DBMS > Galaxybase vs. HEAVY.AI vs. Lovefield vs. Splice Machine

System Properties Comparison Galaxybase vs. HEAVY.AI vs. Lovefield vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Scalable, ACID-compliant native distributed parallel graph platform

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

Embeddable relational database for web apps written in pure JavaScript

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Graph DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.00
Rank	#383	Overall
	#39	Graph DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

galaxybase.com

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

google.github.io/lovefield

splicemachine.com

Technical documentation

docs.heavy.ai

github.com/google/lovefield/blob/master/docs/spec_index.md

splicemachine.com/how-it-works

Developer

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

HEAVY.AI, Inc.

Google

Splice Machine

Initial release

2017

2016

2014

Current release

Nov 20, November 2021

5.10, January 2022

2.1.12, February 2017

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C and Java

C++ and CUDA

JavaScript

Java

Server operating systems

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Solaris
Windows

Data scheme

Strong typed schema

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 query language

via JavaScript builder pattern

yes

APIs and other access methods

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

JDBC
ODBC
Thrift
Vega

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Go
Java
Python

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined procedures and functions

yes

Java

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

yes

User concepts

Access control

Role-based access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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