DBMS > HEAVY.AI vs. Lovefield vs. Postgres-XL vs. RavenDB

System Properties Comparison HEAVY.AI vs. Lovefield vs. Postgres-XL vs. RavenDB

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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

Embeddable relational database for web apps written in pure JavaScript

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Relational DBMS

Document store

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Score	2.68
Rank	#102	Overall
	#19	Document stores

Website

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

google.github.io/lovefield

www.postgres-xl.org

ravendb.net

Technical documentation

docs.heavy.ai

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

www.postgres-xl.org/documentation

ravendb.net/docs

Developer

HEAVY.AI, Inc.

Google

Hibernating Rhinos

Initial release

2016

2014

since 2012, originally named StormDB

2010

Current release

5.10, January 2022

2.1.12, February 2017

10 R1, October 2018

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

Mozilla public license

Open Source

AGPL version 3, 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 CUDA

JavaScript

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

Linux
macOS

Linux
macOS
Raspberry Pi
Windows

Data scheme

yes

schema-free

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

yes

distributed, parallel query execution

SQL-like query language (RQL)

APIs and other access methods

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

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More resources

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