DBMS > GridGain vs. Manticore Search vs. Snowflake vs. Trino vs. Yaacomo

System Properties Comparison GridGain vs. Manticore Search vs. Snowflake vs. Trino vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Search engine

Relational DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.23
Rank	#301	Overall
	#21	Search engines

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Website

yaacomo.com

Technical documentation

www.gridgain.com/docs/index.html

manual.manticoresearch.com

docs.snowflake.net/manuals/index.html

trino.io/broadcast
trino.io/docs/current

Developer

GridGain Systems, Inc.

Manticore Software

Snowflake Computing Inc.

Trino Software Foundation

Q2WEB GmbH

Initial release

2007

2017

2014

2012

2020 rebranded from PrestoSQL

2009

Current release

GridGain 8.5.1

6.0, February 2023

License

Commercial or Open Source

commercial, open source

Open Source

GPL version 2

commercial

Open Source

Apache Version 2.0

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++, .Net, Python, REST, SQL

C++

Java

Server operating systems

Linux
OS X
Solaris
Windows
z/OS

FreeBSD
Linux
macOS
Windows

hosted

Linux
macOS

for devlopment

Android
Linux
Windows

Data scheme

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

depending on connected data-source

yes

SQL

Support of SQL

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

SQL-like query language

yes

APIs and other access methods

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

JDBC
RESTful HTTP API
Trino CLI

JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

JavaScript (Node.js)
Python

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

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

user defined functions

yes, depending on connected data-source

Triggers

yes (cache interceptors and events)

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

yes

depending on connected data-source

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Synchronous replication based on Galera library

yes

depending on connected data-source

Source-replica replication

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

depending on connected data-source

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

depending on connected data-source

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

depending on connected data-source

yes

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control
Security Hooks for custom implementations

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

SQL standard access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

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