A system fault can happen in the blink of an eye. Your throat goes dry and your palms begin to sweat as you read the alert popping up on your screen. Can you be sure that the fault will be contained within seconds? Are you confident that the integrity of critical data can be restored?
Enterprises Can Embrace the Future of Fault Tolerance
Anyone who has ever worked with IT systems and databases knows that being down for even a few seconds can have serious ramifications that can affect an entire enterprise. There are thousands of reasons why a system can experience a fault. However, there is just one way to make sure a big data analytics system can be preserved and recovered. The solution is fault tolerance. Take a moment to learn why fault-tolerance is such an essential feature in today’s high-tech world.
The Importance of Fault Tolerance
You can think of fault-tolerance as insurance, security and integrity assurance all rolled into one package. It ensures that a process you’re depending on can run infinitely. Fault-tolerance allows a system to continue operating even if a fault compromises one or more of its components.
This means you can depend on the system to automatically restart any portion if faults occur. In addition, there is a guarantee that the data will be fully processed. This works differently than a naively designed system because any reduction in operational quality will be directly proportional to the size of the fault.
Systems that are not fault tolerant can potentially break down completely in the event of a fault of any size. Fault-tolerance is crucial in critical systems that cannot be shut down for the sake of the health of an entire enterprise. What is the result of a lack of fault-tolerance? You can look forward to dealing with missing data, compromised data and lapsed syncing.
Some of the situations that may present the need for fault-tolerance include mechanical fatigue, corrosion, manufacturing errors, impact, hacks, natural disasters and sabotage. Fault-tolerance must demonstrate four essential characteristics to be considered genuine and effective. Those characteristics are:
- A lack of a single point of failure. This means that a system can continue to operate during the diagnosis and repair period.
- Fault isolation for the component that has been affected.
- Fault containment to prevent the failure from spreading to other components. This is especially important in the case of sabotage or hacking.
- Access to reversion modes that allow systems to be restored to the point before a fault occurred.
The Genius of Exactly-Once Processing
Preserving and restoring data following a fault is no small task. In fact, it is one of the most challenging aspects of recovering from a fault. The biggest issue that IT professionals face is attempting to verify that all affected data has been successfully recovered and restored. Many fault-tolerant systems attempt to solve this problem by creating duplicates of all messages that are sent within a system.
This method can be problematic because of the fact that message duplicity is incompatible with many critical systems.
What is an IT professional to do when the need to protect a system clashes with the desire to create copies of messages? Exactly-once processing has become the go-to workaround solution for many enterprises. An input operator that uses exactly-once processing recalls which events were consumed in which windows. It can then replay and re-compute those streaming windows in the exact same order that the original stream of data occurred.
This method essentially reconstructs a data flow using data that has been flawlessly preserved. Exactly-once processing is brilliant because it matches every piece of data word to word. This cuts down on the need to spend time and resources trying to piece together a picture of what stored data looked like before a fault occurred. Possessing the ability to have data automatically restored may help to drastically cut the potential high cost of a system fault or data breach.
Additional Perks of Exactly-Once Processing
It is obvious to see that the primary benefit of exactly-once processing is accuracy. However, there are some other benefits that make this processing method the best bet around for modern enterprises. The concept of the streaming window is especially useful if you have efficiency in mind.
This type of processing actually allows for database updates to be batched and saved in large groups. This effectively reduces the volume of data sent through the stream. As a result, fewer database updates are needed. The bottom line is that there simply isn’t a more cutting-edge way to capture and safely archive every event that happens in a system. 🙂
Storm is the same technology that enables Twitter to process 400 million tweets per day. Kafka is the foundation of LinkedIn’s activity streams, and their operational data processing pipeline. Together, Storm and Kafka form the best enterprise-grade real-time streaming analytics solution on the market.