System Design – Design Memcache System on A Single Machine

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Memcached is an open-source, high-performance, distributed memory object caching system. It’s primarily used to speed up dynamic web applications by alleviating database load. Memcached accomplishes this by caching data and objects in RAM to reduce the number of times an external data source (like a database or API) must be read. Use Cases

  • Caching Database Queries: Reducing database load by caching frequent queries.
  • Caching Objects: Storing objects, sessions, or HTML fragments for web applications.
  • API Rate Limiting: Caching API responses or using counters in Memcached for rate limiting.

(Image source: https://dzone.com/articles/memcached-a-distributed-memory-object-caching-syst)

Designing a memcache system like memcached on a single machine involves several important considerations to ensure it works efficiently and without any downtime. Here are the key factors to consider:

Hardware and Software and Operating System:

  • Memory: Sufficient RAM is crucial as memcache stores data in memory. The amount depends on the cache size needed.
  • CPU: A powerful CPU can handle more concurrent connections and requests.
  • Disk Space: Although memcache is in-memory, disk space is needed for logging and OS operations.
  • Network Bandwidth: High bandwidth ensures quick data transfer between the cache and clients.
  • Choose an OS with stable support for networking and memory management.
  • Ensure that the memcached version is compatible with your OS and is up to date for security and performance enhancements.

Memcache Configuration:

  • Memory Allocation: Allocate enough memory for the cache but leave room for the OS and other processes.
  • Eviction Policy: Configure an appropriate eviction policy for when the cache is full (e.g., LRU – Least Recently Used).
  • Connection Limit: Set the maximum number of concurrent connections to prevent overload.

Key Algorithms & Techniques

  1. Hashing: Implement an efficient hashing algorithm (like MurmurHash or FNV) for key mapping.
  2. Eviction Policy: Use LRU (Least Recently Used) or a similar strategy for data eviction.
  3. Serialization/Deserialization: Optimize this process for performance, considering the data types frequently stored.
  4. Data Compression: Implement on-the-fly data compression to save memory space, balancing the trade-off between CPU usage and memory savings.

Performance Optimization

  1. Connection Pooling: Manage connections effectively to reduce overhead.
  2. Thread Management: Efficient thread management to handle concurrent connections.
  3. Memory Management: Optimize memory usage and allocation to prevent fragmentation.

Scalability

  1. Vertical Scaling: Design the system to be easily upgradable in terms of hardware resources.
  2. Modular Design: Although initially non-distributed, design software components to be easily extendable to a distributed system in the future.

High Availability and Fault Tolerance:

  • Implement failover strategies (like replication to a secondary machine) even though the primary setup is on a single machine.
  • Regularly test failover mechanisms to ensure they work as expected.

Documentation & Best Practices

  1. Documentation: Maintain comprehensive documentation for system configuration, maintenance procedures, and troubleshooting guidelines.
  2. Coding Standards: Follow best practices in software development to ensure maintainability and scalability.

Data Partitioning:

  • Partition data efficiently to optimize cache usage and retrieval speed.
  • Avoid large objects that could consume excessive memory and lead to inefficiencies.

Load Balancing:

  • Even on a single machine, distributing load across multiple memcached instances can enhance performance.
  • Use consistent hashing for key distribution to minimize cache misses during scaling or failures.

Backup and Recovery:

  • Regularly backup configuration and log files.
  • Although memcache is volatile, consider snapshot mechanisms if necessary.

Monitoring and Logging:

  • Implement monitoring tools for memory usage, hit rates, evictions, and other performance metrics.
  • Plan for regular maintenance and updates without causing downtime.

Security:

  • Implement network security measures to restrict unauthorized access.
  • Use secure connections between clients and the cache server.

Core Cache Algorithms

When it comes to memcached or similar in-memory caching systems, several algorithms play a crucial role in their efficiency and performance. Here are some key algorithms and concepts that are often associated with such systems:

