12 System Design Algorithms You Must Know Before the Interview
This blog breaks down 12 essential algorithms behind scalable system design. You’ll learn what each algorithm is, why it’s important, and how it helps build robust, efficient systems – from preventing overload to enabling real-time collaboration.
What do systems like YouTube, Uber, and Instagram have in common?
They're all backed by clever algorithms that quietly keep things fast, reliable, and scalable—without users ever noticing.
But here’s the thing: you don’t need to be a senior architect to understand these algorithms.
In fact, many of the most powerful tools in system design are surprisingly approachable once you know what problems they solve.
In this blog, we’ll walk through the essential algorithms that power scalable system architecture—from handling billions of requests to syncing data across the globe.
Let's directly discuss the list of system design algorithms.
1. Bloom Filter – Quick Membership Checks without Heavy Memory
Imagine you have a huge database or list and you want to quickly check if an item might be in it without doing a slow database lookup.
Bloom Filters are perfect for this.
A Bloom Filter is a probabilistic data structure that tells you if an element is possibly in a set, with a tiny chance of being wrong (false positive) but guaranteed to catch all real members. In other words, if a Bloom Filter says “not present,” you can trust that – it never gives false negatives.
2. Merkle Tree – Verifying Data Integrity Across Nodes
A Merkle Tree is a tree of hashes; each leaf node is a hash of a piece of data, and parent nodes are hashes of their children.
The beauty is that you can compare the top hash (root) of two Merkle Trees to quickly tell if any difference exists in the underlying data.
If the roots differ, you can traverse down the tree to pinpoint where the discrepancy is.
This is heavily used in blockchain systems (like Bitcoin or Ethereum) to verify transactions efficiently without transmitting all data.
In system design, Merkle Trees help with things like data synchronization and detecting inconsistencies between replicas – for example, in distributed databases or file systems that need to ensure all nodes have the same data.
3. Raft (Consensus Algorithm) – Keeping Distributed Systems in Sync
If you have multiple servers all working together, how do they agree on a single source of truth, especially for critical data like updates or leader election?
Raft is a popular consensus algorithm that answers this.
It’s used to ensure that even if some nodes fail or messages get delayed, all the remaining nodes can agree on a consistent state (like the latest committed transactions or who the primary leader is).
Raft simplifies the notoriously tricky Paxos algorithm into something more understandable while achieving the same goal: reliable consistency across a cluster.
Real-world systems like etcd (which Kubernetes uses for storing cluster state) implement Raft under the hood.
For system design interviews, knowing Raft shows you understand how to keep data consistent across microservices or database replicas in distributed systems.
4. Ray Casting – Geospatial Analysis and Collision Detection
Ray Casting is a technique where you “cast” an imaginary ray from a point and see what it hits.
In system design, this comes up in two notable areas: computer graphics (and games) for things like collision detection or figuring out what a player can see, and geospatial systems for tasks like point-in-polygon queries (e.g. determining if a GPS coordinate lies within a region on a map).
For example, a game server might use ray casting to determine line-of-sight (does player A’s shot hit an obstacle before reaching player B?), while a mapping service might use it to check if a point is inside a complex boundary.
Ray casting simplifies these problems by reducing them to math checks along a ray.
5. Geohash – Mapping Locations to a Search-Friendly Code
Working with location data?
Geohash comes to the rescue.
Geohash is an algorithm that converts latitude/longitude coordinates into a short alphanumeric string.
Nearby locations produce similar prefixes in their Geohash codes. This means you can store and index locations by these prefixes to quickly query “what’s nearby” without doing complex math each time.
Many location-based services (think finding the nearest restaurant or driver) use Geohashes to partition the globe into a grid for efficient lookup. By simplifying coordinates into a string, Geohash makes geo-queries fast and scalable in location-based system designs.
6. HyperLogLog – Estimating Huge Counts with Tiny Memory
HyperLogLog is a clever probabilistic counting algorithm that estimates the number of unique elements in a large data set with just a small, fixed amount of memory.
For instance, instead of keeping a gigantic list of all user IDs visiting a site, a HyperLogLog structure can estimate the count of unique users on the fly.
