DBMS > Apache Impala vs. Hazelcast vs. OpenQM vs. SpaceTime

System Properties Comparison Apache Impala vs. Hazelcast vs. OpenQM vs. SpaceTime

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A widely adopted in-memory data grid

QpenQM is a high-performance, self-tuning, multi-value DBMS

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

Relational DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	0.34
Rank	#284	Overall
	#10	Multivalue DBMS

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Website

impala.apache.org

hazelcast.com

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

www.mireo.com/spacetime

Technical documentation

impala.apache.org/impala-docs.html

hazelcast.org/imdg/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Hazelcast

Rocket Software, originally Martin Phillips

Mireo

Initial release

2013

2008

1993

2020

Current release

4.1.0, June 2022

5.3.6, November 2023

3.4-12

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2; commercial licenses available

Open Source

GPLv2, extended commercial license available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

Linux

All OS with a Java VM

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

with some exceptions

yes

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.

yes

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

A subset of ANSI SQL is implemented

APIs and other access methods

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net
Basic
C
Java
Objective C
PHP
Python

C#
C++
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Event Listeners, Executor Services

yes

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Replicated Map

yes

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Role-based access control

Access rights can be defined down to the item level

yes

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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

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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

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