DBMS > Apache Druid vs. GridGain vs. Manticore Search vs. Snowflake vs. TerminusDB

System Properties Comparison Apache Druid vs. GridGain vs. Manticore Search vs. Snowflake vs. TerminusDB

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

GridGain is an in-memory computing platform, built on Apache Ignite

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

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Relational DBMS
Time Series DBMS

Key-value store
Relational DBMS

Search engine

Relational DBMS

Graph DBMS

Secondary database models

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	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

www.gridgain.com/docs/index.html

manual.manticoresearch.com

docs.snowflake.net/manuals/index.html

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation and contributors

GridGain Systems, Inc.

Manticore Software

Snowflake Computing Inc.

DataChemist Ltd.

Initial release

2012

2007

2017

2014

2018

Current release

29.0.1, April 2024

GridGain 8.5.1

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

commercial

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

Java, C++, .Net

C++

Prolog, Rust

Server operating systems

Linux
OS X
Unix

Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS
Windows

hosted

Linux

Data scheme

yes

schema-less columns are supported

yes

Fixed schema

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL for querying

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

yes

SQL-like query language (WOQL)

APIs and other access methods

JDBC
RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

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

CLI Client
JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

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

JavaScript (Node.js)
Python

JavaScript
Python

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

user defined functions

yes

Triggers

yes (cache interceptors and events)

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

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

yes

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes (replicated cache)

Synchronous replication based on Galera library

yes

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

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

Security Hooks for custom implementations

Users with fine-grained authorization concept, user roles and pluggable authentication

Role-based access control

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