---
id: wiki-2026-0508-mapreduce
title: MapReduce
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [맵리듀스, Hadoop MR, Map-Reduce]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [distributed, big-data, parallel, hadoop, batch]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: hadoop-spark
---

# MapReduce

## 매 한 줄
> **"매 split → map → shuffle → reduce"**. MapReduce (Dean & Ghemawat, Google 2004) 는 대규모 batch 처리 의 functional programming 모델. 2026 perspective 에서 raw Hadoop MR 은 legacy, Spark / Flink / BigQuery / Beam 이 후속 표준.

## 매 핵심

### 매 4 phase
- **Split**: input → fixed-size shards (HDFS block 64-128MB).
- **Map**: (k1, v1) → list[(k2, v2)]. Stateless, parallelizable.
- **Shuffle/Sort**: same k2 grouped to same reducer.
- **Reduce**: (k2, list[v2]) → list[(k3, v3)].

### 매 design principles
- **Data locality**: code → data, not data → code.
- **Fault tolerance**: re-execute failed tasks (idempotent map/reduce).
- **Speculative execution**: slow tasks 의 backup copy.
- **Immutable inputs**: re-runnable.

### 매 응용
1. Log analysis / web indexing (original use case).
2. ETL pipelines.
3. ML feature aggregation.
4. Data warehouse build.

## 💻 패턴

### Word count (canonical)
```python
from collections import defaultdict
from itertools import groupby

def map_phase(doc_id, text):
    for word in text.split():
        yield (word.lower(), 1)

def reduce_phase(word, counts):
    yield (word, sum(counts))

def mapreduce(docs):
    # Map
    pairs = [kv for did, t in docs for kv in map_phase(did, t)]
    # Shuffle
    pairs.sort(key=lambda x: x[0])
    grouped = {k: [v for _, v in g] for k, g in groupby(pairs, key=lambda x: x[0])}
    # Reduce
    return dict(kv for k, vs in grouped.items() for kv in reduce_phase(k, vs))
```

### Combiner (local reduce)
```python
def map_with_combiner(doc_id, text):
    local = defaultdict(int)
    for word in text.split():
        local[word.lower()] += 1
    for w, c in local.items():
        yield (w, c)
# 매 network shuffle 양 감소
```

### Spark RDD equivalent
```python
from pyspark import SparkContext
sc = SparkContext()

counts = (sc.textFile("hdfs:///logs/*.txt")
            .flatMap(lambda line: line.split())
            .map(lambda w: (w.lower(), 1))
            .reduceByKey(lambda a, b: a + b))
counts.saveAsTextFile("hdfs:///out/wc")
```

### Inverted index
```python
def map_idx(doc_id, text):
    for word in set(text.split()):
        yield (word.lower(), doc_id)

def reduce_idx(word, doc_ids):
    yield (word, sorted(set(doc_ids)))
```

### Secondary sort
```python
# Composite key for sort-within-group
def map_temp(line):
    parts = line.split(",")
    year, temp = parts[0], int(parts[1])
    yield ((year, temp), None)  # negative temp for desc

def partitioner(key):
    return hash(key[0]) % num_reducers  # group by year only

def grouping_comparator(a, b):
    return (a[0] > b[0]) - (a[0] < b[0])  # year only
```

### Join (reduce-side)
```python
def map_users(row):
    yield (row["user_id"], ("user", row))

def map_orders(row):
    yield (row["user_id"], ("order", row))

def reduce_join(uid, tagged):
    user = next(r for tag, r in tagged if tag == "user")
    for tag, r in tagged:
        if tag == "order":
            yield {**user, **r}
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Batch ETL on TB+ | Spark (Hadoop MR 은 legacy) |
| Streaming | Flink / Spark Structured Streaming |
| SQL-shaped query | BigQuery / Athena / Presto |
| Cross-cloud portability | Apache Beam |
| Educational | Raw MR pseudocode |

**기본값**: Spark for new projects; Hadoop MR 은 legacy 유지보수만.

## 🔗 Graph
- 부모: [[Distributed Systems]] · [[Parallel-Computing]]
- 변형: [[Spark]] · [[Apache Flink]]
- 응용: [[Data Pipeline]] · [[ETL]]

## 🤖 LLM 활용
**언제**: pipeline design review, Spark migration 가이드, query optimization.
**언제 X**: real-time low-latency — wrong paradigm.

## ❌ 안티패턴
- **Many small files**: HDFS namenode 폭발. 매 compaction 필수.
- **Skewed keys**: 한 reducer 가 hotspot — salting / combiner 로 완화.
- **Stateful map**: 매 idempotency 깨짐 → fault recovery 실패.
- **Re-implementing SQL**: 매 BigQuery / Spark SQL 사용.

## 🧪 검증 / 중복
- Verified (Dean & Ghemawat OSDI 2004, Spark NSDI 2012, Hadoop docs 3.x).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — word-count + Spark + secondary-sort + join 패턴 |