DBMS > BigchainDB vs. HBase vs. HEAVY.AI vs. Microsoft Azure Cosmos DB vs. TimesTen

System Properties Comparison BigchainDB vs. HBase vs. HEAVY.AI vs. Microsoft Azure Cosmos DB vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Globally distributed, horizontally scalable, multi-model database service

In-Memory RDBMS compatible to Oracle

Primary database model

Document store

Wide column store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.35
Rank	#165	Overall
	#75	Relational DBMS

Website

www.bigchaindb.com

hbase.apache.org

github.com/heavyai/heavydb
www.heavy.ai

azure.microsoft.com/services/cosmos-db

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

Technical documentation

bigchaindb.readthedocs.io/en/latest

hbase.apache.org/book.html

docs.heavy.ai

learn.microsoft.com/azure/cosmos-db

docs.oracle.com/database/timesten-18.1

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

HEAVY.AI, Inc.

Microsoft

Oracle, TimesTen Performance Software, HP

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

Initial release

2016

2008

2016

2014

1998

Current release

2.3.4, January 2021

5.10, January 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

Apache version 2

Open Source

Apache Version 2; enterprise edition available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Python

Java

C++ and CUDA

Server operating systems

Linux

Linux
Unix
Windows

using Cygwin

Linux

hosted

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

Data scheme

schema-free

schema-free, schema definition possible

yes

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

CLI Client
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
Thrift
Vega

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

Coprocessors in Java

JavaScript

PL/SQL

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

Multi-source replication

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

by means of logfiles and checkpoints

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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