DBMS > Atos Standard Common Repository vs. Google BigQuery vs. OpenMLDB vs. VictoriaMetrics

System Properties Comparison Atos Standard Common Repository vs. Google BigQuery vs. OpenMLDB vs. VictoriaMetrics

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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

Large scale data warehouse service with append-only tables

An open-source machine learning database that provides a feature platform for training and inference

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Document store
Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.02
Rank	#367	Overall
	#37	Time Series DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

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

cloud.google.com/bigquery

openmldb.ai

victoriametrics.com

Technical documentation

cloud.google.com/bigquery/docs

openmldb.ai/docs/zh/main

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Atos Convergence Creators

Google

4 Paradigm Inc.

VictoriaMetrics

Initial release

2016

2010

2020

2018

Current release

1703

2024-2 February 2024

v1.91, May 2023

License

Commercial or Open Source

commercial

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

C++, Java, Scala

Server operating systems

Linux

hosted

Linux

FreeBSD
Linux
macOS
OpenBSD

Data scheme

Schema and schema-less with LDAP views

yes

Fixed schema

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

LDAP

RESTful HTTP/JSON API

JDBC
SQLAlchemy

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

Since BigQuery is designed for querying data

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

User concepts

Access control

LDAP bind authentication

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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