DBMS > LeanXcale vs. Microsoft Azure Cosmos DB vs. Oracle NoSQL vs. Trino

System Properties Comparison LeanXcale vs. Microsoft Azure Cosmos DB vs. Oracle NoSQL vs. Trino

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Editorial information provided by DB-Engines

Name

Description

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

Globally distributed, horizontally scalable, multi-model database service

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Key-value store
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

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

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Website

www.leanxcale.com

azure.microsoft.com/services/cosmos-db

www.oracle.com/database/nosql/technologies/nosql

trino.io

Technical documentation

learn.microsoft.com/azure/cosmos-db

docs.oracle.com/en/database/other-databases/nosql-database/index.html

trino.io/broadcast
trino.io/docs/current

Developer

LeanXcale

Microsoft

Oracle

Trino Software Foundation

Initial release

2015

2014

2011

2012

2020 rebranded from PrestoSQL

Current release

24.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Apache 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

Java

Server operating systems

hosted

Linux
Solaris SPARC/x86

Linux
macOS

for devlopment

Data scheme

yes

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

yes

JSON types

optional

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

yes

All properties auto-indexed by default

yes

depending on connected data-source

SQL

Support of SQL

yes

through Apache Derby

SQL-like query language

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

C
Java
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C#
Go
Java
JavaScript (Node.js)
Python

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

JavaScript

yes, depending on connected data-source

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

depending on connected data-source

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

configurable

ACID within a storage node (=shard)

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

depending on connected data-source

In-memory capabilities

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

yes

off heap cache

User concepts

Access control

Access rights can be defined down to the item level

Access rights for users and roles

SQL standard access control

More information provided by the system vendor

News

23 is a go, keeping pace with Java
17 September 2024

65: Performance boosts
12 September 2024

64: Control with Open Policy Agent OPA
22 August 2024

63: Querying with JS
1 August 2024

62: A lakehouse that simply works at Prezi
11 July 2024

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