DBMS > Google BigQuery vs. gStore vs. HarperDB vs. IBM Db2 vs. Linter

System Properties Comparison Google BigQuery vs. gStore vs. HarperDB vs. IBM Db2 vs. Linter

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A native Graph DBMS to store and maintain very large RDF datasets.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Common in IBM host environments, 2 different versions for host and Windows/Linux

RDBMS for high security requirements

Primary database model

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.14
Rank	#342	Overall
	#34	Graph DBMS
	#16	RDF stores

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	125.90
Rank	#9	Overall
	#6	Relational DBMS

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Website

cloud.google.com/bigquery

en.gstore.cn

www.harperdb.io

www.ibm.com/products/db2

linter.ru

Technical documentation

cloud.google.com/bigquery/docs

en.gstore.cn/#/enDocs

docs.harperdb.io/docs

www.ibm.com/docs/en/db2

Developer

Google

HarperDB

IBM

relex.ru

Initial release

2010

2016

2017

1983

host version

1990

Current release

1.2, November 2023

3.1, August 2021

12.1, October 2016

License

Commercial or Open Source

commercial

Open Source

BSD

commercial

free community edition available

commercial

free version is 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

C++

Node.js

C and C++

Server operating systems

hosted

Linux

Linux
OS X

AIX
HP-UX
Linux
Solaris
Windows
z/OS

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Data scheme

yes

schema-free and OWL/RDFS-schema support

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data 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

SQL

Support of SQL

yes

SQL-like data manipulation statements

yes

APIs and other access methods

RESTful HTTP/JSON API

HTTP API
SPARQL 1.1

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
PHP
Python

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Custom Functions

since release 3.1

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

A table resides as a whole on one (or more) nodes in a cluster

Sharding

only with Windows/Unix/Linux Version

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

yes

with separate tools (MQ, InfoSphere)

Source-replica replication

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

Since BigQuery is designed for querying data

yes

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

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

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions, Role-Based Access Control (RBAC) supported

Access rights for users and roles

fine grained access rights according to SQL-standard

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