DBMS > DolphinDB vs. Google BigQuery vs. Graphite vs. GridDB

System Properties Comparison DolphinDB vs. Google BigQuery vs. Graphite vs. GridDB

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Editorial information provided by DB-Engines

Name

Description

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Large scale data warehouse service with append-only tables

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

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

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Relational DBMS

Key-value store
Relational DBMS

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	4.83
Rank	#67	Overall
	#4	Time Series DBMS

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Website

www.dolphindb.com

cloud.google.com/bigquery

github.com/graphite-project/graphite-web

griddb.net

Technical documentation

docs.dolphindb.cn/en/help200/index.html

cloud.google.com/bigquery/docs

graphite.readthedocs.io

docs.griddb.net

Developer

DolphinDB, Inc

Google

Chris Davis

Toshiba Corporation

Initial release

2018

2010

2006

2013

Current release

v2.00.4, January 2022

5.1, August 2022

License

Commercial or Open Source

commercial

free community version available

commercial

Open Source

Apache 2.0

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

C++

Python

C++

Server operating systems

Linux
Windows

hosted

Linux
Unix

Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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

yes

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

RESTful HTTP/JSON API

HTTP API
Sockets

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Supported programming languages

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

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

Immediate Consistency

none

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

Since BigQuery is designed for querying data

ACID at container level

Concurrency

Support for concurrent manipulation of data

yes

locking

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

Administrators, Users, Groups

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

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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