DBMS > Google BigQuery vs. Heroic vs. MaxDB vs. VictoriaMetrics

System Properties Comparison Google BigQuery vs. Heroic vs. MaxDB vs. VictoriaMetrics

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A robust and reliable RDBMS optimized to run all major SAP solutions

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

Primary database model

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

cloud.google.com/bigquery

github.com/spotify/heroic

maxdb.sap.com

victoriametrics.com

Technical documentation

cloud.google.com/bigquery/docs

spotify.github.io/heroic

maxdb.sap.com/documentation

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

Developer

Google

Spotify

SAP, acquired from Software AG (Adabas-D) in 1997

VictoriaMetrics

Initial release

2010

2014

1984

2018

Current release

7.9.10.12, February 2024

v1.91, May 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Limited community edition free

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++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows

FreeBSD
Linux
macOS
OpenBSD

Data scheme

yes

schema-free

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
ODBC
WebDAV

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

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

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.

User concepts

Access control

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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