DBMS > BigchainDB vs. Google BigQuery vs. HugeGraph vs. Ignite vs. TimesTen

System Properties Comparison BigchainDB vs. Google BigQuery vs. HugeGraph vs. Ignite vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

A fast-speed and highly-scalable Graph DBMS

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

In-Memory RDBMS compatible to Oracle

Primary database model

Document store

Relational DBMS

Graph DBMS

Key-value store
Relational DBMS

Relational DBMS

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

www.bigchaindb.com

cloud.google.com/bigquery

github.com/hugegraph
hugegraph.apache.org

ignite.apache.org

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

Technical documentation

bigchaindb.readthedocs.io/en/latest

cloud.google.com/bigquery/docs

hugegraph.apache.org/docs

apacheignite.readme.io/docs

docs.oracle.com/database/timesten-18.1

Developer

Google

Baidu

Apache Software Foundation

Oracle, TimesTen Performance Software, HP

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

Initial release

2016

2010

2018

2015

1998

Current release

0.9

Apache Ignite 2.6

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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++, Java, .Net

Server operating systems

Linux

hosted

Linux
macOS
Unix

Linux
OS X
Solaris
Windows

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

Data scheme

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

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

CLI Client
RESTful HTTP API

RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Groovy
Java
Python

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

in JavaScript

asynchronous Gremlin script jobs

yes (compute grid and cache interceptors can be used instead)

PL/SQL

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

depending on used storage backend, e.g. Cassandra and HBase

yes (replicated cache)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

Since BigQuery is designed for querying data

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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