DBMS > Google BigQuery vs. GridDB vs. Hive vs. Riak KV

System Properties Comparison Google BigQuery vs. GridDB vs. Hive vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Scalable in-memory time series database optimized for IoT and Big Data

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Key-value store
Relational DBMS

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	2.02
Rank	#132	Overall
	#11	Time Series DBMS

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Score	4.44
Rank	#80	Overall
	#9	Key-value stores

Website

cloud.google.com/bigquery

griddb.net

hive.apache.org

Technical documentation

cloud.google.com/bigquery/docs

docs.griddb.net

cwiki.apache.org/confluence/display/Hive/Home

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Google

Toshiba Corporation

Apache Software Foundation

initially developed by Facebook

OpenSource, formerly Basho Technologies

Initial release

2010

2013

2012

2009

Current release

5.1, August 2022

3.1.3, April 2022

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Apache Version 2

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Erlang

Server operating systems

hosted

Linux

All OS with a Java VM

Linux
OS X

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

restricted

SQL

Support of SQL

yes

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC
Thrift

HTTP API
Native Erlang Interface

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C++
Java
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

Since BigQuery is designed for querying data

ACID at container level

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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)

Access rights for users can be defined per database

Access rights for users, groups and roles

yes, using Riak Security

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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