{
  "id": 251561,
  "title": "One Database Can't Hold Everything: Learn Database Sharding",
  "url": "https://urgent.news/2026/08/07/one-database-cant-hold-everything-learn-database-sharding",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-08-07T05:46:43.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/aditya_d_sharma/one-database-cant-hold-everything-learn-database-sharding-442b"
  },
  "original_language": "en",
  "account": "The ability of a single database to handle an ever-increasing amount of data is limited. While adding more application servers and caching layers can address traffic growth, the sheer volume of data eventually becomes a bottleneck. As the database grows, backups take longer, indexes consume more memory, and routine maintenance operations become risky and time-consuming. Upgrading hardware by adding more RAM, faster disks, or CPU cores provides temporary relief but ultimately reaches its physical and economic limits. Shrinking the data by vertically scaling the server, from a small machine to larger ones, also becomes increasingly expensive and offers diminishing returns. The fundamental issue remains: the data itself is too large to be efficiently managed in a single database. This realization leads to the idea of database sharding, which involves dividing the data across multiple databases based on a specific criterion, such as user IDs. By splitting the data into smaller, more manageable chunks, each database handles a fraction of the total data, resulting in smaller indexes, faster backups, and reduced write loads on individual databases. Each shard can be located on its own server, allowing for parallel processing and improved scalability. Sharding transforms the approach from simply trying to make the database bigger to actively splitting the data across multiple databases, addressing the core problem of data management as the application scales from a single user to millions of users.",
  "summary": "This is Part 9 of my \"From One User to One Million\" series, where we'll build an understanding of System Design by following a simple application as it grows from a single user to millions. Instead of memorising technologies, we'll learn why they exist by solving real problems as they appear. Look at what we've built so far. We started with a single server. We added more application servers when…",
  "key_points": [
    "Single database faces data management limitations as application scales",
    "Sharding divides data across multiple databases based on criteria",
    "Sharding improves scalability by enabling parallel processing"
  ],
  "editors_take": "Database sharding addresses the core problem of data management by transforming the approach from scaling up a single database to splitting data across multiple databases for improved scalability.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}