DBMS > Geode vs. Google BigQuery vs. Google Cloud Bigtable vs. GridDB

System Properties Comparison Geode vs. Google BigQuery vs. Google Cloud Bigtable vs. GridDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Large scale data warehouse service with append-only tables

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

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

Primary database model

Key-value store

Relational DBMS

Key-value store
Wide column store

Time Series DBMS

Secondary database models

Key-value store
Relational DBMS

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

Score	1.86
Rank	#134	Overall
	#24	Key-value stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Website

geode.apache.org

cloud.google.com/bigquery

cloud.google.com/bigtable

griddb.net

Technical documentation

geode.apache.org/docs

cloud.google.com/bigquery/docs

cloud.google.com/bigtable/docs

docs.griddb.net

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Google

Toshiba Corporation

Initial release

2002

2010

2015

2013

Current release

1.1, February 2017

5.1, August 2022

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

Open Source

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

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

Java

C++

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

SQL-like query language (OQL)

yes

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Internal replication in Colossus, and regional replication between two clusters in different zones

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate consistency within container, eventual consistency across containers

Foreign keys

Referential integrity

Transaction concepts

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

yes, on a single node

Since BigQuery is designed for querying data

Atomic single-row operations

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 rights per client and object definable

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 rights for users can be defined per database

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.