DBMS > BigchainDB vs. Google BigQuery vs. Google Cloud Spanner vs. HBase vs. TimesTen

System Properties Comparison BigchainDB vs. Google BigQuery vs. Google Cloud Spanner vs. HBase 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 horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

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

In-Memory RDBMS compatible to Oracle

Primary database model

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

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	2.89
Rank	#105	Overall
	#53	Relational DBMS

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Score	1.35
Rank	#165	Overall
	#75	Relational DBMS

Website

www.bigchaindb.com

cloud.google.com/bigquery

cloud.google.com/spanner

hbase.apache.org

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

Technical documentation

bigchaindb.readthedocs.io/en/latest

cloud.google.com/bigquery/docs

cloud.google.com/spanner/docs

hbase.apache.org/book.html

docs.oracle.com/database/timesten-18.1

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Oracle, TimesTen Performance Software, HP

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

Initial release

2016

2010

2017

2008

1998

Current release

2.3.4, January 2021

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Open Source

Apache version 2

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

Server operating systems

Linux

hosted

Linux
Unix
Windows

using Cygwin

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

Data scheme

schema-free

yes

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

Query statements complying to ANSI 2011

yes

APIs and other access methods

CLI Client
RESTful HTTP API

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

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

Go
Java
JavaScript (Node.js)
Python

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Coprocessors in Java

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication with 3 replicas for regional instances.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Strict serializable isolation

Single row ACID (across millions of columns)

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)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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

fine grained access rights according to SQL-standard

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