memcached vs BigMemory

Struggling to choose between memcached and BigMemory? Both products offer unique advantages, making it a tough decision.

memcached is a Network & Admin solution with tags like caching, memory, performance.

It boasts features such as In-memory key-value store, Distributed architecture, Simple protocol, Horizontal scalability and pros including Very fast data lookup, Reduces database load, Improves overall application performance.

On the other hand, BigMemory is a Development product tagged with caching, data-management, low-latency.

Its standout features include Distributed in-memory data storage, Automatic data eviction and loading, Read/write caching for databases, Support for terabytes of data, Integration with Hadoop and Spark, High availability through replication and failover, and it shines with pros like Very fast data access and throughput, Reduces load on databases, Scales horizontally, Lowers infrastructure costs by using RAM instead of disks, Supports both Java and .NET platforms.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

memcached

memcached

Memcached is an open source, high-performance distributed memory object caching system. It is used to speed up dynamic web applications by alleviating database load for reading/writing frequently accessed data.

Categories:
Network & Admin Caching
caching memory performance

Memcached Features

  1. In-memory key-value store
  2. Distributed architecture
  3. Simple protocol
  4. Horizontal scalability

Pricing

  • Open Source

Pros

Very fast data lookup

Reduces database load

Improves overall application performance

Cons

Data loss on server restart

Additional system complexity

Requires application code changes

BigMemory

BigMemory

BigMemory is an in-memory data management system that provides a fast, scalable cache and data store for applications. It allows storing terabytes of data in memory for low-latency data access.

Categories:
Development In-Memory Databases
caching data-management low-latency

BigMemory Features

  1. Distributed in-memory data storage
  2. Automatic data eviction and loading
  3. Read/write caching for databases
  4. Support for terabytes of data
  5. Integration with Hadoop and Spark
  6. High availability through replication and failover

Pricing

  • Subscription-Based

Pros

Very fast data access and throughput

Reduces load on databases

Scales horizontally

Lowers infrastructure costs by using RAM instead of disks

Supports both Java and .NET platforms

Cons

Can lose data if not persisted

RAM is more expensive than disk

Not fully ACID compliant

Can be complex to configure and tune