Data Systems
map-side (broadcast hash) join
small table in every mapper's RAM
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- batch vs service vs streamoffline bulk / online requests / near-real-time
- bounded inputfinite dataset, so the job finishes
- dataflow engine (Spark/Flink)one DAG, in-memory, no forced materialization
- distributed filesystem (HDFS)replicated blocks across shared-nothing nodes
- MapReducemap → shuffle(sort) → reduce over a cluster
- materializationwriting full intermediate output to disk
- RDD / lineagerecompute lost data from its recipe
- reduce-side (sort-merge) joinjoin in the reducer after a shuffle
- saltingsplit a hot key
- shufflepartition + sort by key
- Unix pipestream one tool's output into the next
- workflow/DAGchained jobs