MapReduce BatchAggregation
Large-scale batch data flow: map a transformation over many records in parallel, then reduce/aggregate the results.
Why we need this / what value this brings
Lets aggregation over very large datasets be parallelized across many workers instead of run serially on one machine.
When to use this
Only once a single-query or single-machine aggregation is a proven bottleneck — significant infra complexity for datasets that don't need it.
How to use or implement this
Not yet relevant at Localz's scale; if it ever is, reach for a managed option (e.g. a warehouse's built-in parallel query engine) before hand-building a MapReduce pipeline.
Research questions
- Relevant once Analytics/DataWarehouse-BI needs to process volumes too large to aggregate in a single query — not yet at Localz's current scale.
- This is the general pattern behind AI/ContextWindowManagement-ChunkingStitching's map-reduce summarization: map = summarize each chunk of a 10MB+ device diff independently, reduce = summarize the summaries into one final report, times 1000s of devices.
Empty folder — drop notes, links, and findings here as you research.