Apache Spark vs MapReduce: When to Use Which

Apache Spark has largely replaced MapReduce for new Hadoop workloads. But MapReduce is not dead — understanding when each is appropriate will help you build more efficient data pipelines.

The Core Difference: Memory vs Disk

MapReduce writes intermediate results to HDFS disk between every Map and Reduce stage. Spark keeps intermediate data in memory (with spill to disk when needed). For iterative algorithms that process the same data repeatedly, this makes Spark orders of magnitude faster.

Performance Benchmark Example

For a machine learning algorithm that iterates over a dataset 10 times:

Approach	I/O Pattern	Relative Speed
MapReduce	10 HDFS reads + 10 HDFS writes	1x (baseline)
Spark (with cache)	1 HDFS read, rest in memory	~10-100x faster

When MapReduce Wins

Despite Spark's performance advantage, MapReduce still makes sense when:

1. Memory is severely constrained — MapReduce handles datasets larger than cluster RAM by spilling everything to disk
2. Long-running, write-once batch jobs — the disk durability of MapReduce is a feature, not a bug
3. Legacy compatibility — existing MapReduce jobs in production don't need to be rewritten if they're working fine

When Spark Wins

Use Spark when:

1. Iterative ML training — Spark MLlib and graph algorithms benefit enormously from in-memory caching
2. Interactive analytics — Spark's REPL (PySpark, spark-shell) supports exploratory data analysis
3. Streaming — Spark Structured Streaming provides unified batch/streaming APIs
4. SQL workloads — Spark SQL with DataFrames is faster and more expressive than Hive on MapReduce

Running Spark on YARN

Spark integrates natively with YARN, making it a first-class Hadoop citizen:

# Submit a Spark job to YARN
spark-submit \
  --master yarn \
  --deploy-mode cluster \
  --num-executors 10 \
  --executor-memory 4g \
  --executor-cores 2 \
  myapp.py

# Launch PySpark shell on YARN
pyspark --master yarn --num-executors 5 --executor-memory 2g

Recommendation

For any new Hadoop workload, start with Spark. Only fall back to MapReduce if you have specific memory constraints or need to maintain a legacy codebase.