DBMS > DuckDB vs. Google BigQuery vs. SingleStore vs. Spark SQL vs. Transbase

System Properties Comparison DuckDB vs. Google BigQuery vs. SingleStore vs. Spark SQL vs. Transbase

Editorial information provided by DB-Engines

Name

Description

An embeddable, in-process, column-oriented SQL OLAP RDBMS

Large scale data warehouse service with append-only tables

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

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

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	5.38
Rank	#62	Overall
	#35	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

duckdb.org

cloud.google.com/bigquery

www.singlestore.com

spark.apache.org/sql

www.transaction.de/en/products/transbase.html

Technical documentation

duckdb.org/docs

cloud.google.com/bigquery/docs

docs.singlestore.com

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

www.transaction.de/en/products/transbase/features.html

Developer

Google

SingleStore Inc.

Apache Software Foundation

Transaction Software GmbH

Initial release

2018

2010

2013

2014

1987

Current release

0.10, February 2024

8.5, January 2024

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

MIT License

commercial

free developer edition available

Open Source

Apache 2.0

commercial

free development license

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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

C++

C++, Go

Scala

C and C++

Server operating systems

server-less

hosted

Linux

64 bit version required

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

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

but no triggers and foreign keys

SQL-like DML and DDL statements

yes

APIs and other access methods

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

RESTful HTTP/JSON API

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
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

hash partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

stores two copies of each physical data partition on two separate nodes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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)

Fine grained access control via users, groups and roles

fine grained access rights according to SQL-standard

More information provided by the system vendor

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Key customers

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Market metrics

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Licensing and pricing models

F ree Tier and Enterprise Edition
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