What Is Memcached?
Memcached is an open-source, in-memory key-value store used to cache data and objects in RAM to reduce the number of times an external data source (such as a database or API) must be read. It’s designed for speed and efficiency, allowing applications to retrieve data quickly without the overhead of disk-based access.
Memcached operates as a simple remote hash table that stores strings and objects in memory. Clients can store and retrieve items using keys, useful for storing session data, API responses, user profiles, or data that is expensive to fetch or compute. It is widely used in distributed systems for horizontal scaling, with multiple servers deployed in parallel, and the client library handles key distribution. However, it has limited support for persistence or replication out of the box.
How Does Memcached Work?
Memcached works by storing data in memory as key-value pairs, allowing quick access without querying a slower backend data store. For example, one of the common caching strategies is cache-aside, also known as lazy loading. When an application needs data, it first checks Memcached. If the data is found (a cache hit), it’s returned immediately. If not (a cache miss), the application fetches the data from the primary source, stores it in Memcached, and then serves it to the user.
Internally, Memcached uses a slab allocator to manage memory efficiently. Memory is divided into chunks of different sizes, grouped into slabs. When an item is stored, it’s assigned to a slab class based on its size. This avoids memory fragmentation and ensures consistent performance.
Clients interact with Memcached over TCP or UDP using a simple text-based protocol (the legacy binary protocol is deprecated, together with its SASL authentication method). They can issue commands like get, set, delete, incr/decr, and cas (compare-and-swap). Libraries in most programming languages encapsulate these commands as functions, simplifying integration with web applications.
Memcached does not support persistence. Items are stored temporarily and can be evicted when memory is full, typically using a least recently used (LRU) policy. This makes it suitable for hot but volatile data that can be regenerated or refetched if needed.
Key Features and Benefits of Memcached
Memcached’s features focus on delivering high-performance caching through an efficient in-memory structure, easy integration, and scalable architecture.
Memcached Key Features
- In-memory storage: Memcached stores all data directly in RAM, allowing extremely fast read and write operations. This enables microsecond-level access times for cached content.
- Simple key-value architecture: Data is stored as key-value pairs, which simplifies both the architecture and the client interaction. Applications use unique keys to store and retrieve data efficiently.
- Distributed design: Memcached can be scaled horizontally by adding more servers. Client libraries handle consistent key distribution across multiple nodes, making it suitable for large-scale applications.
- Efficient memory management: Using a slab allocation mechanism, Memcached categorizes data by size and reduces memory fragmentation. This ensures stable performance even under high loads.
- Language-agnostic protocol: It communicates via a simple text-based protocol. Most major programming languages have robust client libraries, making it easy to integrate into diverse tech stacks.
- Limited persistence or replication: Memcached avoids disk I/O mostly, focusing primarily on speed. Although it provides warm restart and flash storage (keys in RAM, values on disk) options. It doesn’t provide built-in replication, which simplifies its operation but makes it unsuitable for critical data storage.
- Automatic eviction policy: When memory is full, Memcached evicts the least recently used (LRU) items. This ensures that the most accessed data remains available in the cache.
Memcached Primary Benefits
- Ultra-fast access: In-memory storage drastically reduces data access times compared to traditional databases.
- Reduces backend load: Offloads frequent queries from databases or APIs, improving overall system performance.
- Scalable architecture: Easily scales across multiple servers with client-side sharding.
- Simple to use: Straightforward API and wide language support make it easy to implement.
- Efficient memory use: Slab allocation minimizes memory fragmentation and maintains speed.
- Cost-efficient: By reducing expensive database queries, Memcached helps lower infrastructure costs.
- Self-cleaning cache: Automatic LRU-based eviction keeps the cache clean without manual intervention.
Top 4 Use Cases of Memcached
1. Database Query Caching
Memcached is commonly used to cache the results of resource-intensive database queries. When a user or application sends a query, the system first checks Memcached. If the result is present, it’s returned immediately (cache hit); otherwise, the application fetches it from the database (cache miss), stores it in Memcached, and then returns the data.
This significantly reduces database load, especially in high-traffic systems where the same queries are executed repeatedly. By avoiding repeated execution of complex SQL statements, it improves overall application performance and scalability. This is valuable for read-heavy workloads, dashboards, analytics views, and situations where query execution time is high.
2. Session Storage
Web applications use Memcached to store session data such as login state, user preferences, or temporary tokens. Since Memcached is an in-memory store, it allows near-instant access to session information across multiple application nodes. This enables a stateless architecture, which is crucial for scaling applications horizontally.
