DBMS > Kinetica vs. TimescaleDB vs. VelocityDB
System Properties Comparison Kinetica vs. TimescaleDB vs. VelocityDB
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|Editorial information provided by DB-Engines|
|Name||Kinetica Xexclude from comparison||TimescaleDB Xexclude from comparison||VelocityDB Xexclude from comparison|
|Description||Fully vectorized database across both GPUs and CPUs||A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL||A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)|
|Primary database model||Relational DBMS||Time Series DBMS||Graph DBMS|
Object oriented DBMS
|Secondary database models||Spatial DBMS|
Time Series DBMS
|Current release||7.1, August 2021||2.6.0, February 2022||7.x|
|License Commercial or Open Source||commercial||Open Source Apache 2.0||commercial|
|Cloud-based only Only available as a cloud service||no||no||no|
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|Implementation language||C, C++||C||C#|
|Server operating systems||Linux||Linux|
|Any that supports .NET|
|Typing predefined data types such as float or date||yes||numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types||yes|
|XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.||no||yes||no|
|SQL Support of SQL||SQL-like DML and DDL statements||yes full PostgreSQL SQL syntax||no|
|APIs and other access methods||JDBC|
RESTful HTTP API
native C library
streaming API for large objects
|Supported programming languages||C++|
|Server-side scripts Stored procedures||user defined functions||user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell||no|
|Triggers||yes triggers when inserted values for one or more columns fall within a specified range||yes||Callbacks are triggered when data changes|
|Partitioning methods Methods for storing different data on different nodes||Sharding||yes, across time and space (hash partitioning) attributes||Sharding|
|Replication methods Methods for redundantly storing data on multiple nodes||Source-replica replication||Source-replica replication with hot standby and reads on replicas|
|MapReduce Offers an API for user-defined Map/Reduce methods||no||no||no|
|Consistency concepts Methods to ensure consistency in a distributed system||Immediate Consistency or Eventual Consistency depending on configuration||Immediate Consistency||Immediate Consistency|
|Foreign keys Referential integrity||yes||yes||no|
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data||no||ACID||ACID|
|Concurrency Support for concurrent manipulation of data||yes||yes||yes|
|Durability Support for making data persistent||yes||yes||yes|
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.||yes GPU vRAM or System RAM||no||yes|
|User concepts Access control||Access rights for users and roles on table level||fine grained access rights according to SQL-standard||Based on Windows Authentication|
|More information provided by the system vendor|
|Specific characteristics||Native and fully vectorized database across both GPUs and CPUs, with memory first...|
|Competitive advantages||Best in class geospatial and temporal analytics. Lockless architecture provides real-time...|
|Typical application scenarios||Any application requiring (1) real time insights, (2) time series analysis (3) geospatial...|
|Key customers||Citibank, US Air Force, Softbank, OVO, Telkomsel, USPS, 2 of the top 3 US Telcos,...|
|Market metrics||The largest IoT deployment in the world, NORAD, runs on Kinetica.|
|Licensing and pricing models||Always Free Tier in the Cloud, Pay-As-You-Go Consumption Based Pricing, and Portable...|
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