DBMS > Google BigQuery vs. Google Cloud Spanner vs. InfluxDB vs. MaxDB vs. Quasardb

System Properties Comparison Google BigQuery vs. Google Cloud Spanner vs. InfluxDB vs. MaxDB vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

DBMS for storing time series, events and metrics

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

Distributed, high-performance timeseries database

Primary database model

Secondary database models

Spatial DBMS

with GEO package

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	2.26
Rank	#114	Overall
	#55	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

cloud.google.com/bigquery

cloud.google.com/spanner

www.influxdata.com/products/influxdb-overview

maxdb.sap.com

quasar.ai

Technical documentation

cloud.google.com/bigquery/docs

cloud.google.com/spanner/docs

docs.influxdata.com/influxdb

maxdb.sap.com/documentation

doc.quasar.ai/master

Developer

Google

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

quasardb

Initial release

2010

2017

2013

1984

2009

Current release

2.7.6, April 2024

7.9.10.12, February 2024

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

MIT-License; commercial enterprise version available

commercial

Limited community edition free

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

C++

Server operating systems

hosted

Linux
OS X

through Homebrew

AIX
HP-UX
Linux
Solaris
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

yes

integer and binary

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

with tags

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

SQL-like query language

yes

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

HTTP API
JSON over UDP

ADO.NET
JDBC
ODBC
WebDAV

HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
C#
Java
Perl
PHP
Python

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

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

in enterprise version only

none

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

selectable replication factor

in enterprise version only

Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Strict serializable isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Depending on used storage engine

yes

Transient mode

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple rights management via user accounts

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

More information provided by the system vendor

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Market metrics

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Licensing and pricing models

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