DBMS > Google BigQuery vs. GridGain vs. Manticore Search vs. OpenTSDB vs. Riak KV

System Properties Comparison Google BigQuery vs. GridGain vs. Manticore Search vs. OpenTSDB vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

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

Scalable Time Series DBMS based on HBase

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Search engine

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

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	1.59
Rank	#140	Overall
	#12	Time Series DBMS

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Website

cloud.google.com/bigquery

www.gridgain.com

manticoresearch.com

opentsdb.net

Technical documentation

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

manual.manticoresearch.com

opentsdb.net/docs/build/html/index.html

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Google

GridGain Systems, Inc.

Manticore Software

currently maintained by Yahoo and other contributors

OpenSource, formerly Basho Technologies

Initial release

2010

2007

2017

2011

2009

Current release

GridGain 8.5.1

6.0, February 2023

3.2.0, December 2022

License

Commercial or Open Source

commercial

commercial, open source

Open Source

GPL version 2

Open Source

LGPL

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

Server operating systems

hosted

Linux
OS X
Solaris
Windows
z/OS

FreeBSD
Linux
macOS
Windows

Linux
Windows

Linux
OS X

Data scheme

yes

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

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

numeric data for metrics, strings for tags

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

restricted

SQL

Support of SQL

yes

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

SQL-like query language

APIs and other access methods

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

HTTP API
Telnet API

HTTP API
Native Erlang Interface

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

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

Erlang
Go
Java
Python
R
Ruby

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

user defined functions

Erlang

Triggers

yes (cache interceptors and events)

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

Sharding

based on HBase

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Synchronous replication based on Galera library

selectable replication factor

based on HBase

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

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

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.

yes

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Role-based access control
Security Hooks for custom implementations

yes, using Riak Security

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