DBMS > Lovefield vs. OpenMLDB vs. Snowflake vs. Sphinx vs. Stardog

System Properties Comparison Lovefield vs. OpenMLDB vs. Snowflake vs. Sphinx vs. Stardog

Editorial information provided by DB-Engines

Name

Description

Embeddable relational database for web apps written in pure JavaScript

An open-source machine learning database that provides a feature platform for training and inference

Cloud-based data warehousing service for structured and semi-structured data

Open source search engine for searching in data from different sources, e.g. relational databases

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Secondary database models

Relational DBMS

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

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

google.github.io/lovefield

Technical documentation

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

openmldb.ai/docs/zh/main

docs.snowflake.net/manuals/index.html

sphinxsearch.com/docs

docs.stardog.com

Developer

Google

4 Paradigm Inc.

Snowflake Computing Inc.

Sphinx Technologies Inc.

Stardog-Union

Initial release

2014

2020

2014

2001

2010

Current release

2.1.12, February 2017

2024-2 February 2024

3.5.1, February 2023

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial

Open Source

GPL version 2, commercial licence available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

JavaScript

C++, Java, Scala

C++

Java

Server operating systems

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

tested with Chrome, Firefox, IE, Safari

Linux

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
macOS
Windows

Data scheme

yes

Fixed schema

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

schema-free and OWL/RDFS-schema support

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

Import/export of XML data possible

Secondary indexes

yes

full-text index on all search fields

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

SQL-like query language (SphinxQL)

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC
SQLAlchemy

CLI Client
JDBC
ODBC

Proprietary protocol

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

JavaScript

C++
Go
Java
Python
Scala

JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

Using read-only observers

similar concept for controling cloud resources

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

yes

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

none

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

The original contents of fields are not stored in the Sphinx index.

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights for users and roles

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