DBMS > BigchainDB vs. KeyDB vs. Solr vs. TimesTen vs. Transbase

System Properties Comparison BigchainDB vs. KeyDB vs. Solr vs. TimesTen vs. Transbase

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

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

In-Memory RDBMS compatible to Oracle

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

www.bigchaindb.com

github.com/Snapchat/KeyDB
keydb.dev

solr.apache.org

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

www.transaction.de/en/products/transbase.html

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.keydb.dev

solr.apache.org/resources.html

docs.oracle.com/database/timesten-18.1

www.transaction.de/en/products/transbase/features.html

Developer

EQ Alpha Technology Ltd.

Apache Software Foundation

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Transaction Software GmbH

Initial release

2016

2019

2006

1998

1987

Current release

9.5.0, February 2024

11 Release 2 (11.2.2.8.0)

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

BSD-3

Open Source

Apache Version 2

commercial

free development license

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

Python

C++

Java

C and C++

Server operating systems

Linux

All OS with a Java VM

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

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

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

yes

Typing

predefined data types such as float or date

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

Secondary indexes

yes

by using the Redis Search module

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

Solr Parallel SQL Interface

yes

APIs and other access methods

CLI Client
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

Java API
RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

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

C
C++
Java
PL/SQL

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

Lua

Java plugins

PL/SQL

yes

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

yes

Multi-source replication
Source-replica replication

Source-replica replication

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

Eventual Consistency
Strong eventual consistency with CRDTs

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Optimistic locking, atomic execution of commands blocks and scripts

optimistic locking

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

by means of logfiles and checkpoints

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

yes

simple password-based access control and ACL

yes

fine grained access rights according to SQL-standard

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