Cloud Load Balancer Cheat Sheet

Cloud Load Balancer Cheat Sheet

Published on
Authors

Having recently completed your master’s in Software Systems with a focus on Distributed Systems, this is the perfect topic to showcase your expertise. This guide dives into 12 key algorithms that frequently arise in system design interviews and real-world architecture challenges. Each algorithm includes practical use cases and insights into how they can enhance load balancer strategies.

1. Bloom Filter

A probabilistic data structure that checks if an element is in a set with space efficiency.

  • How It Works

    • Uses multiple hash functions.
    • Maps elements to bits in a bit array.
    • Can have false positives, but never false negatives.

  • Typical Use Case

    • Used in databases like Cassandra and HBase.
    • Prevents unnecessary disk lookups.
    • Employed in CDNs and browsers to detect malicious URLs.

  • Load Balancer Consideration

    • Quickly checks if a resource exists in cache.
    • Reduces unnecessary redirects and latency.

2. Geohash

A spatial indexing technique that encodes lat-long coordinates into alphanumeric strings.

  • How It Works

    • Divides Earth into grid-like regions.
    • Nearby locations share common prefixes.

  • Typical Use Case

    • Used in ride-sharing apps, map services, and local search.

  • Load Balancer Consideration

    • Routes users to geographically closest data center.
    • Reduces latency and optimizes regional availability.

3. HyperLogLog

An approximate algorithm to estimate the number of unique elements (cardinality).

  • How It Works

    • Uses hash functions.
    • Tracks max leading zeros in hash values.

  • Typical Use Case

    • Used by Redis for unique user counting.
    • Web analytics, search queries, etc.

  • Load Balancer Consideration

    • Tracks unique users for privacy-friendly traffic distribution.

4. Consistent Hashing

Solves data redistribution issues in distributed systems when nodes are added or removed.

  • How It Works

    • Maps servers and data on a ring.
    • Only a fraction of data is moved on node change.

  • Typical Use Case

    • Used in Memcached, DynamoDB, distributed caches.

  • Load Balancer Consideration

    • Ensures session stickiness while enabling scalability.

5. Merkle Tree

A hash tree for efficient and secure data verification.

  • How It Works

    • Leaf nodes contain data hashes.
    • Parent nodes hash of child hashes.
    • Root hash verifies entire dataset.

  • Typical Use Case

    • Used in blockchains, IPFS, Cassandra for data integrity.

  • Load Balancer Consideration

    • Ensures data consistency between replicas before routing.

6. Raft Algorithm

A leader-based consensus algorithm for replicated state machines.

  • How It Works

    • Elects a leader.
    • Leader handles log replication and enforces safety.

  • Typical Use Case

    • Used in etcd, Kubernetes, Consul, CockroachDB.

  • Load Balancer Consideration

    • Routes write requests to leader.
    • Read requests can go to followers.

7. Lossy Count

A streaming algorithm to identify frequent items with limited memory.

  • How It Works

    • Divides stream into buckets.
    • Periodically prunes infrequent items.

  • Typical Use Case

    • Used in traffic monitoring, recommendation engines, analytics.

  • Load Balancer Consideration

    • Helps identify popular resources to cache or route to faster servers.

8. QuadTree

A 2D space partitioning tree for spatial data.

  • How It Works

    • Each node splits into 4 quadrants.
    • Efficient for locating nearby items.

  • Typical Use Case

    • Used in GIS systems, games, Google Maps rendering.

  • Load Balancer Consideration

    • Routes queries to region-specific backend servers.

9. Operational Transformation (OT)

Allows real-time collaborative editing with concurrent user changes.

  • How It Works

    • Transforms operations to maintain consistency.
    • Supports concurrent changes across replicas.

  • Typical Use Case

    • Used in Google Docs, Notion, code editors.

  • Load Balancer Consideration

    • Routes edits of the same document to the same cluster for consistency.

10. Leaky Bucket

A rate-limiting algorithm to enforce a consistent request rate.

  • How It Works

    • Requests flow into a “bucket”.
    • Leak out at a fixed rate.
    • Overflowing requests are dropped/delayed.

  • Typical Use Case

    • Used in API rate limiting, DoS protection, network shaping.

  • Load Balancer Consideration

    • Prevents traffic bursts from overwhelming backend services.

11. Rsync Algorithm

Efficient file synchronization via delta transfer.

  • How It Works

    • Uses rolling checksums.
    • Transfers only changed data blocks.

  • Typical Use Case

    • Used in Dropbox, CI/CD tools, backup systems.

  • Load Balancer Consideration

    • Distributes updated content efficiently across edge servers.

12. Ray Casting

Determines point-in-polygon using geometric rays.

  • How It Works

    • Casts a ray from the point.
    • Odd number of edge intersections → point is inside.

  • Typical Use Case

    • Used in geofencing, collision detection, rendering engines.

  • Load Balancer Consideration

    • Routes rendering or spatial queries to the correct backend node.

🧠 Conclusion

As a distributed systems specialist, mastering these algorithms prepares you for system design interviews and real-world scalable system challenges. These algorithms enable:

  • Scalability
  • High availability
  • Performance optimization
  • Efficient load balancing

Keep in mind:

  • Real-world systems often combine multiple algorithms.
  • Trade-offs must be carefully considered.
  • Load balancers are the gatekeepers of performance — they benefit directly from these algorithmic strategies.

Cheers,

Sim