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DBMS > AgensGraph vs. HEAVY.AI vs. Immudb vs. Kinetica vs. Splice Machine

System Properties Comparison AgensGraph vs. HEAVY.AI vs. Immudb vs. Kinetica vs. Splice Machine

Editorial information provided by DB-Engines
NameAgensGraph  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonImmudb  Xexclude from comparisonKinetica  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionMulti-model database supporting relational and graph data models and built upon PostgreSQLA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareAn open source immutable (append-only) database with cryptographic verification which makes it tamper-resistant and fully auditable.Fully vectorized database across both GPUs and CPUsOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelGraph DBMS
Relational DBMS
Relational DBMSKey-value storeRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMSRelational DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.20
Rank#321  Overall
#27  Graph DBMS
#143  Relational DBMS
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score0.24
Rank#305  Overall
#43  Key-value stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitebitnine.net/­agensgraphgithub.com/­heavyai/­heavydb
www.heavy.ai
github.com/­codenotary/­immudb
immudb.io
www.kinetica.comsplicemachine.com
Technical documentationbitnine.net/­documentationdocs.heavy.aidocs.immudb.iodocs.kinetica.comsplicemachine.com/­how-it-works
DeveloperBitnine Global Inc.HEAVY.AI, Inc.CodenotaryKineticaSplice Machine
Initial release20162016202020122014
Current release2.1, December 20185.10, January 20221.2.3, April 20227.1, August 20213.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache License 2.0Open Source infoApache Version 2; enterprise edition availableOpen Source infoApache Version 2.0commercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCC++ and CUDAGoC, C++Java
Server operating systemsLinux
OS X
Windows
LinuxBSD
Linux
macOS
Solaris
Windows
z/OS
LinuxLinux
OS X
Solaris
Windows
Data schemedepending on used data modelyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nononono
Secondary indexesyesnoyesyesyes
SQL infoSupport of SQLyesyesSQL-like syntaxSQL-like DML and DDL statementsyes
APIs and other access methodsCypher Query Language
JDBC
JDBC
ODBC
Thrift
Vega
gRPC protocol
PostgreSQL wire protocol
JDBC
ODBC
RESTful HTTP API
JDBC
Native Spark Datasource
ODBC
Supported programming languagesC
Java
JavaScript
Python
All languages supporting JDBC/ODBC/Thrift
Python
.Net
Go
Java
JavaScript (Node.js)
Python
C++
Java
JavaScript (Node.js)
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyesnonouser defined functionsyes infoJava
Triggersnononoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes
Partitioning methods infoMethods for storing different data on different nodesno, but can be realized using table inheritanceSharding infoRound robinShardingShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replicationSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyesnonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnoyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardAccess rights for users and roles on table levelAccess rights for users, groups and roles according to SQL-standard

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More resources
AgensGraphHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022ImmudbKineticaSplice Machine
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