DBMS > AgensGraph vs. HarperDB vs. InfinityDB vs. Microsoft SQL Server vs. Pinecone

System Properties Comparison AgensGraph vs. HarperDB vs. InfinityDB vs. Microsoft SQL Server vs. Pinecone

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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.

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

Microsofts flagship relational DBMS

A managed, cloud-native vector database

Primary database model

Graph DBMS
Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

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

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	3.23
Rank	#92	Overall
	#2	Vector DBMS

Website

bitnine.net/agensgraph

www.harperdb.io

boilerbay.com

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

www.pinecone.io

Technical documentation

bitnine.net/documentation

docs.harperdb.io/docs

boilerbay.com/infinitydb/manual

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

docs.pinecone.io/docs/overview

Developer

Bitnine Global Inc.

HarperDB

Boiler Bay Inc.

Microsoft

Pinecone Systems, Inc

Initial release

2016

2017

2002

1989

2019

Current release

2.1, December 2018

3.1, August 2021

4.0

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

free community edition available

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Node.js

Java

C++

Server operating systems

Linux
OS X
Windows

Linux
OS X

All OS with a Java VM

Linux
Windows

hosted

Data scheme

depending on used data model

dynamic schema

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

JSON data types

yes

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

yes

String, Number, Boolean

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

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

yes

SQL

Support of SQL

yes

SQL-like data manipulation statements

yes

APIs and other access methods

Cypher Query Language
JDBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

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 API

Supported programming languages

C
Java
JavaScript
Python

.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

Java

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

Python

Server-side scripts

Stored procedures

yes

Custom Functions

since release 3.1

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

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

none

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

the nodes on which a table resides can be defined

none

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

yes

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

Atomic execution of specific operations

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

yes, using LMDB

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

fine grained access rights according to SQL-standard

Access rights for users and roles

fine grained access rights according to SQL-standard

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