DBMS > Amazon Neptune vs. Axibase vs. InfinityDB vs. Microsoft SQL Server vs. Prometheus

System Properties Comparison Amazon Neptune vs. Axibase vs. InfinityDB vs. Microsoft SQL Server vs. Prometheus

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

A Java embedded Key-Value Store which extends the Java Map interface

Microsofts flagship relational DBMS

Open-source Time Series DBMS and monitoring system

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Website

aws.amazon.com/neptune

axibase.com/docs/atsd/finance

boilerbay.com

www.microsoft.com/en-us/sql-server

prometheus.io

Technical documentation

aws.amazon.com/neptune/developer-resources

boilerbay.com/infinitydb/manual

learn.microsoft.com/en-US/sql/sql-server

prometheus.io/docs

Developer

Amazon

Axibase Corporation

Boiler Bay Inc.

Microsoft

Initial release

2017

2013

2002

1989

2015

Current release

15585

4.0

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

Server operating systems

hosted

Linux

All OS with a Java VM

Linux
Windows

Data scheme

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

Numeric data only

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

Import of XML data possible

Secondary indexes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

RESTful HTTP/JSON API

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

Go
Java
PHP
Python
R
Ruby

Java

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

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.

Source-replica replication

none

yes, but depending on the SQL-Server Edition

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

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

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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