How does Redis store data?

Redis is an in-memory data store. It stores data primarily in RAM, allowing extremely fast read and write operations. The data is organized using key-value pairs, where keys are unique identifiers, and values can be of different types, including strings, lists, sets, sorted sets, hashes, bitmaps, and more. Data in Redis is accessed by keys, making it a highly efficient and simple data store.

Data Structures in Redis

Redis supports a wide range of data structures, making it a versatile data store. These data structures include:

• Strings: Basic key-value storage for text or binary data.
• Lists: Ordered collections of strings, allowing for operations like push, pop, and range retrieval.
• Sets: Unordered collections of unique strings with set operations like union, intersection, and difference.
• Sorted Sets: Sets with associated scores, used for ranked data.
• Hashes: Key-value pairs within a key, suitable for storing structured data.
• Bitmaps: Efficiently store and manipulate binary data.

Note: Each data structure has specific use cases, and Redis provides various commands for manipulating and querying these structures.

Persistence Options in Redis

Redis offers different persistence options to ensure data durability:

• RDB Snapshot: Redis periodically saves a point-in-time snapshot of the dataset to disk, which can be configured at intervals. RDB snapshots provide good disk space efficiency.
• AOF (Append-Only File): Redis logs every write operation in an append-only log file. This log can be used to reconstruct the dataset in case of data loss. AOF is more durable but can be larger in size.
• Hybrid Persistence: Redis allows you to use a combination of RDB and AOF for both efficiency and durability.

Partitioning in Redis

Redis provides horizontal partitioning (sharding) for distributing data across multiple instances. This is useful for handling large datasets and achieving high throughput. Each shard (instance) is responsible for a subset of the keys, and Redis Cluster provides built-in support for partitioning. It is used to distribute data across multiple Redis instances (also known as nodes) to improve performance, scalability, and fault tolerance. Instead of relying on a single Redis server, partitioning allows you to divide your dataset and workloads among several Redis nodes, enabling you to harness the combined processing power of these nodes.

Virtual Memory in Redis

Virtual memory in Redis allows the database to store and manage data larger than the available physical RAM. It swaps less frequently accessed data to disk, freeing up RAM, but this can slow down access times since disk read is slower than RAM. Consider performance trade-offs and alternatives for large datasets.

Data Eviction

To prevent memory exhaustion, Redis uses various strategies for data eviction when it reaches its memory limit. Common eviction policies include:

• LRU (Least Recently Used): Removes the least recently accessed keys first.
• LFU (Least Frequently Used): Removes the least frequently accessed keys.
• TTL (Time To Live): Keys with an expiration time are removed when they expire.
• No Eviction: Redis can be configured to reject write operations when memory is full, ensuring no data loss.

These eviction policies help Redis maintain optimal memory usage while preserving essential data.