DBMS > atoti vs. LeanXcale vs. Realm vs. Riak TS

System Properties Comparison atoti vs. LeanXcale vs. Realm vs. Riak TS

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Editorial information provided by DB-Engines

Name

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

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Object oriented DBMS

Key-value store
Relational DBMS

Document store

Time Series DBMS

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Website

atoti.io

www.leanxcale.com

realm.io

Technical documentation

docs.atoti.io

realm.io/docs

www.tiot.jp/riak-docs/riak/ts/latest

Developer

ActiveViam

LeanXcale

Realm, acquired by MongoDB in May 2019

Open Source, formerly Basho Technologies

Initial release

2015

2014

2015

Current release

3.0.0, September 2022

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

Server operating systems

Android
Backend: server-less
iOS
Windows

Linux
OS X

Data scheme

yes

schema-free

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.

Secondary indexes

yes

restricted

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

through Apache Derby

yes, limited

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

HTTP API
Native Erlang Interface

Supported programming languages

C
Java
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Python

runs within the applications so server-side scripts are unnecessary

Erlang

Triggers

yes

Change Listeners

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

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

In-Memory realm

User concepts

Access control

yes

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More resources

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