An Engineer's VaultIdeas for systems that scale
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A growing collection of practical articles on systems, architecture, and backend engineering.

System Architecture

Designing an Email Service at Scale

Ingest billions of emails with zero data loss, shard petabyte mailboxes, and protect edge deliverability at scale.

This article answers
  • Why is accepting an email an irreversible custody contract?
  • Why should message brokers never carry raw email bodies?
  • How do you guarantee a user immediately sees the email they just sent?
  • Why can an automated bounce turn your server into a spam cannon?
Distributed SystemsEmailSMTPCassandraKafkaS3SecuritySystem Design
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System Architecture

Design a Metrics Monitoring System

Durable metric ingestion, bounded time-series queries, stateful alert evaluation, and recoverable notification delivery.

This article answers
  • Where is the boundary between accepting a metric and showing it on a dashboard?
  • How can an alert distinguish a sustained problem from a brief spike or missing data?
  • How do rollups preserve useful history after raw metrics expire?
  • Why must notification systems handle duplicate delivery after a retry?
Distributed SystemsMetricsMonitoringAlertingKafkaFlinkInfluxDB
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System Architecture

Design a Real-Time Gaming Leaderboard

Idempotent scoring, fast exact-rank reads, and safe recovery from retries, cache loss, and regional failover.

This article answers
  • How do score retries avoid awarding points twice?
  • How can Top K and exact rank stay fast at scale?
  • What survives Redis loss or an event replay?
  • When should an exact global rank become a sharded or regional design?
Distributed SystemsLeaderboardsRedisKafkaEvent StreamingReliability
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System Architecture

Design a Durable Distributed Message Broker

See how a message broker decouples services by persisting events, letting consumers work and replay history independently, and recovering safely after failures.

This article answers
  • What happens after the broker says "accepted"?
  • How does a stream stay ordered at scale?
  • How does processing recover after a consumer crash?
  • How can teams replay the same history independently?
Distributed SystemsMessage BrokersEvent StreamingReplicationReliability
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System Architecture

How Google Drive Syncs Files and Resolves Concurrent Edits

Trace the mechanisms behind reliable file sync: incremental uploads, version checks, and conflict copies that preserve both edits.

This article answers
  • How does Drive avoid uploading an entire file for a small change?
  • Who decides which version becomes the source of truth?
  • How can an offline device safely catch up later?
Distributed SystemsFile StorageSyncConflict ResolutionReliability
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