  1. Hashing Algorithm:
    • Used to determine where to store or retrieve data in the cache.
    • Common algorithms include MurmurHash, CRC32, and FNV (Fowler–Noll–Vo).
    • The choice of hashing algorithm impacts the distribution of data and collision rates.
  2. Eviction Policies:
    • Determine which data to remove when the cache is full.
    • LRU (Least Recently Used): Evicts the least recently accessed item.
    • LFU (Least Frequently Used): Removes the least frequently accessed item.
    • Random Replacement: Randomly selects an item for eviction.
    • Slab Allocation: Specific to memcached, this algorithm manages memory allocation by dividing it into slabs of different sizes.
  3. Consistent Hashing:
    • Important for distributed caching systems.
    • Minimizes cache invalidation when a cache node is added or removed.
    • Provides a more uniform distribution of keys across the cache nodes.
  4. Concurrency Control:
    • Algorithms to handle multiple clients accessing and modifying data simultaneously.
    • Lock-free algorithms and atomic operations to ensure data consistency.
    • Techniques like optimistic locking can also be used.
  5. Serialization and Deserialization Algorithms:
    • Transform objects into a format that can be stored in the cache and vice versa.
    • Common formats include JSON, XML, and binary formats.
    • Efficient serialization can reduce memory footprint and speed up storage/retrieval.
  6. Compression Algorithms:
    • Used to reduce the size of the cached data.
    • Algorithms like zlib, LZO, or Snappy can be employed.
    • Balances the trade-off between CPU cycles for compression/decompression and memory savings.
  7. Load Balancing Algorithms:
    • In a distributed environment, algorithms to distribute load evenly across cache nodes.
    • Round-robin, weighted distribution, or adaptive algorithms based on node performance.
  8. Data Partitioning Strategies:
    • Sharding algorithms to divide data across different nodes or instances.
    • Ensures efficient use of resources and improves access speed.
  9. Bloom Filters:
    • Probabilistic data structure to test whether an element is a member of a set.
    • Useful for quickly checking if a key is not in the cache, thus avoiding unnecessary cache lookups.
  10. Garbage Collection Algorithms:
    • For memory management, especially in systems that use persistent memory.
    • Automatic memory reclamation techniques to free up space occupied by unused data.

Data Persistence Strategies

  1. Write-Through Cache:
    • Data is written to the cache and the backend storage simultaneously.
    • Ensures data persistence but may be slower due to write latency.
  2. Write-Around Cache:
    • Data is written directly to persistent storage, and only loaded into the cache on subsequent accesses.
    • Reduces cache pollution with write-intensive workloads.
  3. Write-Back Cache:
    • Data is written to the cache and marked as ‘dirty’. It’s only written to the backend storage at specified intervals or when evicted.
    • Faster writes but risks data loss if the cache fails before syncing.
  4. Snapshot and Backup:
    • Regular snapshots of the cache can be taken and stored in persistent storage.
    • Useful for recovery purposes but may not always reflect the most current state.
  5. Logging Changes:
    • Changes to data in the cache can be logged and applied to the persistent store asynchronously.
    • Balances performance and data consistency.
  6. Hybrid Approaches:
    • A combination of above techniques based on specific use cases and requirements.
    • Can be tailored for optimal performance and reliability.

Key-Value Store Operations

The core of the memcache system is its ability to store, retrieve, and manage key-value pairs.

  1. Set a Value
    • Endpoint: /set
    • Method: POST
    • Request Body: JSON containing key, value, and optional ttl (time-to-live)
    • Response: Success or error message
    • Description: Stores a value associated with a key in the cache. If ttl is provided, the key expires after the specified time.
  2. Get a Value
    • Endpoint: /get/{key}
    • Method: GET
    • Response: Value associated with the key or an error if the key does not exist.
    • Description: Retrieves a value by its key.
  3. Delete a Key
    • Endpoint: /delete/{key}
    • Method: DELETE
    • Response: Success or error message
    • Description: Deletes a key-value pair from the cache.
  4. Update a Value
    • Endpoint: /update
    • Method: PUT
    • Request Body: JSON containing key, new value, and optional new ttl
    • Response: Success or error message
    • Description: Updates the value (and optionally, the TTL) of an existing key.
  5. Flush All
    • Endpoint: /flush
    • Method: POST
    • Response: Success or error message
    • Description: Clears all data from the cache.

Designing a data scheme for a single-node memcache system involves defining the structure and format of data that the system will store and manage. Memcache typically operates as a key-value store, so the data scheme is relatively straightforward. However, it’s important to plan for efficient storage, quick access, and potential future extensions.

Life Cycle Of Data

Initialization: When a piece of data (key-value pair) is first stored in Memcached, it is typically through a set command issued by a client application. Memcached allocates memory for this data. The allocation is based on the slab allocator mechanism, which organizes memory in slabs of different sizes. Once the value is stored, the Memcached server sends an acknowledgment to the client, typically a simple response like STORED.

Retrieval: Data can be retrieved using a get command. If the requested key exists and has not expired, Memcached returns the value. If the key is not found or the value has expired, the server sends a NOT FOUND message or simply no data.

Modification: Data can be modified or overwritten with commands like set, add, replace, append, prepend, and cas (check-and-set).

