Cache Strategy

[webysther]

After adjusting the code to handle the current data volume, I had some ideas for simple optimizations that can make this project more sustainable.

In most big data projects I’ve worked on, a very common strategy is partitioning. The larger the dataset, the simpler and more effective it is to split processing into smaller chunks. For example, in massive data lakes (14 PB+), partitioning is not optional — it’s essential. Typically, data is divided into three main stages: raw, processed, and gold. One of the key benefits is persisting processed results for data that doesn’t change over time (e.g., historical data).

As a data engineer, here are a few practical tips you could apply:

• Leverage historical data caching: Any data outside the current year can be consolidated and cached on disk. If a query matches this range and cached results exist, simply return them directly. This can be done manually or with libraries like cachegoose or ts-cache-mongoose.
• Partition processing by year: Similar to what I did in task Batch processing for Longest Listening Session #555. It may seem counterintuitive, but in big data environments, breaking processing into every possible dimension is the key to scalability.
• Use window functions: These have been around for years and remain extremely powerful for splitting and aggregating results within database engines. MongoDB 5.0+ supports them (reference).
• Apply data lake partitioning concepts to MongoDB: Partitioning strategies can deliver a massive performance boost. Start with year and year-month, and extend to year-month-day where appropriate.