DBMS > Amazon Neptune vs. LeanXcale vs. Pinecone vs. YTsaurus

System Properties Comparison Amazon Neptune vs. LeanXcale vs. Pinecone vs. YTsaurus

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

A managed, cloud-native vector database

YTsaurus is an open source platform for distributed storage and processing.

Primary database model

Graph DBMS
RDF store

Key-value store
Relational DBMS

Vector DBMS

Document store
Key-value store

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

Score	2.20
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.28
Rank	#286	Overall
	#41	Key-value stores
	#130	Relational DBMS

Score	3.02
Rank	#87	Overall
	#3	Vector DBMS

Score	0.15
Rank	#323	Overall
	#44	Document stores
	#47	Key-value stores

Website

aws.amazon.com/neptune

www.leanxcale.com

www.pinecone.io

ytsaurus.tech

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.pinecone.io/docs/overview

ytsaurus.tech/docs/en

Developer

Amazon

LeanXcale

Pinecone Systems, Inc

Yandex

Initial release

2017

2015

2019

2023

License

Commercial or Open Source

commercial

Open Source

Apache License, Version 2.0

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

C++

Server operating systems

hosted

Ubuntu

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

String, Number, 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.

Secondary indexes

SQL

Support of SQL

yes

through Apache Derby

YQL, an SQL-based language, is supported

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

RESTful HTTP API

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C
Java
Scala

Python

C++
Go
Java
JavaScript
Python

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

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.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

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)

Access Control Lists

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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