DBMS > Apache Druid vs. Google BigQuery vs. InterSystems Caché vs. Manticore Search vs. TerminusDB

System Properties Comparison Apache Druid vs. Google BigQuery vs. InterSystems Caché vs. Manticore Search vs. TerminusDB

Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

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

A multi-model DBMS and application server

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Search engine

Graph DBMS

Secondary database models

Document store

Time Series DBMS

using the Manticore Columnar Library

Document store
RDF store

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

druid.apache.org

cloud.google.com/bigquery

www.intersystems.com/products/cache

manticoresearch.com

terminusdb.com

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

docs.intersystems.com

manual.manticoresearch.com

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation and contributors

Google

InterSystems

Manticore Software

DataChemist Ltd.

Initial release

2012

2010

1997

2017

2018

Current release

29.0.1, April 2024

2018.1.4, May 2020

6.0, February 2023

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

GPL version 2

Open Source

GPL V3

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++

Prolog, Rust

Server operating systems

Linux
OS X
Unix

hosted

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS
Windows

Linux

Data scheme

yes

schema-less columns are supported

yes

depending on used data model

Fixed schema

yes

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.

yes

Can index from XML

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL for querying

yes

SQL-like query language

SQL-like query language (WOQL)

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
RESTful HTTP API

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

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java

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

JavaScript
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions

yes

Triggers

yes

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

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Source-replica replication

Synchronous replication based on Galera library

Journaling Streams

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

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 journaling

In-memory capabilities

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

yes

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 for users, groups and roles

Role-based access control

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