What is persistent?

Persistence means keeping data around even after the program that created it stops running. In tech, persistent data is stored on a medium like a hard drive, SSD, or cloud storage so it can be read later, unlike temporary data that lives only in RAM while the app is active.

Let's break it down

  • Memory vs. storage: RAM is fast but volatile; when power is lost, everything disappears. Persistent storage (disk, SSD, database) retains information.
  • Types of persistence: files on a filesystem, records in a database, key‑value stores, object serialization, cloud buckets, etc.
  • How it works: The program writes data to a persistent medium using APIs (e.g., file I/O, SQL queries). Later it reads the same data back, reconstructing the previous state.

Why does it matter?

  • Data durability: Users expect their work, settings, and history to survive crashes or restarts.
  • Recovery: Persistent logs let systems recover from failures and continue where they left off.
  • Business value: Sales records, user profiles, and analytics depend on data that persists over time.

Where is it used?

  • Databases (MySQL, PostgreSQL, MongoDB) storing application data.
  • File systems for documents, images, videos, and configuration files.
  • Mobile and desktop apps saving preferences, game progress, or offline caches.
  • Web services persisting session tokens, user accounts, and transaction logs.
  • IoT devices writing sensor readings to local flash or cloud storage.

Good things about it

  • Reliability: Information isn’t lost when a program crashes or a device restarts.
  • Continuity: Users can pick up where they left off, improving experience.
  • Auditability: Persistent logs provide a history for debugging, compliance, and analytics.
  • Scalability: Centralized persistent stores let many services share the same data.

Not-so-good things

  • Performance cost: Writing to disk or a remote database is slower than using RAM.
  • Complexity: Managing schemas, migrations, backups, and concurrency adds development overhead.
  • Consistency challenges: Distributed persistence can lead to stale or conflicting data if not handled correctly.
  • Storage expense: Large amounts of persistent data require more hardware or cloud storage fees.