DBMS > Atos Standard Common Repository vs. QuestDB vs. SpatiaLite vs. STSdb vs. Vertica

System Properties Comparison Atos Standard Common Repository vs. QuestDB vs. SpatiaLite vs. STSdb vs. Vertica

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

A high performance open source SQL database for time series data

Spatial extension of SQLite

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store
Key-value store

Column oriented

Secondary database models

Relational DBMS

Spatial DBMS
Time Series DBMS

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

Score	2.81
Rank	#98	Overall
	#7	Time Series DBMS

Score	1.56
Rank	#142	Overall
	#3	Spatial DBMS

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

questdb.io

www.gaia-gis.it/fossil/libspatialite/index

github.com/STSSoft/STSdb4

www.vertica.com

Technical documentation

questdb.io/docs

www.gaia-gis.it/gaia-sins/spatialite_topics.html

vertica.com/documentation

Developer

Atos Convergence Creators

QuestDB Technology Inc

Alessandro Furieri

STS Soft SC

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2016

2014

2008

2011

2005

Current release

1703

5.0.0, August 2020

4.0.8, September 2015

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Open Source

GPLv2, commercial license available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Java (Zero-GC), C++, Rust

C++

Server operating systems

Linux

Linux
macOS
Windows

server-less

Windows

Linux

Data scheme

Schema and schema-less with LDAP views

yes

schema-free via InfluxDB Line Protocol

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

optional

yes

primitive types and user defined types (classes)

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL with time-series extensions

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

LDAP

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

.NET Client API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

All languages with LDAP bindings

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

C#
Java

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

horizontal partitioning (by timestamps)

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication with eventual consistency

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID for single-table writes

ACID

Concurrency

Support for concurrent manipulation of data

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

through memory mapped files

yes

User concepts

Access control

LDAP bind authentication

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

News

Combine Java and Rust Code Coverage in a Polyglot Project
10 September 2024

Weather data visualization and forecasting with QuestDB, Kafka and Grafana
4 September 2024

Building a new vector based storage model
22 August 2024

Calibrating VWAP executions with QuestDB and Grafana
16 August 2024

Write Time: a call for community writers
13 August 2024

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