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Sharding is the splitting of a database or workload into fragments (shards) distributed across different machines, each responsible for a portion of the data. When a dataset becomes too large or too hot for a single server, you break it up: each shard is smaller, faster and can be managed in parallel with the others.
The split follows a shard key: for instance, users with initials A-M on one server and N-Z on another, or more commonly a hash function on the ID that spreads records evenly. A routing layer knows which shard to query for each request. The challenges are well known: queries spanning multiple shards, rebalancing when a shard grows too large, and distributed transactions.
In AI, sharding operates on two fronts: vector databases fragment billions of embeddings across nodes to keep semantic search fast, and distributed training of large models shards weights and optimizer state across GPUs (as in FSDP and ZeRO techniques). Beyond AI, social networks and e-commerce platforms have sharded user data for decades.
Shard means a fragment of pottery or glass, from Old English sceard. Several stories circulate about the computing usage: it is documented that the online game Ultima Online (1997) called its parallel worlds shards, and some link the technical term to that narrative choice, though the attribution is debated. What is certain is that the practice exploded in the 2000s with the growth of the web giants.
Grace splits the learning path into small, parallel units, 1515 courses and a daily gym, an approach reminiscent of sharding: manageable fragments instead of a monolithic block. In the scenarios on data and infrastructure, the term helps you write technically accurate prompts.
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