Compare MemSQL to
Built for maximum ingest, fast query, and high concurrency on a spectrum of workloads and natively supports multi-model to help you process, analyze, and act on data instantly. Available in the cloud, on Kubernetes, or on-premises.
Primarily built for transactional workloads only, and poor in handling analytical workloads. Extensible for multi-model through plug-ins.
Ingesting streaming data in parallel with exactly-once semantics for direct, immediate processing or raw data & events from sources such as Kafka, Spark, S3 and data formats such as Parquet, JSON, and CSV. Common batch load methods are also supported.
Common batch load methods are supported
Flexibility and Manageability
Effortlessly supports both on-premise and multi-cloud/hybrid cloud architectures. Leverage MemSQL as a fully managed service across cloud providers or self deploy across clouds and on-premise environments. MemSQL runs well in any environment, including bare metal, VMs, or containers through our certified Kubernetes operator.
Installation, configuration, upgrades, and cluster management tasks require humungous manual efforts from highly skilled resources.
Scale-out, shared nothing distributed architecture with memory-first tiered storage design. Responds efficiently and quickly to growing workloads leveraging commodity hardware without add-ons or specialized tuning expertise. Proven with industry standard benchmarks: TPC-C, TPC-H, & TPC-DS.
Traditional 20th century single-node, disk-based architecture which has buffer cache and I/O issues such as write amplification. Does not support full query compilation or vectorized query execution, so vastly slower per core for both OLTP and analytical workloads, but especially analytical workloads.
Delivers the fastest and most scalable reporting and analytics across all of your operational data; including streaming, real-time, and historical data. Lock-free skiplists speeding queries on rowstore, query compilation for blazing response time, and vectorized query execution for highly-efficent use of resources resulting in greater scale and performance for combined transactional-analytical workloads.
Not capable of running analytics on rapidly changing/high velocity real-time data. Does not support full query compilation or vectorized query execution, so vastly slower per core for both OLTP and analytical workloads, but especially analytical workloads.
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