It is a good practice to separate session data from caching data used for other purposes. For systems that need speed and can afford transient data loss, Memcached provides a fast, simple solution for managing user sessions in distributed environments.
3. API Response Caching
Memcached can cache the results of expensive or rate-limited API calls, reducing the need to re-fetch or recompute the same data multiple times. When a response is requested, the application first checks Memcached to see if the data is already cached. If available, the response is served instantly; if not, the application calls the API, stores the result, and returns it.
This is especially useful when integrating with third-party services that impose strict rate limits or have slow response times. Caching API responses not only speeds up application response but also reduces dependency on external service availability and performance.
4. Content Caching
Memcached is effective for storing rendered content such as HTML fragments, full pages, JSON responses, or templated data. This is useful in content-heavy applications like CMS platforms, e-commerce sites, blogs, or forums where the same content is repeatedly accessed but doesn’t change frequently.
By caching pre-rendered content, the application reduces the need to regenerate pages or components for each request. This minimizes CPU usage, speeds up response times, and improves the user experience under load. It also simplifies load balancing and scaling since cached content can be served quickly from memory across nodes.
Limitations of Memcached
While Memcached is quite useful, it has a few limitations to be aware of.
Lack of Replication and High Availability
Memcached does not provide native support for replication or high availability. It is originally designed as a simple in-memory cache without built-in mechanisms for data replication or automatic failover, meaning that if one Memcached server fails, this particular instance needs to be recovered before continuing to serve traffic.
This limitation makes Memcached unsuitable for scenarios where high availability is critical. Applications must be designed to tolerate cache loss and fetch or recompute data when needed. Although some third-party tools are available to mitigate scenarios like this, it indeed adds complexity and operational overhead. As a result, Memcached is best used for transient or easily reproducible data.
No Built-in Security
Memcached does not provide built-in authentication, encryption, or access control. Anyone with network access to the Memcached instance can read, write, or delete cached data. This exposes sensitive data to potential misuse if proper network-level protections are not in place.
To use Memcached securely, administrators must rely on firewalls, private networks, or VPNs to restrict access. In cloud environments, it’s important to ensure Memcached is not publicly accessible and that only trusted services can reach it.
Simple Data Types
Memcached supports only basic key-value pairs, where values are treated as opaque binary or string data. It lacks support for more complex data structures like lists, sets, hashes, or sorted sets, which are available in systems like Redis.
Because memcached does not support atomic operations as part of composite structures and does not have advanced data types, developers might have to serialize and deserialize complex data or implement custom logic along with the cas operation to work around these limitations.
Redis vs. Memcached: The Key Differences
Redis and Memcached are two popular in-memory data stores, but they have important technical differences.
Data Structures
Redis provides a variety of native data structures beyond simple key-value storage. These include:
- Strings for simple text or binary values
- Lists for ordered sequences
- Sets for unique, unordered values
- Sorted Sets for ranked elements with scores
- Hashes for storing field-value pairs (similar to maps)
- Bitmaps and HyperLogLogs for compact data structures
- Streams for log-like data management
- Geospatial Indexes for location-based data
These structures allow developers to perform complex operations like list pushes, set unions, and sorted range queries directly on the server, with high performance and atomicity. This makes Redis suitable for use cases like task queues, recommendation engines, leaderboards, and real-time analytics.
Memcached supports only opaque binary or string values. Developers must serialize and deserialize structured data manually. There’s no support for atomic operations on composite structures, which limits its utility in applications that require data transformation or rich interactions.
Persistence
Redis offers two persistence mechanisms:
- RDB (Redis Database): Takes point-in-time snapshots of the dataset at specified intervals. This is efficient for backups but may lose recent data in case of a crash.
- AOF (Append Only File): Logs write operations received by the server. It can be configured to fsync to disk with different frequencies. AOF allows for more fine-grained durability than RDB.
- RDB and AOF can be used together to store point-in-time snapshots using RDB while capturing incremental writes using AOF, combining the strengths of both options.
Persistence in Redis is configurable, allowing users to balance performance and data safety. It can also be disabled entirely if used purely as a cache.
Memcached has limited support of persistence with persisting item-related data into an external mmap file in order to achieve warm restarts. Compared with Redis, Memcached’s persistence is less comprehensive and less flexible.
Replication & Clustering
Redis supports:
- Master-replica replication: One Redis instance can replicate data to one or more replicas. This is useful for scaling reads and data redundancy.
- Redis Sentinel: Provides high availability through monitoring, automatic failover, and notification mechanisms.
