DBMS > Amazon Redshift vs. Elasticsearch vs. HugeGraph vs. Newts vs. Quasardb

System Properties Comparison Amazon Redshift vs. Elasticsearch vs. HugeGraph vs. Newts vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

A fast-speed and highly-scalable Graph DBMS

Time Series DBMS based on Cassandra

Distributed, high-performance timeseries database

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

aws.amazon.com/redshift

www.elastic.co/elasticsearch

github.com/hugegraph
hugegraph.apache.org

opennms.github.io/newts

quasar.ai

Technical documentation

docs.aws.amazon.com/redshift

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

hugegraph.apache.org/docs

github.com/OpenNMS/newts/wiki

doc.quasar.ai/master

Developer

Amazon (based on PostgreSQL)

Elastic

Baidu

OpenNMS Group

quasardb

Initial release

2012

2010

2018

2014

2009

Current release

8.6, January 2023

0.9

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Elastic License

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

hosted

All OS with a Java VM

Linux
macOS
Unix

Linux
OS X
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

restricted

yes

All search fields are automatically indexed

yes

also supports composite index and range index

yes

with tags

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP REST
Java API

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

Groovy
Java
Python

Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

in Python

yes

asynchronous Gremlin script jobs

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

based on Cassandra

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

selectable replication factor

based on Cassandra

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

via hugegraph-spark

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

edges in graph

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

Memcached and Redis integration

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Users, roles and permissions

Cryptographically strong user authentication and audit trail

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