DBMS > BoltDB vs. HEAVY.AI vs. Kinetica vs. Realm vs. SAP HANA

System Properties Comparison BoltDB vs. HEAVY.AI vs. Kinetica vs. Realm vs. SAP HANA

Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Fully vectorized database across both GPUs and CPUs

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

github.com/boltdb/bolt

github.com/heavyai/heavydb
www.heavy.ai

www.kinetica.com

realm.io

www.sap.com/products/hana.html

Technical documentation

Developer

HEAVY.AI, Inc.

Kinetica

Realm, acquired by MongoDB in May 2019

SAP

Initial release

2013

2016

2012

2014

2010

Current release

5.10, January 2022

7.1, August 2021

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++ and CUDA

C, C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

Android
Backend: server-less
iOS
Windows

Appliance or cloud-service

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

user defined functions

runs within the applications so server-side scripts are unnecessary

SQLScript, R

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

Source-replica replication

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

yes

In-Memory realm

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

yes

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