DBMS > FeatureBase vs. Infobright vs. Splice Machine
System Properties Comparison FeatureBase vs. Infobright vs. Splice Machine
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|Editorial information provided by DB-Engines|
|Name||FeatureBase Xexclude from comparison||Infobright Xexclude from comparison||Splice Machine Xexclude from comparison|
|Description||Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.||High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend||Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark|
|Primary database model||Relational DBMS||Relational DBMS||Relational DBMS|
|Developer||Molecula and Pilosa Open Source Contributors||Ignite Technologies Inc.; formerly InfoBright Inc.||Splice Machine|
|Current release||2022, May 2022||3.1, March 2021|
|License Commercial or Open Source||commercial||commercial The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016||Open Source AGPL 3.0, commercial license available|
|Cloud-based only Only available as a cloud service||no||no||no|
|DBaaS offerings (sponsored links) Database as a Service|
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|Server operating systems||Linux|
|Typing predefined data types such as float or date||yes||yes||yes|
|Secondary indexes||no||no Knowledge Grid Technology used instead||yes|
|SQL Support of SQL||SQL queries||yes||yes|
|APIs and other access methods||gRPC|
Native Spark Datasource
|Supported programming languages||Java|
|Server-side scripts Stored procedures||no||yes Java|
|Partitioning methods Methods for storing different data on different nodes||Sharding||none||Shared Nothhing Auto-Sharding, Columnar Partitioning|
|Replication methods Methods for redundantly storing data on multiple nodes||yes||Source-replica replication||Multi-source replication|
|MapReduce Offers an API for user-defined Map/Reduce methods||no||Yes, via Full Spark Integration|
|Consistency concepts Methods to ensure consistency in a distributed system||Immediate Consistency||Immediate Consistency|
|Foreign keys Referential integrity||yes||no||yes|
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data||yes||ACID||ACID|
|Concurrency Support for concurrent manipulation of data||yes||yes||yes, multi-version concurrency control (MVCC)|
|Durability Support for making data persistent||yes, using Linux fsync||yes||yes|
|User concepts Access control||fine grained access rights according to SQL-standard exploiting MySQL or PostgreSQL frontend capabilities||Access rights for users, groups and roles according to SQL-standard|
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