DBMS > Databend vs. Lovefield vs. Snowflake vs. Solr vs. Stardog

System Properties Comparison Databend vs. Lovefield vs. Snowflake vs. Solr vs. Stardog

Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Embeddable relational database for web apps written in pure JavaScript

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

A widely used distributed, scalable search engine based on Apache Lucene

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

Primary database model

Relational DBMS

Search engine

Graph DBMS
RDF store

Secondary database models

Spatial DBMS

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

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

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

Website

github.com/datafuselabs/databend
www.databend.com

google.github.io/lovefield

www.snowflake.com

solr.apache.org

www.stardog.com

Technical documentation

docs.databend.com

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

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

docs.stardog.com

Developer

Databend Labs

Google

Snowflake Computing Inc.

Apache Software Foundation

Stardog-Union

Initial release

2021

2014

2006

2010

Current release

1.0.59, April 2023

2.1.12, February 2017

9.6.1, May 2024

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2

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

Rust

JavaScript

Java

Server operating systems

hosted
Linux
macOS

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

tested with Chrome, Firefox, IE, Safari

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
macOS
Windows

Data scheme

yes

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

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

yes

Solr Parallel SQL Interface

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

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

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

Go
Java
JavaScript (Node.js)
Python
Rust

JavaScript

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

Java plugins

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

Using read-only observers

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

yes

ACID

optimistic locking

ACID

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

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles

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

yes

Access rights for users and roles

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