30 Memcached Interview Questions and Answers

Memcached is an open-source, high-performance, distributed memory object caching system. Its primary purpose is to speed up dynamic web applications by reducing database load through caching data and objects in RAM.

Memcached stores frequently accessed data in memory, allowing applications to retrieve data much faster than querying a database. This reduces latency and the number of database queries, leading to improved application performance.

Data in Memcached is stored as key-value pairs. Applications store data by assigning it a unique key, and later retrieve the data using the same key. Memcached uses a hash table to efficiently map keys to values.

Common use cases include caching database query results, session data, API responses, and rendered web pages. This helps reduce database load and speeds up response times for users.

Memcached allows you to set an expiration time for each key-value pair. When the memory limit is reached, it uses a Least Recently Used (LRU) algorithm to evict old or unused data to make space for new entries.

Memcached is not persistent. All cached data is stored in memory only, so if the server restarts or crashes, all data in Memcached is lost.

Memcached uses a technique called consistent hashing to distribute keys across multiple servers. This ensures that data is evenly distributed and allows for horizontal scaling.

Memcached does not support complex queries, transactions, or data persistence. It is best suited for simple key-value caching and is not a replacement for a database.

Memcached is designed to handle many concurrent connections efficiently. It uses a multi-threaded architecture to allow multiple clients to read and write data simultaneously.

Both are in-memory key-value stores, but Redis supports more complex data types, persistence, and replication, while Memcached is simpler and focuses on high-speed caching of simple key-value pairs.

The slab allocator is Memcached's memory management system that divides memory into chunks of fixed sizes called slabs. This prevents memory fragmentation and ensures efficient storage and retrieval of objects of varying sizes.

Memcached does not provide built-in data consistency mechanisms like replication or transactions. Consistency must be managed at the application level, often by ensuring that updates to the cache and the underlying database are coordinated.

Cache invalidation can be done by explicitly deleting keys, setting expiration times, or using versioning. Strategies include time-based expiration, manual invalidation after data updates, or using cache-aside patterns to refresh data.

To prevent cache stampede, you can use techniques like request coalescing (only one request regenerates the cache), locking (mutex keys), or pre-warming the cache. These approaches help avoid overwhelming the backend when a cache entry expires.

Monitoring involves tracking metrics like hit/miss ratio, memory usage, evictions, and response times. Tuning may include adjusting slab sizes, memory allocation, concurrency settings, and optimizing key distribution across servers.

Memcached does not have built-in authentication or encryption. It should be deployed in a trusted network, access should be restricted via firewalls, and sensitive data should not be stored in the cache.

Memcached has a configurable maximum item size (default 1MB). Large objects must be split into smaller chunks at the application level, or alternative storage solutions should be considered for very large data.

Consistent hashing allows Memcached to distribute keys evenly across servers. When servers are added or removed, only a small subset of keys need to be remapped, minimizing cache misses and making scaling more efficient.

Using Memcached across regions can introduce latency and consistency issues. It's generally recommended to deploy separate Memcached clusters per region and synchronize data at the application or database level.

Start by analyzing cache usage patterns, checking for key mismatches, short expiration times, or insufficient memory. Review application logic for proper cache population and invalidation, and monitor for evictions or network issues.

Scaling Memcached clusters involves challenges such as uneven key distribution, network bottlenecks, and increased complexity in managing nodes. Solutions include using consistent hashing, sharding data effectively, monitoring node health, and automating node addition/removal to minimize downtime and cache misses.

Since Memcached does not provide built-in replication or persistence, fault tolerance must be achieved at the application level. This can involve using multiple Memcached clusters, implementing fallback mechanisms to the database, and designing the application to repopulate the cache after failures. Monitoring and alerting are also crucial for rapid recovery.

Network partitions can cause some clients to lose access to certain Memcached nodes, leading to increased cache misses and potential data inconsistency. Mitigation strategies include deploying Memcached within a single trusted network, using redundant clusters, and designing the application to handle cache failures gracefully.

Optimization techniques include tuning network stack parameters, using high-performance instance types, adjusting Memcached thread and connection settings, and colocating Memcached nodes with application servers to reduce latency. Monitoring and autoscaling can help maintain performance under variable loads.

Cache versioning involves associating a version number with cached data. When data changes, the version is incremented, and the cache key is updated, ensuring clients always fetch the latest data. Invalidation can be triggered by data updates, using publish/subscribe mechanisms or event-driven architectures to keep the cache in sync.

Security measures include network isolation (VPCs, firewalls), running separate Memcached instances per tenant, using access control lists at the network level, and encrypting traffic between clients and Memcached using stunnel or similar tools. Avoid storing sensitive data in the cache.

Memcached offers high performance and simplicity but lacks persistence and advanced data structures. Redis provides persistence, replication, and richer data types, making it more suitable for critical session data. However, Redis may introduce more operational complexity and slightly higher latency.

Memory fragmentation can occur when objects of varying sizes are cached and evicted over time. To resolve this, monitor slab allocation statistics, adjust slab class sizes, periodically restart Memcached during maintenance windows, or use tools to rebalance memory allocation between slab classes.

Stale data can occur if the cache is not invalidated after an underlying data change. To prevent this, implement cache-aside patterns, use short expiration times for volatile data, and ensure that all data-modifying operations also update or invalidate the relevant cache entries.

Benchmarking involves using tools like memtier_benchmark or custom scripts to simulate various workloads (read-heavy, write-heavy, mixed). Measure metrics such as throughput, latency, hit/miss ratio, and resource utilization. Compare results across different configurations, instance types, and network setups to identify bottlenecks and optimize performance.