DBMS > Apache Impala vs. Ehcache vs. EXASOL vs. RocksDB

System Properties Comparison Apache Impala vs. Ehcache vs. EXASOL vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A widely adopted Java cache with tiered storage options

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	4.64
Rank	#68	Overall
	#8	Key-value stores

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

Technical documentation

impala.apache.org/impala-docs.html

www.ehcache.org/documentation

www.exasol.com/resources

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Terracotta Inc, owned by Software AG

Exasol

Facebook, Inc.

Initial release

2013

2009

2000

2013

Current release

4.1.0, June 2022

3.10.0, March 2022

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

BSD

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

Linux

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

JCache

.Net
JDBC
ODBC
WebSocket

C++ API
Java API

Supported programming languages

All languages supporting JDBC/ODBC

Java

Java
Lua
Python
R

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

by using Terracotta Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

Access rights for users, groups and roles according to SQL-standard

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