DBMS > Oracle Berkeley DB vs. Tarantool vs. Tibero vs. Transwarp StellarDB

System Properties Comparison Oracle Berkeley DB vs. Tarantool vs. Tibero vs. Transwarp StellarDB

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Editorial information provided by DB-Engines

Name

Description

Widely used in-process key-value store

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

A secure RDBMS, designed for easy portability from Oracle

A distributed graph DBMS built for enterprise-level graph applications

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store
Key-value store
Relational DBMS

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Score	0.07
Rank	#371	Overall
	#39	Graph DBMS

Website

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

www.tarantool.io

us.tmaxsoft.com/products/tibero

www.transwarp.cn/en/product/stellardb

Technical documentation

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

www.tarantool.io/en/doc

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

TmaxSoft

Transwarp

Initial release

1994

2008

2003

Current release

18.1.40, May 2020

2.10.0, May 2022

6, April 2015

License

Commercial or Open Source

Open Source

commercial license available

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

commercial

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

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

C and C++

C and Assembler

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

BSD
Linux
macOS

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

yes

Typing

predefined data types such as float or date

string, double, decimal, uuid, integer, blob, boolean, datetime

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

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

Full-featured ANSI SQL support

yes

SQL-like query language

APIs and other access methods

Open binary protocol

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

OpenCypher

Supported programming languages

.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#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

Lua, C and SQL stored procedures

Persistent Stored Procedure (PSM)

Triggers

yes

only for the SQL API

yes, before/after data modification events, on replication events, client session events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

horizontal partitioning

by range, hash, list or composite

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

In-memory capabilities

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

yes

yes, full featured in-memory storage engine with persistence

planned for next version

User concepts

Access control

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

yes

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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