This project provides a fully dockerized setup for Redis, including different deployment configurations:
This is a single-instance Redis deployment.
cd standalone
docker-compose up -dThis setup includes one Redis master and one or more replicas.
cd master_replica
docker-compose up -dThis setup includes a Redis master, replicas, and three Sentinel instances to monitor failover.
cd sentinel
docker-compose up -d- ⚡ In-memory storage: Redis keeps data in memory, ensuring high-speed read/write operations.
- 📂 Data structures: Supports strings, hashes, lists, sets, sorted sets, bitmaps, and more.
- 🛠️ Persistence: Supports RDB snapshots and AOF logging for durability.
- 🔁 Replication: Allows master-replica replication for data redundancy.
- 🏗️ High availability: Sentinel provides automatic failover for a resilient setup.
- 🌍 Clustering: Enables horizontal scaling via sharding.
Redis is a high-performance, in-memory key-value store designed for speed and efficiency. Here are some key aspects of its architecture:
- 🚀 Single-threaded core: Redis processes all commands using a single-threaded event loop, avoiding context switching overhead and ensuring predictable performance.
- 🌋 Blazing-fast operations: Due to its in-memory nature, Redis achieves sub-millisecond response times for most operations.
- 🛑 Atomic commands: All Redis commands are atomic, ensuring that operations are completed in a single step.
- 📝 Data durability options: Redis supports both RDB snapshots (point-in-time persistence) and AOF (Append-Only File) logging for durability.
- 🔄 Replication & failover: Redis supports master-replica replication and Sentinel-based automatic failover for high availability.
- 🔀 Sharding for scalability: Redis Cluster enables horizontal scaling by partitioning data across multiple nodes.
For more details, refer to the official Redis documentation: Redis.io
- The application checks Redis for the data first.
- If not present, it queries the database, updates Redis, and returns the data.
- Used for read-heavy workloads with occasional writes.
- The application writes data to Redis and the database simultaneously.
- Ensures consistency between Redis and the database.
- Suitable for scenarios where data consistency is critical.
- The application writes to Redis first, deferring database updates.
- Redis periodically flushes updates to the database.
- Improves write performance but may cause temporary inconsistencies.
- Redis automatically removes the least recently used keys when memory is full.
- Helps manage cache size efficiently.
- Can be configured with policies like
volatile-lru,allkeys-lru, etc.
- Keys can have an expiration time set.
- Ensures stale data is automatically removed.
- Useful for session storage and temporary caches.
- Multiple Redis instances can be used to distribute cache load.
- Commonly used in large-scale applications with high concurrency.
Each Redis instance uses its respective redis.conf file, located within the conf directories.
Each Redis instance stores data in the data/ directory, ensuring persistence across restarts.
This project is licensed under The Unlicense. See the LICENSE file for details.