DBMS > Apache Doris vs. Apache Druid vs. Google BigQuery vs. TimescaleDB

System Properties Comparison Apache Doris vs. Apache Druid vs. Google BigQuery vs. TimescaleDB

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Large scale data warehouse service with append-only tables

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	0.69
Rank	#237	Overall
	#108	Relational DBMS

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	4.87
Rank	#74	Overall
	#4	Time Series DBMS

Website

doris.apache.org
github.com/apache/doris

druid.apache.org

cloud.google.com/bigquery

www.timescale.com

Technical documentation

github.com/apache/doris/wiki

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

docs.timescale.com

Developer

Apache Software Foundation, originally contributed from Baidu

Apache Software Foundation and contributors

Google

Timescale

Initial release

2017

2012

2010

2017

Current release

1.2.2, February 2023

29.0.1, April 2024

2.13.0, November 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache license v2

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Java

Server operating systems

Linux

Linux
OS X
Unix

hosted

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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 for querying

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
MySQL client

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

Java

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

manual/auto, time-based

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, via HDFS, S3 or other storage engines

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Since BigQuery is designed for querying data

ACID

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.

User concepts

Access control

fine grained access rights according to SQL-standard

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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