DBMS > atoti vs. LeanXcale vs. PostGIS vs. Yaacomo

System Properties Comparison atoti vs. LeanXcale vs. PostGIS vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

Spatial extension of PostgreSQL

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Object oriented DBMS

Key-value store
Relational DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Website

atoti.io

www.leanxcale.com

postgis.net

yaacomo.com

Technical documentation

docs.atoti.io

postgis.net/documentation

Developer

ActiveViam

LeanXcale

Q2WEB GmbH

Initial release

2015

2005

2009

Current release

3.4.2, February 2024

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

GPL v2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

Android
Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

through Apache Derby

yes

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JDBC
ODBC

Supported programming languages

C
Java
Scala

Server-side scripts

Stored procedures

Python

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

yes

based on PostgreSQL

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on PostgreSQL

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

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

yes

based on PostgreSQL

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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