In Memory Computing

• In-memory computing is running computer calculations entirely in computer memory (e.g., in RAM)

• Used for large-scale, complex calculations which require specialized systems software

• Biggest advantage of in-memory processing is speed.

• By storing data in RAM and processing it in parallel, it supplies real-time insights that enable businesses to deliver immediate actions and responses.

• In-memory databases are faster than traditional databases because they require fewer CPU instructions. Eliminates the time it takes to access data from a disk.

• In-memory databases are more volatile than traditional databases because data is lost when there is a loss of power or the computer's RAM crashes.

• Not easy to implement.

• Only usable if low change in data stored in cache / may need more frequent updates of the cache.

• Unifying transactional and analytical processing for real-time insights.

• Availability of SSD and persistent memory technologies are also driving costs down.

• Security is another consideration, as in-memory tools expose huge amounts of data to end user.

• Random access of data across nodes not fully feasible.

Redis

• As your usage scales up, price and/or system resources can start to become a little high, especially if it is configured to be all in memory mode.
• Monitoring and administration tools lacking.
• Redis can work as a messaging queue but is not as reliable.
• If the instance goes down, there is no backup preserved.

Memachad

• Needs external disk dumping tools.
• Extremely high speed.
• Limitation of 1MB Object size.
• If the instance goes down, there is no backup preserved.

Aerospike

• Scales really quickly and easily.
• Can deploy a new cluster or add to a cluster fairly quick.
• The sharding removes any need to overlook to make sure if balanced correctly.
• HA is simple, that almost little to no need figure out how to do it.
• Cross datacenter replication usage isn't so straightforward.
• Sometimes cross dc replication can have issues of bad data.
• Excellent recommendation engine.

Mongo DB

• Not an in-memory database but can be configured to run that way. Makes use of cache, meaning data records kept memory for fast retrieval, as opposed to on disk.
• High performance, speed, supports sharding, scalable , has adhoc query support.
• Weak in transaction handling, joins, duplicate management , uses high memory , limited nesting capabilities.

Amazon / Google / Azure

• Fully manages administrative tasks for Redis instances such as hardware provisioning, setup and configuration management, software patching, failover, monitoring and other nuances that require considerable effort for service owners who just want to use Redis as a memory store or a cache.