DBMS > Cachelot.io vs. EsgynDB vs. Google Cloud Datastore vs. Greenplum vs. Lovefield

System Properties Comparison Cachelot.io vs. EsgynDB vs. Google Cloud Datastore vs. Greenplum vs. Lovefield

Editorial information provided by DB-Engines

Name

Description

In-memory caching system

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

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

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	0.04
Rank	#388	Overall
	#62	Key-value stores

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	8.08
Rank	#48	Overall
	#31	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Website

cachelot.io

www.esgyn.cn

cloud.google.com/datastore

greenplum.org

google.github.io/lovefield

Technical documentation

cloud.google.com/datastore/docs

docs.greenplum.org

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

Esgyn

Google

Pivotal Software Inc.

Google

Initial release

2015

2008

2005

2014

Current release

7.0.0, September 2023

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

Open Source

Apache 2.0

Open Source

Apache 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

C++

C++, Java

JavaScript

Server operating systems

FreeBSD
Linux
OS X

Linux

hosted

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Data scheme

schema-free

yes

schema-free

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

since Version 4.2

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (GQL)

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

Memcached protocol

ADO.NET
JDBC
ODBC

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

JDBC
ODBC

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

All languages supporting JDBC/ODBC/ADO.Net

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

C
Java
Perl
Python
R

JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

Multi-source replication using Paxos

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

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)

fine grained access rights according to SQL-standard

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