DBMS > Google BigQuery vs. SAP HANA vs. VictoriaMetrics vs. Vitess

System Properties Comparison Google BigQuery vs. SAP HANA vs. VictoriaMetrics vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

In-memory, column based data store. Available as appliance or cloud service

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

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Document store
Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	1.23
Rank	#172	Overall
	#15	Time Series DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

cloud.google.com/bigquery

www.sap.com/products/hana.html

victoriametrics.com

vitess.io

Technical documentation

cloud.google.com/bigquery/docs

help.sap.com/hana

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

vitess.io/docs

Developer

Google

SAP

VictoriaMetrics

The Linux Foundation, PlanetScale

Initial release

2010

2018

2013

Current release

2.0 SPS07 (April 4, 2023), April 2023

v1.91, May 2023

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Server operating systems

hosted

Appliance or cloud-service

FreeBSD
Linux
macOS
OpenBSD

Docker
Linux
macOS

Data scheme

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

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

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

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

SQLScript, R

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous replication

Multi-source replication
Source-replica 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

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

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

Google Cloud Identity & Access Management (IAM)

yes

Users with fine-grained authorization concept

no user groups or roles

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