The trade-off is an acceptable error margin, but in exchange you get massive savings in memory.
7. Lossy Counting – Finding Frequent Items in Data Streams
Picture an online analytics system processing millions of events (search queries, clicks, network packets).
You want to find the “heavy hitters” – the most frequent items – but you can’t possibly store every event in memory.
Lossy Counting is an algorithm that gives an approximate frequency count of elements in a data stream using limited space.
As the name implies, it “forgets” less frequent items, focusing on elements that occur often.
8. Quadtree – Efficient Spatial Indexing
A Quadtree is a tree data structure that recursively divides a 2D space into four quadrants, and then subdivides as needed.
This makes searching for points in a region much faster than scanning everything.
For system design, quadtrees shine in spatial databases, map services, or any feature that deals with 2D (or even 3D) space like GPS coordinates.
For example, if you’re designing an Uber-like service, a Quadtree could help partition a city’s map so that finding drivers near a location is super quick.
9. Rsync Algorithm – Efficient Data Synchronization
Keeping files in sync between two servers or devices can be bandwidth-heavy if you always copy everything.
The rsync algorithm revolutionized this by only sending the differences (deltas) between files.
It works by splitting files into chunks, using clever rolling checksums to find which chunks changed, and transferring only those.
In practice, this means you can sync huge files or directories over a network very efficiently.
For example, if you have two servers and you update a few lines in a log file, rsync will send just those changes rather than the whole file.
10. Leaky Bucket – Smoothing Out Traffic Spikes (Rate Limiting)
Servers can only handle so much traffic at once.
To prevent overload (and abuse), we use rate limiting algorithms.
One popular metaphor is the Leaky Bucket.
Picture a bucket with a small hole: water (requests) can flow in at any rate, but it leaks out at a fixed steady rate.
If too much water comes in too fast, the bucket overflows – in networking terms, excess requests are dropped or delayed.
Implementing this ensures that a service can handle bursts of requests by queueing or rejecting them instead of crashing.
In system design, you’d use Leaky Bucket or similar algorithms (like Token Bucket) in APIs to prevent abuse and ensure fairness.
11. Operational Transformation – Real-Time Collaboration
If you’ve used Google Docs or any real-time collaborative editor, you’ve seen Operational Transformation (OT) in action.
OT is an algorithm that allows multiple users to edit the same document simultaneously without tripping over each other’s changes. It transforms operations (like insert or delete) from different users so that the end document is consistent for everyone.
In plain terms, if two people type or edit at the same time, both changes are applied in a sensible way.
This is how Google Docs, Confluence, and others enable collaboration – no more “document lock” like the old days!
12. Consistent Hashing – Even Data Distribution Across Servers
In a distributed system, we often need to distribute data or requests evenly across multiple servers (like shards or cache nodes).
Consistent Hashing is a hashing strategy that makes this easy and efficient.
In regular hashing, if you add or remove a server, a lot of data needs to move around (expensive!).
Consistent hashing fixes this by mapping both servers and data keys to the same hash ring, so when the number of servers changes, only a minimal portion of keys need to remap.
This algorithm is a cornerstone of scalable designs – it’s used in systems like Amazon’s DynamoDB, distributed caches (Redis Cluster, Memcached), and content delivery networks.
Wrapping Up: Bringing It All Together
We’ve just covered a dozen algorithms and data structures that frequently pop up in system design discussions.
Did you notice a pattern?
Many of these algorithms deal with scale and efficiency: they handle huge amounts of data or requests by being smart about accuracy (Bloom Filter, HyperLogLog, Lossy Count), by partitioning the problem (Geohash, Quadtree), or by ensuring consistency and reliability in distributed setups (Consistent Hashing, Merkle Tree, Raft, Operational Transformation).
Others protect systems and optimize performance (Leaky Bucket for overload protection, Rsync for bandwidth saving, Ray Casting for spatial computation efficiency).
With knowledge of these algorithms and some practice, you’ll be well on your way to designing systems that can handle millions of users.
What our users say
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