DBMS > Amazon Neptune vs. Apache IoTDB vs. Manticore Search vs. PostgreSQL vs. SurrealDB

System Properties Comparison Amazon Neptune vs. Apache IoTDB vs. Manticore Search vs. PostgreSQL vs. SurrealDB

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Multi-storage database for search, including full-text search.

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Document store
Graph DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	2.13
Rank	#111	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	1.45
Rank	#143	Overall
	#13	Time Series DBMS

Score	0.28
Rank	#283	Overall
	#21	Search engines

Score	674.32
Rank	#4	Overall
	#4	Relational DBMS

Score	1.08
Rank	#176	Overall
	#30	Document stores
	#16	Graph DBMS

Website

aws.amazon.com/neptune

Technical documentation

aws.amazon.com/neptune/developer-resources

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

manual.manticoresearch.com

www.postgresql.org/docs

surrealdb.com/docs

Developer

Amazon

Apache Software Foundation

Manticore Software

PostgreSQL Global Development Group

www.postgresql.org/developer

SurrealDB Ltd

Initial release

2017

2018

2017

1989

1989: Postgres, 1996: PostgreSQL

2022

Current release

1.1.0, April 2023

6.0, February 2023

16.4, August 2024

v1.5.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL version 2

Open Source

BSD

Open Source

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Rust

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
macOS
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux
macOS
Windows

Data scheme

schema-free

yes

Fixed schema

yes

schema-free

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language

yes

standard with numerous extensions

SQL-like query language

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
Native API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C
C#
C++
Go
Java
Python
Scala

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

Partitioning is done manually, search queries against distributed index is supported

partitioning by range, list and (since PostgreSQL 11) by hash

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.

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Synchronous replication based on Galera library

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

The original contents of fields are not stored in the Manticore index.

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

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

yes

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

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