DBMS > Apache Impala vs. EJDB vs. Ignite vs. InfinityDB

System Properties Comparison Apache Impala vs. EJDB vs. Ignite vs. InfinityDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

A Java embedded Key-Value Store which extends the Java Map interface

Primary database model

Relational DBMS

Document store

Key-value store
Relational DBMS

Key-value store

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Website

impala.apache.org

github.com/Softmotions/ejdb

ignite.apache.org

boilerbay.com

Technical documentation

impala.apache.org/impala-docs.html

github.com/Softmotions/ejdb/blob/master/README.md

apacheignite.readme.io/docs

boilerbay.com/infinitydb/manual

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Softmotions

Apache Software Foundation

Boiler Bay Inc.

Initial release

2013

2012

2015

2002

Current release

4.1.0, June 2022

Apache Ignite 2.6

4.0

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GPLv2

Open Source

Apache 2.0

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++

C++, Java, .Net

Java

Server operating systems

Linux

server-less

Linux
OS X
Solaris
Windows

All OS with a Java VM

Data scheme

yes

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

SQL

Support of SQL

SQL-like DML and DDL statements

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

APIs and other access methods

JDBC
ODBC

in-process shared library

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

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

Supported programming languages

All languages supporting JDBC/ODBC

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes (replicated cache)

none

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

READ-COMMITTED or SERIALIZED

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

Security Hooks for custom implementations

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More resources

Recent citations in the news

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6 June 2023, SiliconANGLE News

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29 July 2021, iProgrammer

Apache Impala becomes Top-Level Project
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Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

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provided by Google News

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