DBMS > Apache Impala vs. EsgynDB vs. GigaSpaces vs. Tkrzw

System Properties Comparison Apache Impala vs. EsgynDB vs. GigaSpaces vs. Tkrzw

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Key-value store

Secondary database models

Document store

Graph DBMS
Search engine

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

Technical documentation

impala.apache.org/impala-docs.html

docs.gigaspaces.com/latest/landing.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Esgyn

Gigaspaces Technologies

Mikio Hirabayashi

Initial release

2013

2015

2000

2020

Current release

4.1.0, June 2022

15.5, September 2020

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache Version 2.0

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++

C++, Java

Java, C++, .Net

C++

Server operating systems

Linux

Linux
macOS
Solaris
Windows

Linux
macOS

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-99 for query and DML statements

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

Supported programming languages

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/ADO.Net

.Net
C++
Java
Python
Scala

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

yes

Triggers

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication between multi datacenters

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

using specific database classes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

Role-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

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

GigaSpaces to hand out almost $14 million in dividends following Cloudify’s acquisition by Dell
19 July 2023, CTech

GigaSpaces' New Cloudify Player Designed for OpenStack
11 December 2023, Channel Futures

Data Sciences Corporation partners with GigaSpaces Technologies to usher DIH technology to enterprises in SA
10 October 2023, ITWeb

GigaSpaces Announces Version 16.0 with Breakthrough Data Integration Tools to Ease Enterprises' Digital ...
3 November 2021, PR Newswire

GigaSpaces Spins Off Cloudify, Its Open Source Cloud Orchestration Unit
27 July 2017, Data Center Knowledge

provided by Google News

Share this page