Expiration

  • TTL (Time-to-Live): When data is stored, a TTL can be specified. This is the duration for which the data should remain in the cache. If a TTL is not set, the data might persist indefinitely, or until it is explicitly deleted or evicted.
  • Automatic Expiry: When the TTL expires, the data is considered invalid. Memcached does not immediately delete expired items but marks them as invalid. The memory occupied by expired data is reclaimed when new data needs to be stored.

Eviction

  • Memory Pressure: If Memcached reaches its memory limit, it may need to evict data to make room for new data.
  • Eviction Policy: Memcached typically uses an LRU (Least Recently Used) algorithm for eviction. Data that hasn’t been accessed recently is more likely to be evicted.
  • Eviction Process: Eviction happens when a new piece of data is being stored, and there is not enough memory available. The system selects an old or least-used item to evict based on its policy.

Deletion

  • Manual Deletion: Data can be explicitly removed from the cache using a delete command. This is done by the client application.
  • Automatic Deletion: Besides eviction, data is also automatically deleted when it expires (post-TTL).

Memory Reclamation

  • Memory Reclamation: The memory occupied by evicted or expired data is not immediately freed but is marked for reclamation. When new data comes in, Memcached uses this reclaimed space.
  • Slab Rebalancing: Memcached can move slabs between classes (sizes) for better memory utilization, a process known as slab rebalancing.

System Restart or Flush

  • Cache Clearing: The flush command can be used to clear all data from the cache.
  • Server Restart: If the Memcached server restarts, all stored data is lost, as it does not persist data to disk.

Basic Key-Value Structure

  1. Key:
    • Type: String
    • Constraints: Unique identifier for each cache entry. Length and character set limitations may be applied based on system requirements.
    • Usage: Used to retrieve the corresponding value.
  2. Value:
    • Type: Binary/blob
    • Usage: The data associated with the key. Could be strings, serialized objects, JSON, etc.
    • Size Limitation: Memcache typically limits the size of a value (e.g., 1MB). This can be configured based on memory availability and use case.

Additional Metadata

Along with the key and value, storing some metadata can help manage the cache more effectively.

  1. Time-to-Live (TTL):
    • Type: Integer (timestamp or duration)
    • Usage: Determines how long the key-value pair should be stored in the cache. When the TTL expires, the entry is eligible for eviction.
  2. Creation Time:
    • Type: Timestamp
    • Usage: Records when the key-value pair was added to the cache. Useful for LRU (Least Recently Used) and other eviction policies.
  3. Last Accessed Time:
    • Type: Timestamp
    • Usage: The time when the key was last accessed. Important for implementing LRU eviction.
  4. Data Size:
    • Type: Integer
    • Usage: Size of the value. Useful for monitoring and managing memory usage.
  5. Reference Count (Optional):
    • Type: Integer
    • Usage: Tracks how many times a key is accessed. Can be used for LFU (Least Frequently Used) eviction policies.

Archiving Higher Availability and Fault Tolerance

Achieving high availability and fault tolerance in a single-node memcache system poses a unique challenge, as the traditional methods involve using multiple nodes or replicas. However, there are still strategies that can be employed to maximize uptime and minimize data loss in a single-node setup.

1. Robust Hardware Setup

  • Use Reliable Hardware: Invest in high-quality, enterprise-grade hardware with a reputation for reliability.
  • ECC (Error-Correcting Code) Memory: Reduces the likelihood of data corruption.
  • Redundant Power Supplies: Protects against power supply failures.
  • UPS (Uninterruptible Power Supply): Ensures the system remains operational during short power outages and provides a safe shutdown during extended outages.

2. Regular Snapshotting and Data Backup

  • Automated Backups: Regularly back up the cache data to another system or persistent storage. This won’t prevent downtime but will prevent data loss.
  • Backup Frequency: The frequency should align with how often the data changes and the acceptable data loss window.
  • Periodic Snapshots: Regularly save the state of the cache to allow for quicker recovery in case of a failure.

3. System Redundancy

  • RAID Configuration for Disks: Use RAID (such as RAID 1 or RAID 10) for disk redundancy to protect against disk failures.
  • Network Redundancy: Employ multiple network interfaces and paths to avoid network-related downtime.

4. Failover Mechanisms

  • Secondary Standby System: Have a secondary system that can quickly take over in case the primary system fails. This is a form of passive redundancy.
  • Heartbeat Monitoring: Implement a monitoring system that regularly checks the health of the memcache server and can trigger a failover if needed.

5. Security Measures

  • Preventative Security: Implement robust security measures to protect against cyber attacks, which can be a common cause of downtime.

