DBMS > Atos Standard Common Repository vs. DolphinDB vs. Google Cloud Datastore vs. GreptimeDB

System Properties Comparison Atos Standard Common Repository vs. DolphinDB vs. Google Cloud Datastore vs. GreptimeDB

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

An open source Time Series DBMS built for increased scalability, high performance and efficiency

Primary database model

Document store
Key-value store

Time Series DBMS

Document store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.06
Rank	#352	Overall
	#33	Time Series DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.dolphindb.com

cloud.google.com/datastore

greptime.com

Technical documentation

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

cloud.google.com/datastore/docs

docs.greptime.com

Developer

Atos Convergence Creators

DolphinDB, Inc

Google

Greptime Inc.

Initial release

2016

2018

2008

2022

Current release

1703

v2.00.4, January 2022

License

Commercial or Open Source

commercial

free community version available

commercial

Open Source

Apache Version 2.0

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++

Rust

Server operating systems

Linux

Linux
Windows

hosted

Android
Docker
FreeBSD
Linux
macOS
Windows

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

schema-free, schema definition possible

Typing

predefined data types such as float or date

optional

yes

yes, details here

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (GQL)

yes

APIs and other access methods

LDAP

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

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

gRPC
HTTP API
JDBC

Supported programming languages

All languages with LDAP bindings

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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++
Erlang
Go
Java
JavaScript

Server-side scripts

Stored procedures

yes

using Google App Engine

Python

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

Atomic execution of specific operations

yes

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

LDAP bind authentication

Administrators, Users, Groups

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Simple rights management via user accounts

More information provided by the system vendor

Specific characteristics

GreptimeDB is a SQL & Python-enabled timeseries database system built from scratch...
» more

Competitive advantages

- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
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Typical application scenarios

For IoT industries, GreptimeDB can seamless integrate with message queues and other...
» more

Key customers

Greptime's clients span multiple sectors including IoT, connected vehicles, and energy...
» more

Market metrics

GreptimeDB has garnered global recognition by topping GitHub trends following its...
» more

Licensing and pricing models

GreptimeDB: open source, distributed, cloud-native TSDB; supports Hybrid Time-series...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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