DBMS > EsgynDB vs. EXASOL vs. Google Cloud Datastore vs. ReductStore vs. Riak KV

System Properties Comparison EsgynDB vs. EXASOL vs. Google Cloud Datastore vs. ReductStore vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

www.esgyn.cn

www.exasol.com

cloud.google.com/datastore

github.com/reductstore
www.reduct.store

Technical documentation

www.exasol.com/resources

cloud.google.com/datastore/docs

www.reduct.store/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Esgyn

Exasol

Google

ReductStore LLC

OpenSource, formerly Basho Technologies

Initial release

2015

2000

2008

2023

2009

Current release

1.9, March 2024

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Business Source License 1.1

Open Source

Apache version 2, commercial enterprise edition

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

C++, Rust

Erlang

Server operating systems

Linux

hosted

Docker
Linux
macOS
Windows

Linux
OS X

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

restricted

SQL

Support of SQL

yes

SQL-like query language (GQL)

APIs and other access methods

ADO.NET
JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

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

HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Java
Lua
Python
R

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

C++
JavaScript (Node.js)
Python
Rust

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

using Google App Engine

Erlang

Triggers

yes

Callbacks using the Google Apps Engine

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Multi-source replication using Paxos

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

using Google Cloud Dataflow

yes

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.

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

links between data sets can be stored

Transaction concepts

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

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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

yes, using Riak Security

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