DBMS > Google BigQuery vs. Oracle NoSQL vs. QuestDB vs. SpaceTime

System Properties Comparison Google BigQuery vs. Oracle NoSQL vs. QuestDB vs. SpaceTime

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

A high performance open source SQL database for time series data

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Relational DBMS

Document store
Key-value store
Relational DBMS

Time Series DBMS

Spatial DBMS

Secondary database models

Relational DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Score	2.81
Rank	#98	Overall
	#7	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#7	Spatial DBMS

Website

cloud.google.com/bigquery

www.oracle.com/database/nosql/technologies/nosql

questdb.io

www.mireo.com/spacetime

Technical documentation

cloud.google.com/bigquery/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

questdb.io/docs

Developer

Google

Oracle

QuestDB Technology Inc

Mireo

Initial release

2010

2011

2014

2020

Current release

24.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Apache 2.0

commercial

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

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

C++

Server operating systems

hosted

Linux
Solaris SPARC/x86

Linux
macOS
Windows

Linux

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

schema-free via InfluxDB Line Protocol

yes

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL with time-series extensions

A subset of ANSI SQL is implemented

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

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

RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
Go
Java
JavaScript (Node.js)
Python

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

over HTTP

C#
C++
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning (by timestamps)

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Source-replica replication with eventual consistency

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

configurable

ACID within a storage node (=shard)

ACID for single-table writes

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

off heap cache

yes

through memory mapped files

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 and roles

yes

More information provided by the system vendor

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