Why Knowing HashMaps Won’t Make You a Better Engineer?
Every engineer should ask, which datastructure should I use to solve the problem. Base on experience and knowledge you can use the right one?
“Which data structure should I use?”
| Question | Pattern |
|---|
| Data too big? | Partition |
| Reads too slow? | Index |
| Too many repeated reads? | Cache |
| Continuous data? | Stream |
| Need relationships? | Graph |
| Need priority? | Heap |
| Memory limited? | Compress |
Lets get to some details.
INDEX → “Find fast at scale”
Structures
- B-Tree / B+ Tree
- LSM Tree
Mental Model
“I don’t scan — I navigate”
Why it exists
Arrays & hashmaps break when:
- Data is too large (disk)
- Needs ordering + range queries
Real Systems
- MySQL / PostgreSQL → B+ Trees
- RocksDB / Cassandra → LSM Trees
PARTITION → “Split to scale”
Structures
- Sharded Hash Maps
- Distributed Hash Tables (DHT)
Mental Model
“One machine can’t handle it — split it”
Real Systems
- Cassandra
- DynamoDB
- Kafka partitions
CACHE → “Trade memory for speed”
Structures
- LRU Cache
- LFU Cache
Mental Model
“Keep what matters close”
Real Systems
- Redis
- CDN caching
- API response caching
STREAM → “Data never stops”
Structures
- Ring Buffer
- Log (append-only)
Mental Model
“Data is a flow, not a collection”
Real Systems
- Kafka
- Pulsar
- Event streaming systems
PRIORITY → “Not everything is equal”
Structures
- Heap
- Fibonacci Heap
Mental Model
“Always process the most important first”
Real Systems
- Job schedulers
- Task queues
- OS scheduling
GRAPH → “Relationships are everything”
Structures
- Adjacency List / Matrix
- Graph DB structures
Mental Model
“Connections matter more than data”
Real Systems
- Neo4j
- Social networks
- Fraud detection
COMPRESS → “Do more with less”
Structures
- Bloom Filter
- HyperLogLog
Mental Model
“Be approximately right, but very fast”
Real Systems
- Big data systems
- Query optimization
- Cardinality estimation