Scalable Data Mining with MapReduce and Apache Spark

Design for data movement and recovery

You will implement a local map-and-reduce example, then map each stage to Spark transformations, shuffles, caching and actions.

• Explain map, shuffle, partition and reduce stages.
• Distinguish Spark transformations, actions and lazy execution.
• Recognise data skew, wide dependencies and expensive shuffles.
• Test distributed output against a small trusted reference.

The network is often the expensive step

Map functions process partitions independently and emit key-value pairs. The shuffle groups values by key across workers. Reduce functions combine them. Associative and commutative aggregation makes retries and partition order safer, though floating-point addition can still vary slightly.

Spark builds a lazy plan from transformations. An action triggers execution. Narrow transformations such as a simple map can stay within partitions. Grouping and joins create wide dependencies and shuffles. Inspect the plan rather than assuming lazy means efficient.

Partition keys determine balance. One popular key can create a straggler while other workers finish. Salting, pre-aggregation and a different data model can reduce skew. Measure partition sizes before adding machines.

Caching helps only when reused data is costly to recompute and fits the chosen storage level. Unnecessary caching consumes memory and can slow the job. Unpersist data after its useful lifetime.

Distributed mining needs deterministic identifiers, idempotent output and checkpointed lineage for long workflows. Compare a small sample against a single-machine reference and reconcile counts before trusting a large run.

Simulate MapReduce word counts locally

This pure-Python program separates map, shuffle and reduce so the distributed contract remains visible.

Map records, group keys and reduce counts

from collections import defaultdict

documents = [
    "data mining needs evidence",
    "data quality shapes mining",
    "evidence needs context",
]

mapped = []
for document in documents:
    for token in document.split():
        mapped.append((token, 1))

shuffled = defaultdict(list)
for key, value in mapped:
    shuffled[key].append(value)

reduced = {key: sum(values) for key, values in shuffled.items()}
for key in sorted(reduced):
    print(key, reduced[key])

Expected reduced counts

context 1
data 2
evidence 2
mining 2
needs 2
quality 1
shapes 1

A Spark equivalent tokenizes with a flatMap, maps each token to a pair and reduces by key. Real text also needs explicit normalization and token rules.

Distributed failures to design for

Many cluster failures produce a result rather than an exception. Reconciliation and invariants are essential.

• A many-to-many join can multiply records across partitions.
• A hot key can cause one reducer to run far longer than the rest.
• Collecting a large dataset to the driver can exhaust memory.
• Retrying a task that writes non-idempotently can duplicate external side effects.

Plan a scalable frequent-item count

Design a Spark job that counts items per transaction and candidate pairs without collecting the full dataset.

• Write the input grain and partition key.
• Mark every shuffle and estimate what crosses the network.
• Add local combiners or pre-aggregation where valid.
• Define count, checksum and sample-output reconciliation against a local reference.

Scalability evidence

• Logical and physical plan.
• Partition-size and skew report.
• Correctness comparison plus cost and retry notes.

Official references and further reading

• Apache Spark RDD programming guide (Official transformations, actions and persistence guidance)
• Apache Spark SQL performance tuning (Official partition and query guidance)
• Python defaultdict (Official local shuffle example API)

Review note for Scalable Data Mining with MapReduce and Apache Spark: recheck the linked documentation after a dependency changes the relevant API, metric or modelling assumption, then record the tested version beside your result.