6. Regular Maintenance and Monitoring

  • System Updates: Keep the operating system and memcache software up to date with the latest patches.
  • Monitoring Tools: Use monitoring tools to track system health, performance metrics, and potential issues.

Handle Conflicts

Avoiding race conditions in a memcache system, or any caching system, is crucial for maintaining data integrity and ensuring consistent behavior. A race condition occurs when multiple processes access and manipulate the same data concurrently, leading to unpredictable results. Here are some strategies to avoid race conditions:

1. Locking Mechanisms

  • Pessimistic Locking: Lock the data when it is being read or written. Other processes must wait until the lock is released. This can be implemented at the application level where each client ensures that it locks data before access.
  • Optimistic Locking: Use versioning or timestamps for data items. When updating an item, check if the version/timestamp matches the one read initially. If not, it indicates that another process has modified the data, and the current operation should be retried or aborted.

2. Atomic Operations

  • Implement atomic operations in memcache for critical actions. Operations like ‘increment’ or ‘decrement’, which are common in caching scenarios, should be atomic to ensure consistency.
  • Memcache provides certain atomic operations out-of-the-box, such as add (only adds if the key does not exist) and cas (check and set, a form of optimistic locking).

3. Transaction Mechanisms

  • While memcache itself does not support transactions like a database, you can design transaction-like mechanisms in your application logic. This involves a sequence of operations where you check for conditions before committing an update.

How Memcached Handle Conflicts

  • Simple Data Model Memcached uses a basic key-value storage model without relational data structures, transactions, or complex query capabilities. This simplicity reduces the potential for conflicts inherent in more complex systems.
  • Thread-Safe Operations Memcached is designed to be thread-safe. It handles concurrent connections using a non-blocking I/O model and employs internal locking mechanisms to ensure that individual operations on the cache (like get, set, delete) are atomic.
  • No Locking Mechanism for Clients Memcached itself does not provide a built-in feature for clients to lock items. It operates under a “last write wins” model.
  • CAS (Check And Set) Operation Memcached offers a CAS operation, which is a form of optimistic locking. CAS allows a client to avoid overwriting data that has been changed by another client since it was last fetched.
  • Atomic Counters Memcached supports atomic increment and decrement operations, which are useful for counters
  • Statelessness Memcached servers are essentially stateless with respect to client connections.
  • Client-Side Responsibility Conflict resolution, to a large extent, is left to the client. Applications using Memcached are responsible for implementing additional logic if more sophisticated conflict handling is required (e.g., by using versioning or timestamps in the application layer).

Data Consistency in Memcache

This Memcache is used as a cache layer in front of a database or another persistent storage system, it is a simple key-value store with no built-in data consistency features, it is up to the application to ensure that the cache and the underlying persistent storage remain in sync.

1. Cache-Aside Strategy

  • Load on Demand: Data is loaded into the cache on demand. When an application needs data, it first checks the cache. If the data is not found, it fetches it from the database and then stores it in the cache.
  • Write-Through/Write-Behind Caching: When data is updated, the changes are written directly to the database. In write-through caching, data is written simultaneously to the cache and the database. In write-behind caching, data is written to the cache first and then written to the database after a short delay.
  • Read-Through Caching: Implement read-through caching patterns, where the cache is transparent to the application. If a cache miss occurs, the cache system itself is responsible for fetching the data from the database and populating the cache.

2. Time-to-Live (TTL)

  • Assign a TTL (Time-to-Live) to each cache entry. After the TTL expires, the entry is either removed or marked as stale, forcing the application to fetch the latest version from the database.

3. Invalidation on Update

  • When data is updated or deleted in the database, immediately invalidate or update the corresponding entry in the cache. This ensures that stale data is not served to the clients.

4. Versioning

  • Store a version number or a timestamp with each data item, both in the cache and the database. When updating or retrieving data, check the version/timestamp to ensure consistency.

5. Consistency Checks

  • Implement periodic consistency checks between the cache and the database. This can help identify and correct any discrepancies.

6. Atomic Operations

  • Use Memcached’s atomic operations like CAS (Check and Set) to handle concurrent updates to the cache more reliably.

7. Handling Cache Failures

  • Design your application to handle cache failures gracefully. In case of a cache miss or failure, it should be able to retrieve data directly from the database.

Reference

1. https://linuxtechme.wordpress.com/2012/03/29/470/
2. https://medium.com/@abhiruchigupta16/redis-and-memcached-explained-ce8d722c50b7
3. https://dzone.com/articles/memcached-a-distributed-memory-object-caching-syst