DBMS > BigchainDB vs. Drizzle vs. Splice Machine vs. TerarkDB

System Properties Comparison BigchainDB vs. Drizzle vs. Splice Machine vs. TerarkDB

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Document store

Relational DBMS

Key-value store

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Website

www.bigchaindb.com

splicemachine.com

github.com/bytedance/terarkdb

Technical documentation

bigchaindb.readthedocs.io/en/latest

splicemachine.com/how-it-works

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Drizzle project, originally started by Brian Aker

Splice Machine

ByteDance, originally Terark

Initial release

2016

2008

2014

2016

Current release

7.2.4, September 2012

3.1, March 2021

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

GNU GPL

Open Source

AGPL 3.0, commercial license available

commercial

restricted open source version available

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++

Server operating systems

Linux

FreeBSD
Linux
OS X

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

CLI Client
RESTful HTTP API

JDBC

JDBC
Native Spark Datasource
ODBC

C++ API
Java API

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

C
C++
Java
PHP

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C++
Java

Server-side scripts

Stored procedures

yes

Java

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users, groups and roles according to SQL-standard

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More resources

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Recent citations in the news

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