DBMS > HEAVY.AI vs. HugeGraph vs. KairosDB vs. SAP HANA

System Properties Comparison HEAVY.AI vs. HugeGraph vs. KairosDB vs. SAP HANA

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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

A fast-speed and highly-scalable Graph DBMS

Distributed Time Series DBMS based on Cassandra or H2

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

Primary database model

Secondary database models

Spatial 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	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

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

github.com/hugegraph
hugegraph.apache.org

github.com/kairosdb/kairosdb

www.sap.com/products/hana.html

Technical documentation

docs.heavy.ai

hugegraph.apache.org/docs

kairosdb.github.io

help.sap.com/hana

Developer

HEAVY.AI, Inc.

Baidu

SAP

Initial release

2016

2018

2013

2010

Current release

5.10, January 2022

0.9

1.2.2, November 2018

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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

Java

Server operating systems

Linux

Linux
macOS
Unix

Linux
OS X
Windows

Appliance or cloud-service

Data scheme

yes

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

Java API
RESTful HTTP API
TinkerPop Gremlin

Graphite protocol
HTTP REST
Telnet API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

Groovy
Java
Python

Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

based on Cassandra

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

depending on used storage backend, e.g. Cassandra and HBase

selectable replication factor

based on Cassandra

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

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

User concepts

Access control

fine grained access rights according to SQL-standard

Users, roles and permissions

simple password-based access control

yes

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