DBMS > Apache Impala vs. DataFS vs. GridGain vs. Sequoiadb

System Properties Comparison Apache Impala vs. DataFS vs. GridGain vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS

Object oriented DBMS

Key-value store
Relational DBMS

Document store
Relational DBMS

Secondary database models

Document store

Graph DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

dev.mobiland.com/Overview.xsp

www.gridgain.com/docs/index.html

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Mobiland AG

GridGain Systems, Inc.

Sequoiadb Ltd.

Initial release

2013

2018

2007

2013

Current release

4.1.0, June 2022

1.1.263, October 2022

GridGain 8.5.1

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java, C++, .Net

C++

Server operating systems

Linux

Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

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

SQL-like query language

APIs and other access methods

JDBC
ODBC

.NET Client API
Proprietary client DLL
WinRT client

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

proprietary protocol using JSON

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
VB.Net

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

JavaScript

Triggers

no, except callback-events from server when changes happened

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes (replicated cache)

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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 rights for users, groups and roles

based on Apache Sentry and Kerberos

Windows-Profile

Security Hooks for custom implementations

simple password-based access control

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More resources

Recent citations in the news

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

GridGain in-memory data and generative AI – Blocks and Files
10 May 2024, Blocks & Files

GridGain's 2023 Growth Positions Company for Strong 2024
24 January 2024, PR Newswire

GridGain Unified Real-Time Data Platform Version 8.9 Addresses Today's More Complex Real-Time Data Processing ...
12 October 2023, GlobeNewswire

GridGain Showcases Power of Apache Ignite at Community Over Code Conference
5 October 2023, Datanami

GridGain Announces Call for Speakers for Virtual Apache Ignite Summit 2024
8 February 2024, PR Newswire

provided by Google News

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