DBMS > Apache Druid vs. Google BigQuery vs. Linter vs. Tarantool

System Properties Comparison Apache Druid vs. Google BigQuery vs. Linter vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

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

RDBMS for high security requirements

In-memory database with an integrated application server

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

Document store
Relational DBMS
Spatial DBMS

with Tarantool/GIS extension

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

Score	3.00
Rank	#105	Overall
	#54	Relational DBMS
	#6	Time Series DBMS

Score	48.61
Rank	#23	Overall
	#15	Relational DBMS

Score	0.11
Rank	#309	Overall
	#142	Relational DBMS

Score	1.34
Rank	#151	Overall
	#28	Key-value stores

Website

druid.apache.org

cloud.google.com/bigquery

linter.ru/en

www.tarantool.io

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

www.tarantool.io/en/doc

Developer

Apache Software Foundation and contributors

Google

relex.ru/en

Mail.ru Group

Initial release

2012

2010

1990

2008

Current release

0.22.1, December 2021

2.4.2, July 2020

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Server operating systems

Linux
OS X
Unix

hosted

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

BSD
Linux
macOS

Data scheme

yes

schema-less columns are supported

yes

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean

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

SQL for querying

yes

Full-featured ANSI SQL support

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Open binary protocol

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax with the possibility to convert from PL/SQL

Lua, C and SQL stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Hash sharding based on virtual buckets

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Source-replica replication

Multi-source replication
Source-replica replication

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

yes, with SQL

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

ACID, read commited

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, regular snapshots on disk, write ahead logging

In-memory capabilities

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

yes, full featured in-memory storage engine with persistence

User concepts

Access control

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

Access rights for users and roles

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