- Redis Cluster: Enables horizontal scaling by automatically sharding data across multiple nodes. It supports automatic partitioning and failover, making Redis suitable for distributed systems.
Memcached lacks built-in replication or clustering on the server side. It supports distributed caching through client-side sharding, where the client library determines which server to use for a given key. This provides scalability but not redundancy. If a Memcached node fails, its cached data is lost and must be repopulated by the application or by a warm restart.
Threading Model
Redis uses a single-threaded event loop for command execution, which simplifies concurrency and makes behavior predictable. Non-blocking I/O allows it to handle thousands of connections efficiently, and background threads are used for ancillary tasks like AOF rewriting and snapshot saving.
Memcached is multi-threaded by design. It can utilize multiple CPU cores to serve concurrent client requests, which improves throughput under high concurrency. This design makes Memcached better suited for workloads with large numbers of simultaneous connections and high read request rates, provided the application doesn’t require complex logic.
Scripting & Transactions
Redis includes a Lua scripting engine, enabling users to send scripts to be executed atomically on the server. This allows for complex multi-step operations—like updating multiple keys or computing values—to be performed safely without race conditions.
Redis also supports transactions through the MULTI, EXEC, DISCARD, and WATCH commands. These allow multiple operations to be queued and executed together, maintaining atomicity across keys.
Memcached does not support scripting or multi-key transactions. Operations are atomic at the single-key level. Developers can handle multi-key coordination in the application code along with cas-related operations.
Memory Management
Redis provides several eviction policies to manage memory when the max memory limit is reached. Options include:
noeviction: write commands return errorsallkeys-lru: evict least recently used keysvolatile-lru: evict least recently used keys with expiration set- Other random or LFU (least frequently used) policies
Redis also supports TTL-based expiration. This flexibility allows developers to tune memory behavior to match application requirements.
Memcached uses a slab allocator, where memory is divided into fixed-size slabs, each serving objects of a specific size class. This reduces fragmentation and maintains consistent performance. It also uses the LRU policy to ensure the oldest unused data is removed first when memory is full.
Maximum Value Size
Redis supports storing values up to 512 MB per value for a string or per element for a composite data type like a hash, accommodating large objects, serialized data, or binary blobs like media files. This makes it possible for use cases where caching of large payloads is necessary. Although this is not encouraged since large payloads can overload the Redis single thread, blocking all other incoming commands.
Memcached defaults to a 1 MB limit per value. This cap can be increased at startup using configuration flags (e.g., the -I option), but doing so may require tuning slab classes and memory allocation to avoid inefficiency or waste. This constraint limits Memcached’s usefulness for caching large objects unless additional handling is implemented.
Example: Using Memcached in your Application
To use Memcached in your application, you’ll need to install the Memcached server and then choose a client library to interact with the server from your application. Here’s an example of using the Python pymemcache client library to set and retrieve data from Memcached:
from pymemcache.client.base import Client
# Connect to Memcached server:
mc = Client(('127.0.0.1', 11211))
# Set a value in Memcached:
mc.set('my_key', 'my_value')
# Retrieve a value from Memcached:
value = mc.get('my_key')
print(value)In the above example, we first create a new client object, passing in the address and port number of our Memcached server. We can then use the set method to store a key-value pair in Memcached, and the get method to retrieve a value by its key.
Dragonfly: The Next-Generation In-Memory Data Store
Dragonfly is a modern, source-available, multi-threaded, Redis-compatible in-memory data store that stands out by delivering unmatched performance and efficiency. Designed from the ground up to disrupt existing legacy technologies, Dragonfly redefines what an in-memory data store can achieve. With Dragonfly, you get the familiar API of Redis without the performance bottlenecks, making it an essential tool for modern cloud architectures aiming for peak performance and cost savings. Migrating from Redis to Dragonfly requires zero or minimal code changes.
Key Advancements of Dragonfly
- Redis & Memcached API Compatibility: Offers seamless integration with existing applications and frameworks with zero code changes.
- Multi-Threaded Architecture: Efficiently leverages modern multi-core processors to maximize throughput and minimize latency.
- Unmatched Performance: Achieves 25x better performance than Redis, ensuring your applications run with extremely high throughput and consistent latency.
- Cost Efficiency: Reduces hardware and operational costs without sacrificing performance, making it an ideal choice for budget-conscious enterprises.
- Innovative Design: Built to scale vertically and horizontally, providing a robust solution for rapidly growing data needs.
Dragonfly Cloud is a fully managed service from the creators of Dragonfly, handling all operations and delivering effortless scaling so you can focus on what matters without worrying about in-memory data infrastructure anymore.
