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

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

Editorial information provided by DB-Engines

Name

Description

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

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

Microsofts flagship relational DBMS

A managed, cloud-native vector database

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Graph DBMS
Relational DBMS

Key-value store

Relational DBMS

Vector DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

with Tarantool/GIS extension

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	0.08
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

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Website

bitnine.net/agensgraph

boilerbay.com

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

www.pinecone.io

www.tarantool.io

Technical documentation

bitnine.net/documentation

boilerbay.com/infinitydb/manual

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

docs.pinecone.io/docs/overview

www.tarantool.io/en/doc

Developer

Bitnine Global Inc.

Boiler Bay Inc.

Microsoft

Pinecone Systems, Inc

Initial release

2016

2002

1989

2019

2008

Current release

2.1, December 2018

4.0

SQL Server 2022, November 2022

2.10.0, May 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

restricted free version is available

commercial

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

C and C++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Linux
Windows

hosted

BSD
Linux
macOS

Data scheme

depending on used data model

yes

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

yes

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

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

yes

String, Number, Boolean

string, double, decimal, uuid, integer, blob, boolean, datetime

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

Full-featured ANSI SQL support

APIs and other access methods

Cypher Query Language
JDBC

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

Open binary protocol

Supported programming languages

C
Java
JavaScript
Python

Java

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

Python

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

yes

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

Lua, C and SQL stored procedures

Triggers

yes

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

none

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

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes, but depending on the SQL-Server Edition

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

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

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

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

Optimistic locking for transactions; no isolation for bulk loads

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, write ahead logging

In-memory capabilities

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

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

fine grained access rights according to SQL-standard

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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