DBMS > Amazon Neptune vs. EsgynDB vs. etcd vs. NSDb vs. XTDB

System Properties Comparison Amazon Neptune vs. EsgynDB vs. etcd vs. NSDb vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

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

A distributed reliable key-value store

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

aws.amazon.com/neptune

www.esgyn.cn

etcd.io
github.com/etcd-io/etcd

nsdb.io

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

aws.amazon.com/neptune/developer-resources

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

nsdb.io/Architecture

www.xtdb.com/docs

Developer

Amazon

Esgyn

Juxt Ltd.

Initial release

2017

2015

2017

2019

Current release

3.4, August 2019

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

MIT License

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

Java, Scala

Clojure

Server operating systems

hosted

Linux

FreeBSD
Linux
Windows

experimental

Linux
macOS

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes, extensible-data-notation format

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

gRPC
JSON over HTTP

gRPC
HTTP REST
WebSocket

HTTP REST
JDBC

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

Java
Scala

Clojure
Java

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

Multi-source replication between multi datacenters

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

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

with encyption-at-rest

yes

Using Apache Lucene

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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