DBMS > Lovefield vs. OpenQM vs. SQream DB vs. Vertica

System Properties Comparison Lovefield vs. OpenQM vs. SQream DB vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Embeddable relational database for web apps written in pure JavaScript

QpenQM is a high-performance, self-tuning, multi-value DBMS

a GPU-based, columnar RDBMS for big data analytics workloads

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Multivalue DBMS

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	0.28
Rank	#291	Overall
	#10	Multivalue DBMS

Score	0.62
Rank	#234	Overall
	#109	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

google.github.io/lovefield

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

sqream.com

www.vertica.com

Technical documentation

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

docs.sqream.com

vertica.com/documentation

Developer

Google

Rocket Software, originally Martin Phillips

SQream Technologies

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2014

1993

2017

2005

Current release

2.1.12, February 2017

3.4-12

2022.1.6, December 2022

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPLv2, extended commercial license available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

JavaScript

C++, CUDA, Haskell, Java, Scala

C++

Server operating systems

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

tested with Chrome, Firefox, IE, Safari

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux

Data scheme

yes

with some exceptions

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

Secondary indexes

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

.Net
JDBC
ODBC

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

JavaScript

.Net
Basic
C
Java
Objective C
PHP
Python

C++
Java
JavaScript (Node.js)
Python

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

user defined functions in Python

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

Using read-only observers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

yes

horizontal and vertical partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

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

Durability

Support for making data persistent

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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More resources

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