DBMS > Amazon Redshift vs. Dgraph vs. Elasticsearch vs. EsgynDB vs. HarperDB

System Properties Comparison Amazon Redshift vs. Dgraph vs. Elasticsearch vs. EsgynDB vs. HarperDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Distributed and scalable native Graph DBMS

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

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

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	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	1.48
Rank	#158	Overall
	#15	Graph DBMS

Score	134.78
Rank	#7	Overall
	#1	Search engines

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	0.55
Rank	#252	Overall
	#38	Document stores

Website

aws.amazon.com/redshift

dgraph.io

www.elastic.co/elasticsearch

www.esgyn.cn

www.harperdb.io

Technical documentation

docs.aws.amazon.com/redshift

dgraph.io/docs

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

docs.harperdb.io/docs

Developer

Amazon (based on PostgreSQL)

Dgraph Labs, Inc.

Elastic

Esgyn

HarperDB

Initial release

2012

2016

2010

2015

2017

Current release

8.6, January 2023

3.1, August 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Elastic License

commercial

free community edition available

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++, Java

Node.js

Server operating systems

hosted

Linux
OS X
Windows

All OS with a Java VM

Linux

Linux
OS X

Data scheme

yes

schema-free

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

yes

dynamic schema

Typing

predefined data types such as float or date

yes

JSON data types

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

SQL-like data manipulation statements

APIs and other access methods

JDBC
ODBC

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

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

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

All languages supporting JDBC/ODBC/ADO.Net

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Java Stored Procedures

Custom Functions

since release 3.1

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous replication via Raft

yes

Multi-source replication between multi datacenters

yes

the nodes on which a table resides can be defined

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

yes

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

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

User concepts

Access control

fine grained access rights according to SQL-standard

Planned for future releases

fine grained access rights according to SQL-standard

Access rights for users and roles

More information provided by the system vendor

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.