Distributed Database Contrast and Compare
|Primary purpose||Scalable database applications that require maximum CPU, memory and storage utilization to serve large data sets with a high degree of resource efficiency||Database applications that require five 9s availability and instant switch-over. Supports the distribution of read-only workloads (read load balancing)||Applications that require distributed, cooperative computing and a resilient topology with five 9s availability. Supports distribution of all workloads (read- and write load balancing) on modest sized networks||Data aggregation from a large number of data collection points. Smart data containers to support sporadic connectivity. Advanced server-side analytics for aggregated data|
|Replication||When combined with HA||Master-slave replication. Synchronous, Asynchronous||Multi-master replication. Synchronous||On-demand, based on connection state, data modification events, timers, and more|
|Scalability||Elastic, near liner scalability with added shards||Near linear read scalability. Read requests can be distributed across multiple nodes||Near linear read scalability. Overall scalability is a function of the workload (% read-only versus read-write transactions).||Server-side performance can be increased with added cores & sharding|
|Reliability and Fault-tolerance||When combined with HA||Fault tolerant||Fault tolerant||
Containers are durable even with sparse connectivity.
Server-side can be made reliable through the normal means — clustering and HA
|Concept and Distribution Topology||A logical database is horizontally partitioned — physically split into multiple (smaller) parts called shards; shards may reside on separate servers to spread the load or on the same server to better utilize multiple CPU cores. eXtremeDB’s SQL engine handles query distribution and presents the distributed database as a single logical database||A single master database receives all modifications (insert/update/delete operations) and replicates transactions to replicas. In the event of a failure, one replica is elected as new master||Multi-master architecture in which each node can apply modifications (insert/update/delete). Each transaction is synchronously propagated to all nodes, keeping copies of the database identical (consistent). Database reads are always local (and fast). Writes are longer, but don’t block the database —high concurrency is achieved through Optimistic Concurrency Control.||
Push data from IoT Edge to aggregation points (Gateways and/or Servers) for analytics.
Push data down to the edge, usually for new device configuration/provisioning.
Controlled through push/pull interfaces and/or automatic data exchange between collection points and servers.