DBMS > EsgynDB vs. Google Cloud Datastore vs. GreptimeDB vs. PostGIS vs. Transwarp ArgoDB

System Properties Comparison EsgynDB vs. Google Cloud Datastore vs. GreptimeDB vs. PostGIS vs. Transwarp ArgoDB

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Spatial extension of PostgreSQL

Distributed Analytical Database to replace Hadoop+MPP hybrid architecture

Primary database model

Secondary database models

Relational DBMS

Search engine

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.12
Rank	#351	Overall
	#34	Time Series DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.12
Rank	#348	Overall
	#151	Relational DBMS

Website

www.esgyn.cn

cloud.google.com/datastore

greptime.com

postgis.net

www.transwarp.cn/en/product/argodb

Technical documentation

cloud.google.com/datastore/docs

docs.greptime.com

postgis.net/documentation

docs.transwarp.cn/#/documents-support/docs?category=ARGODB

Developer

Esgyn

Google

Greptime Inc.

Transwarp

Initial release

2015

2008

2022

2005

Current release

3.4.2, February 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL v2.0

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++, Java

Rust

Server operating systems

Linux

hosted

Android
Docker
FreeBSD
Linux
macOS
Windows

Data scheme

yes

schema-free

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

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

yes

SQL-like query language (GQL)

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

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

gRPC
HTTP API
JDBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

C++
Erlang
Go
Java
JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

using Google App Engine

Python

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

based on PostgreSQL

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Multi-source replication using Paxos

yes

based on PostgreSQL

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

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

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

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.

User concepts

Access control

fine grained access rights according to SQL-standard

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

Simple rights management via user accounts

yes

based on PostgreSQL

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...
» more

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

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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