making it a requirement for most sophisticated real-time data applications.

Common Stateful Stream Processing Concepts and Operations

Rather than being a replacement for existing data infrastructure, stateful stream processing capabilities sit alongside your data tech stack, ingesting real-time events from databases, data warehouses, and other data sources. Processing stateful streaming data is different than batch processing and requires understanding a few key concepts.

State persistence

State persistence is the capability of a system to maintain and manage the state or intermediate data across multiple events within a stream. Stateful streams require saving state in one or more formats, such as Flink’s key state, an embedded key/value store. Persistence is maintained through a combination of stream replay and checkpointing, where checkpoints represent the position of the last successful processing in the stream. Numerous state backends are supported, including in-memory and key/value storage in RocksDB.

If a failure occurs, events are replayed from the latest available checkpoint upon recovery. This also restores the results of any intermediate calculations. In addition, developers can also manually create save points for replay.

Data processing

Stateful stream processing needs to enforce data processing guarantees. A data processing guarantee refers to the promise made by the system about how it handles the delivery and processing of messages or events within a data stream. These guarantees are crucial for ensuring the reliability and correctness of the data processing pipeline, particularly in systems that must handle large volumes of data in real time.

For example, messages may be delivered exactly once, which is required for use cases like financial transactions, where duplication results in adverse consequences.

You can also deliver messages at least once, but they may be delivered multiple times. This is used in cases where multiple messages may result in behavior that is potentially unexpected, but not adverse. For example, a user might receive two copies of a notification instead of just one.

Windowing

Streams are also known as unbounded data, in that they have a defined start but no defined end. Windowing processes data, such as calculating sums or averages, within a specific timeframe or by the number of events (usually the former).

There are different types of windows available, including tumbling, sliding, session, and more. With Flink, windows can be keyed or non-keyed, with keying enabling parallel processing. Watermarks are used to handle late or out-of-order events, ensuring data is processed as part of the correct window.

Real-time joins

Complex joins are a staple of batch processing systems, enabling users to create reports on data combined from multiple systems. However, data may be days or even weeks old, preventing timely action.

Real-time joins on streaming data enable up-to-date data without sacrificing accuracy. A few types of real-time joins include regular, interval, temporal, and lookup. Check out this previous article for a deeper dive into stream joins.

Challenges of Stateful Stream Processing 

Stateful stream processing introduces significant complexities, particularly when it comes to managing and maintaining state across streams. One of the primary challenges is figuring out how to efficiently serialize and deserialize data, ensuring that state can be stored and retrieved quickly without becoming a bottleneck. Choosing the right storage mechanism is equally crucial, as it needs to balance speed, durability, and scalability. Moreover, storing state raises compliance concerns, especially when dealing with sensitive information such as personal data or financial records.

Maintaining state also necessitates robust mechanisms to ensure fault tolerance and reliability. In stateful stream processing, any loss of state due to network failures, power outages, or natural disasters can lead to incorrect results or data loss, undermining the system’s reliability. To mitigate these risks, systems often use techniques such as duplication, where state is replicated across multiple nodes, and checkpointing, where the state is periodically saved. Ensuring that these mechanisms work seamlessly across a distributed system requires careful design and continuous monitoring.

Achieving exactly-once processing semantics is another major challenge in stateful stream processing. Exactly-once processing ensures that each event is processed only once, even in the face of failures or retries, preventing issues like duplicate records or inconsistent state. Implementing this requires techniques such as idempotency, where operations can be safely repeated without affecting the final result or using sophisticated checkpointing strategies to track the progress of state updates. The need to balance performance, reliability, and consistency often requires tradeoffs, making the pursuit of exactly-once semantics a challenging but critical aspect of stateful stream processing.

Stateful Stream Processing Simplified with Decodable

The complexity of stateful stream processing has historically been a significant barrier to entry for many businesses, preventing them from fully leveraging the power of real-time analytics and applications. Recognizing this challenge, we created Decodable to simplify the development of real-time stateful solutions, making them more accessible and manageable than ever before. With Decodable, you can achieve full support for stateful stream processing with minimal architectural overhead, allowing your business to unlock the potential of real-time data without the complexity.

Ready to see how Decodable can transform your data processing capabilities? Try Decodable today or contact us for a demo to experience the easiest way to realize the benefits of stateful stream processing.