DBMS > Databend vs. GridDB vs. RocksDB vs. Stardog

System Properties Comparison Databend vs. GridDB vs. RocksDB vs. Stardog

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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

Scalable in-memory time series database optimized for IoT and Big Data

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Secondary database models

Key-value store
Relational DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

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

griddb.net

rocksdb.org

www.stardog.com

Technical documentation

docs.databend.com

docs.griddb.net

github.com/facebook/rocksdb/wiki

docs.stardog.com

Developer

Databend Labs

Toshiba Corporation

Facebook, Inc.

Stardog-Union

Initial release

2021

2013

2010

Current release

1.0.59, April 2023

5.1, August 2022

8.11.4, April 2024

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

BSD

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

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Rust

C++

Java

Server operating systems

hosted
Linux
macOS

Linux

Linux
macOS
Windows

Data scheme

yes

schema-free

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

SQL92, SQL-like TQL (Toshiba Query Language)

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

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

C++ API
Java 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

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

C
C++
Go
Java
Perl
Python
Ruby

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

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 at container level

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles

Access rights for users can be defined per database

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
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