DBMS > Google BigQuery vs. PostGIS vs. QuestDB vs. Sequoiadb vs. Spark SQL

System Properties Comparison Google BigQuery vs. PostGIS vs. QuestDB vs. Sequoiadb vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Spatial extension of PostgreSQL

A high performance open source SQL database for time series data

NewSQL database with distributed OLTP and SQL

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Spatial DBMS

Time Series DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Score	2.70
Rank	#105	Overall
	#8	Time Series DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

cloud.google.com/bigquery

Technical documentation

cloud.google.com/bigquery/docs

postgis.net/documentation

questdb.io/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Google

QuestDB Technology Inc

Sequoiadb Ltd.

Apache Software Foundation

Initial release

2010

2005

2014

2013

2014

Current release

3.4.2, February 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

GPL v2.0

Open Source

Apache 2.0

Open Source

Server: AGPL; Client: Apache V2

Open Source

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

C++

Scala

Server operating systems

hosted

Linux
macOS
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free via InfluxDB Line Protocol

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

SQL with time-series extensions

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

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

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

over HTTP

.Net
C++
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

based on PostgreSQL

horizontal partitioning (by timestamps)

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on PostgreSQL

Source-replica replication with eventual consistency

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

ACID for single-table writes

Document is locked during a transaction

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

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)

yes

based on PostgreSQL

simple password-based access control

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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