DBMS > ArcadeDB vs. Percona Server for MongoDB vs. TimescaleDB vs. Trino

System Properties Comparison ArcadeDB vs. Percona Server for MongoDB vs. TimescaleDB vs. Trino

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Editorial information provided by DB-Engines

Name

Description

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

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

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Document store

Time Series DBMS

Relational DBMS

Secondary database models

Relational 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	0.02
Rank	#374	Overall
	#52	Document stores
	#38	Graph DBMS
	#56	Key-value stores
	#38	Time Series DBMS

Score	0.50
Rank	#250	Overall
	#39	Document stores

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Website

arcadedb.com

www.percona.com/mongodb/software/percona-server-for-mongodb

www.timescale.com

trino.io

Technical documentation

docs.arcadedb.com

docs.percona.com/percona-distribution-for-mongodb

docs.timescale.com

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

Developer

Arcade Data

Percona

Timescale

Trino Software Foundation

Initial release

2021

2015

2017

2012

2020 rebranded from PrestoSQL

Current release

September 2021

3.4.10-2.10, November 2017

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL Version 2

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

C++

Java

Server operating systems

All OS with a Java VM

Linux

Linux
OS X
Windows

Linux
macOS

for devlopment

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

depending on connected data-source

SQL

Support of SQL

SQL-like query language, no joins

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

proprietary protocol using JSON

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

Java

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

JavaScript

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

yes, depending on connected data-source

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, across time and space (hash partitioning) attributes

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Source-replica replication with hot standby and reads on replicas

depending on connected data-source

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
Immediate Consistency

Immediate Consistency

depending on connected data-source

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID

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

via In-Memory Engine

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

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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