The Problem We Were Actually Solving
In our previous setup, we had a batch processing engine that processed user transactions and updates in the background. However, this led to a significant delay between user interactions and system updates. Our users were complaining about inconsistent state and delayed rewards. We realized that we needed to switch to a streaming engine to process updates in real-time. We set out to implement Apache Kafka and Apache Flink to handle the treasure hunt engine's transactions and updates.
What We Tried First (And Why It Failed)
Our first attempt at switching to a streaming engine was a disaster. We used a combination of Apache Kafka, Apache Flink, and Amazon Kinesis Data Firehose to process updates in real-time. However, we failed to account for the complexity of handling user transactions and updates in a distributed system. Our pipeline kept crashing due to data inconsistencies and timing issues. We also struggled to maintain a stable query cost due to the high volume of transactions. We realized that our architecture was not scalable and required significant rework.
The Architecture Decision
After weeks of research and experimentation, we decided to switch to a hybrid architecture that combines the best of batch and streaming processing. We implemented Apache Kafka for real-time event processing and Amazon Kinesis Data Firehose for batch processing. We also introduced a caching layer using Amazon ElastiCache to reduce query cost and improve pipeline latency. Our new architecture allowed us to process user transactions and updates in real-time while maintaining a stable query cost.
What The Numbers Said After
Our new architecture resulted in a 20% reduction in pipeline latency and a 30% decrease in query cost. We also saw a significant improvement in system consistency and accuracy. Our users were finally able to experience the thrill of the hunt without any delays or inconsistencies. Our system was serving our users well, and we were confident in our ability to handle high traffic and user growth.
What I Would Do Differently
If I were to do it differently, I would have introduced the caching layer earlier in the project. Our caching layer helped reduce query cost and improve pipeline latency, but it also added complexity to our system. I would have also tested our system more thoroughly before rolling it out to production. We had a few critical issues with data inconsistencies and timing issues that required significant rework. In retrospect, I would have also considered using a more robust streaming engine that can handle high-volume transactions and updates.
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