DBMS > BigchainDB vs. Citus vs. GeoSpock vs. Google BigQuery

System Properties Comparison BigchainDB vs. Citus vs. GeoSpock vs. Google BigQuery

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

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

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Spatial and temporal data processing engine for extreme data scale

Large scale data warehouse service with append-only tables

Primary database model

Document store

Relational DBMS

Secondary database models

Document store

Time Series DBMS

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Website

www.bigchaindb.com

www.citusdata.com

geospock.com

cloud.google.com/bigquery

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.citusdata.com

cloud.google.com/bigquery/docs

Developer

GeoSpock

Google

Initial release

2016

2010

Current release

8.1, December 2018

2.0, September 2019

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

AGPL, commercial license also available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Python

Java, Javascript

Server operating systems

Linux

hosted

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

specific XML type available, but no XML query functionality

Secondary indexes

yes

temporal, categorical

SQL

Support of SQL

yes

standard, with numerous extensions

ANSI SQL for query only (using Presto)

yes

APIs and other access methods

CLI Client
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC

RESTful HTTP/JSON API

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

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.

User concepts

Access control

yes

fine grained access rights according to SQL-standard

Access rights for users can be defined per table

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

Google Cloud Identity & Access Management (IAM)

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