Explanation of Components and Flow:
Client Application: This is your application (e.g., web server, microservice) that needs to read from or write to the database. It doesn't typically know about the individual shards directly.
Sharding Proxy / Query Router:
This is a crucial intermediary layer (sometimes built into the application, but often a separate service or a database's built-in feature, like MongoDB's mongos or ArangoDB's Coordinators).
Its main job is to abstract away the sharding complexity from the application.
It receives all database requests (reads and writes) from the client application.
Shard Key Logic:
Located within the Sharding Proxy or a closely integrated component.
This is where the sharding strategy is applied.
It determines which shard (or shards) a particular piece of data belongs to based on the shard key extracted from the query.
Configuration / Metadata Store:
A central repository that stores vital information about the sharded cluster:
Shard Mapping Rules: How the shard key values map to specific shards (e.g., "users with IDs 1-1000 go to Shard 1").
Shard Status & Location: Which shards are online, their network addresses, etc.
Data Distribution: Overall statistics on how data is distributed.
The Shard Key Logic consults this store to make routing decisions.
Shards (DB Instance 1, 2, ..., N):
These are individual, independent database instances.
Each shard holds a unique subset of the total data (a "logical shard" is the data subset, the DB instance is the "physical shard").
Each shard typically has the same schema as the original logical database.
Shards can themselves be replicated for high availability (e.g., a replica set for each shard).
Data Subset 1, 2, ..., N:
Represents the actual data stored on each corresponding shard.
Rebalancing / Admin Tools:
Tools and processes used by administrators or automated systems to manage the sharded cluster.
Rebalancing: As data grows or access patterns change, shards can become unbalanced (some "hotter" than others). Rebalancing involves moving chunks of data between shards to ensure an even distribution of load and storage.
Adding/Removing Shards: These tools facilitate scaling out or in the cluster by adding new database instances as shards or removing old ones.
Workflow for a Write Request (e.g., INSERT):
Client Application sends an INSERT query to the Sharding Proxy.
The Sharding Proxy extracts the relevant shard key value from the data to be inserted.
The Shard Key Logic consults the Configuration/Metadata Store to determine which specific Shard this data should reside on based on the shard key and mapping rules.
The Sharding Proxy routes the INSERT query directly to the identified Shard.
The Shard processes the write and stores the Data Subset on its local storage.
Workflow for a Read Request (e.g., SELECT):
Client Application sends a SELECT query to the Sharding Proxy.
The Sharding Proxy extracts the shard key (if present in the query conditions).
The Shard Key Logic determines if the query can be fulfilled by a single shard or if it needs to go to multiple shards:
Single Shard Query: If the shard key is part of the query condition (e.g., SELECT * FROM users WHERE user_id = 'XYZ'), the Shard Key Logic identifies the specific Shard where user_id 'XYZ' resides. The query is then routed directly to that shard, which returns the result. This is the most efficient type of sharded query.
Multi-Shard / Fan-out Query: If the query does not include the shard key (e.g., SELECT * FROM users WHERE city = 'New York') or requires aggregating data across multiple partitions (e.g., SELECT COUNT(*) FROM users), the Sharding Proxy might "fan out" the query to all relevant Shards. Each shard executes its portion of the query, and the Sharding Proxy collects and aggregates the results before returning them to the client. This type of query is generally less efficient due to network overhead and potential data aggregation complexity.
This diagram provides a high-level overview of how sharding works to distribute data and queries in a scalable database system.
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