DBMS > GreptimeDB vs. MySQL vs. Oracle Berkeley DB vs. OushuDB

System Properties Comparison GreptimeDB vs. MySQL vs. Oracle Berkeley DB vs. OushuDB

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Editorial information provided by DB-Engines

Name

Description

An open source Time Series DBMS built for increased scalability, high performance and efficiency

Widely used open source RDBMS

Widely used in-process key-value store

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Primary database model

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.06
Rank	#352	Overall
	#33	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Website

greptime.com

www.mysql.com

www.oracle.com/database/technologies/related/berkeleydb.html

www.oushu.com/product/oushuDB

Technical documentation

docs.greptime.com

dev.mysql.com/doc

docs.oracle.com/cd/E17076_05/html/index.html

www.oushu.com/documentation

Developer

Greptime Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Oushu

Initial release

2022

1995

1994

Current release

8.4.0, April 2024

18.1.40, May 2020

4.0.1, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

commercial license available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

Rust

C and C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Android
Docker
FreeBSD
Linux
macOS
Windows

FreeBSD
Linux
OS X
Solaris
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Data scheme

schema-free, schema definition possible

yes

schema-free

yes

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL interfaced based on SQLite is available

Full-featured ANSI SQL support

APIs and other access methods

gRPC
HTTP API
JDBC

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

C++
Erlang
Go
Java
JavaScript

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C++

Server-side scripts

Stored procedures

Python

yes

proprietary syntax

yes

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Simple rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

Kerberos, SSL and role based access

More information provided by the system vendor

Specific characteristics

GreptimeDB is a SQL & Python-enabled timeseries database system built from scratch...
» more

Competitive advantages

- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
» more

Typical application scenarios

For IoT industries, GreptimeDB can seamless integrate with message queues and other...
» more

Key customers

Greptime's clients span multiple sectors including IoT, connected vehicles, and energy...
» more

Market metrics

GreptimeDB has garnered global recognition by topping GitHub trends following its...
» more

Licensing and pricing models

GreptimeDB: open source, distributed, cloud-native TSDB; supports Hybrid Time-series...
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.