The Problem We Were Actually Solving
Our problem wasn't "how to store and query data from anywhere in the world," but rather "how to optimize data infrastructure for regions with unreliable internet connectivity and high latency." We had to consider the reality of our users in Ghana, many of whom rely on relatively slow and unpredictable internet connections. We were trying to solve a problem that was only slightly related to the actual needs of our users.
What We Tried First (And Why It Failed)
When we first started building our infrastructure, we assumed that we could just use AWS's built-in features to replicate data between regions. However, this led to query times that were often over 10 seconds, which was unacceptable for our platform. We also saw query costs that were significantly higher than expected, which was a major concern for our budget. It soon became apparent that simply replicating data between regions wasn't going to cut it – we needed a more sophisticated approach to managing our data.
The Architecture Decision
We decided to move our data infrastructure to a more distributed architecture, using a combination of Apache Kafka and Apache Cassandra to handle the data ingestion and storage needs of our platform. We also implemented a data pipeline that used a combination of AWS Lambda and Apache Spark to handle the processing and aggregation of our data. This allowed us to better handle the variability and unpredictability of our users' internet connections, and significantly improved our query times and costs.
What The Numbers Said After
After implementing our new architecture, we saw significant improvements in our query times and costs. Our average query time dropped from over 10 seconds to under 2 seconds, and our query costs decreased by over 50%. We also saw significant improvements in our data pipeline's ability to handle high volumes of data, with a throughput increase of over 300%.
What I Would Do Differently
If I were to do things differently, I would have taken a more gradual approach to deploying our new architecture. While it was tempting to rip out our existing infrastructure and replace it with a new, shiny system, it would have been better to start by implementing a small-scale proof-of-concept and then incrementally deploying the new system to our production environment. This would have allowed us to more smoothly transition our users to the new system and minimize the risk of downtime and disruptions.
Ultimately, the wrong assumptions about geo-located data infrastructure can have significant consequences. By taking the time to understand the needs and constraints of our users, we can build systems that are better suited to their requirements and ultimately provide a better experience for everyone involved.
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