DBMS > atoti vs. HEAVY.AI vs. Lovefield vs. Realm

System Properties Comparison atoti vs. HEAVY.AI vs. Lovefield vs. Realm

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

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

Primary database model

Object oriented DBMS

Relational DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Website

atoti.io

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

google.github.io/lovefield

realm.io

Technical documentation

docs.atoti.io

docs.heavy.ai

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

realm.io/docs

Developer

ActiveViam

HEAVY.AI, Inc.

Google

Realm, acquired by MongoDB in May 2019

Initial release

2016

2014

Current release

5.10, January 2022

2.1.12, February 2017

License

Commercial or Open Source

commercial

free versions available

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

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

Java

C++ and CUDA

JavaScript

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

Android
Backend: server-less
iOS
Windows

Data scheme

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

Multidimensional Expressions (MDX)

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

JDBC
ODBC
Thrift
Vega

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

Python

runs within the applications so server-side scripts are unnecessary

Triggers

Using read-only observers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

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

In-Memory realm

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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More resources

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