DBMS > Apache Druid vs. Google BigQuery vs. Manticore Search vs. Microsoft Azure Table Storage vs. Oracle Rdb

System Properties Comparison Apache Druid vs. Google BigQuery vs. Manticore Search vs. Microsoft Azure Table Storage vs. Oracle Rdb

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

Multi-storage database for search, including full-text search.

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Relational DBMS
Time Series DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Website

druid.apache.org

cloud.google.com/bigquery

manticoresearch.com

azure.microsoft.com/en-us/services/storage/tables

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

manual.manticoresearch.com

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Apache Software Foundation and contributors

Google

Manticore Software

Microsoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2012

2010

2017

2012

1984

Current release

29.0.1, April 2024

6.0, February 2023

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

GPL version 2

commercial

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++

Server operating systems

Linux
OS X
Unix

hosted

FreeBSD
Linux
macOS
Windows

hosted

HP Open VMS

Data scheme

yes

schema-less columns are supported

yes

Fixed schema

schema-free

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL for querying

yes

SQL-like query language

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

RESTful HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Synchronous replication based on Galera library

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

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

yes

isolated transactions for atomic changes and binary logging for safe writes

optimistic locking

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

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

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)

Access rights based on private key authentication or shared access signatures

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