DBMS > Amazon Neptune vs. etcd vs. HugeGraph vs. Oracle vs. Sqrrl

System Properties Comparison Amazon Neptune vs. etcd vs. HugeGraph vs. Oracle vs. Sqrrl

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

Fast, reliable graph database built for the cloud

A distributed reliable key-value store

A fast-speed and highly-scalable Graph DBMS

Widely used RDBMS

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	2.58
Rank	#112	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	7.64
Rank	#54	Overall
	#5	Key-value stores

Score	0.15
Rank	#339	Overall
	#33	Graph DBMS

Score	1234.27
Rank	#1	Overall
	#1	Relational DBMS

Website

aws.amazon.com/neptune

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

github.com/hugegraph
hugegraph.apache.org

www.oracle.com/database

sqrrl.com

Technical documentation

aws.amazon.com/neptune/developer-resources

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

hugegraph.apache.org/docs

docs.oracle.com/en/database

Developer

Amazon

Baidu

Oracle

Amazon

originally Sqrrl Data, Inc.

Initial release

2017

2018

1980

2012

Current release

3.4, August 2019

0.9

23c, September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

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 and C++

Java

Server operating systems

hosted

FreeBSD
Linux
Windows

experimental

Linux
macOS
Unix

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

schema-free

Typing

predefined data types such as float or date

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

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

gRPC
JSON over HTTP

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

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

Groovy
Java
Python

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

PL/SQL

also stored procedures in Java possible

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

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

Sharding, horizontal partitioning

Sharding

making use of Hadoop

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.

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

yes

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

Multi-source replication
Source-replica replication

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

can be realized in PL/SQL

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

edges in graph

yes

Transaction concepts

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

ACID

isolation level can be parameterized

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

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

Users, roles and permissions

fine grained access rights according to SQL-standard

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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