Algorithms You Should Master to Design Systems That Scale
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- Author
- Ram Simran G
- twitter @rgarimella0124
Designing scalable systems isn’t just about adding more servers — it’s about knowing how to distribute, balance, and synchronize data efficiently across them. The secret sauce behind most distributed systems lies in a handful of core algorithms.
Here’s a breakdown of the 23 key algorithms and patterns every system designer should know — explained simply, with examples and use cases.
1. Consistent Hashing
What it does: Distributes data evenly across multiple servers/nodes with minimal data movement when nodes are added or removed.
Example: Used by distributed caches like Amazon DynamoDB, Cassandra, and Memcached.
Use Case: When you need scalable data partitioning — e.g., in caching, sharding databases, or load distribution.
2. Load Balancing Algorithms
What they do: Distribute incoming traffic across multiple servers to avoid overloading any one server.
Common Algorithms:
- Round Robin
- Least Connections
- IP Hash
- Weighted Round Robin
Use Case: Web servers behind a load balancer like Nginx, HAProxy, or AWS ALB.
3. Leaky Bucket & Token Bucket
What they do: Control request rate — prevent a system from being flooded by too many requests.
Example: APIs use them to rate-limit incoming requests.
Use Case: Protect services from abuse or sudden traffic spikes.
4. Bloom Filters
What it does: Quickly checks if an element might be in a set (with a small chance of false positives).
Example: Used in Bigtable, Cassandra, and HBase to check if a key exists before querying disk.
Use Case: Speeding up lookups when exact accuracy isn’t critical.
5. Merkle Trees
What it does: Verifies data integrity efficiently across distributed systems.
Example: Used in Git and blockchains (like Bitcoin) to verify data consistency.
Use Case: Detecting data corruption or inconsistency across replicas.
6. Quorum Algorithms
What they do: Ensure a majority of nodes agree before confirming an operation — balancing consistency and availability.
Example: Used in Cassandra, DynamoDB, and ZooKeeper.
Use Case: For strong consistency in distributed databases.
7. Leader Election Algorithms
What they do: Select a leader node in a distributed system for coordination tasks.
Example: Raft and ZooKeeper’s Zab protocols use leader election.
Use Case: Cluster coordination, distributed job scheduling, or consensus systems.
8. Distributed Lock Algorithms
What they do: Prevent multiple processes from performing the same action simultaneously.
Example: Redlock algorithm (Redis) or Zookeeper-based locks.
Use Case: Synchronizing actions like updating shared resources.
9. Raft / Paxos
What they do: Achieve consensus among distributed nodes even if some fail.
Example: Used by etcd, Consul, and Google Spanner.
Use Case: Maintaining a single source of truth in distributed systems.
10. Gossip Protocol
What it does: Nodes spread information by “gossiping” — each node randomly communicates with others.
Example: Used by Cassandra, Serf, and Consul for cluster state sharing.
Use Case: Failure detection and decentralized coordination.
11. Vector Clocks / Lamport Timestamps
What they do: Track causality between events in distributed systems.
Example: Amazon Dynamo uses vector clocks for conflict resolution.
Use Case: Maintaining version history and resolving write conflicts.
12. Two-Phase / Three-Phase Commit
What they do: Coordinate atomic transactions across distributed systems.
Example: Used in distributed databases and message queues like Kafka.
Use Case: Ensuring all participants in a transaction either commit or rollback together.
13. Reservoir Sampling
What it does: Randomly samples k items from a stream of unknown size efficiently.
Example: Used in online analytics and logging systems.
Use Case: When dealing with large or infinite data streams where full storage isn’t feasible.
14. HyperLogLog
What it does: Estimates the number of unique elements (cardinality) in a dataset using minimal memory.
Example: Used in Redis and Google BigQuery.
Use Case: Counting unique visitors or users at scale.
15. CRDTs (Conflict-Free Replicated Data Types)
What they do: Allow concurrent updates on distributed nodes that automatically converge to a consistent state.
Example: Used in Redis, Riak, and Collaborative editing apps (like Google Docs).
Use Case: Offline-first systems or real-time collaborative tools.
16. Sharding Algorithms
What they do: Split large datasets across multiple databases or nodes.
Example: User IDs or timestamps can be used as shard keys.
Use Case: Scaling databases horizontally when single-instance storage isn’t enough.
17. MapReduce
What it does: Processes large datasets in parallel by mapping tasks and reducing their results.
Example: Hadoop, Spark, and Google’s MapReduce.
Use Case: Big Data processing and analytics pipelines.
18. Tail Latency Reduction (Hedged Requests)
What it does: Sends duplicate requests to reduce the impact of slow servers.
Example: Used by Google Search to reduce tail latency (p99).
Use Case: Mission-critical services where user experience depends on response speed.
19. Circuit Breaker Pattern
What it does: Prevents cascading failures when a dependent service is slow or down.
Example: Implemented in Netflix Hystrix, Resilience4j.
Use Case: Microservices needing resilience against slow dependencies.
20. Split-Brain Resolution Algorithms
What they do: Handle network partitions where multiple nodes think they are leaders.
Example: ZooKeeper and etcd handle this using quorum or fencing tokens.
Use Case: Ensuring consistency during network splits.
21. Load Shedding Algorithms
What they do: Drop low-priority requests when the system is overloaded.
Example: Used in Netflix, Google, and YouTube to maintain uptime during spikes.
Use Case: Maintaining performance under heavy load.
22. Byzantine Fault Tolerance
What it does: Allows the system to work even when some nodes act maliciously or arbitrarily.
Example: Used in blockchains like Ethereum and Hyperledger.
Use Case: Critical distributed systems that need trust without central authority.
23. Skip Lists
What it does: A probabilistic data structure for fast search, insert, and delete operations.
Example: Used in Redis for sorted sets (ZSET).
Use Case: Ordered indexing in in-memory databases.
🏁 Final Thoughts
These algorithms are the building blocks of scalable, fault-tolerant systems. You don’t need to memorize their proofs — but knowing what problem each one solves will make you a stronger system designer.
If you ever wonder how Google, Netflix, or Amazon handle billions of requests — it’s through clever combinations of these very algorithms.
Cheers,
Sim