DBMS > Ehcache vs. Hive vs. LokiJS vs. Transwarp Hippo

System Properties Comparison Ehcache vs. Hive vs. LokiJS vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

data warehouse software for querying and managing large distributed datasets, built on Hadoop

In-memory JavaScript DBMS

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

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

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.49
Rank	#260	Overall
	#42	Document stores

Score	0.05
Rank	#386	Overall
	#14	Vector DBMS

Website

www.ehcache.org

hive.apache.org

github.com/techfort/LokiJS

www.transwarp.cn/en/subproduct/hippo

Technical documentation

www.ehcache.org/documentation

cwiki.apache.org/confluence/display/Hive/Home

techfort.github.io/LokiJS

Developer

Terracotta Inc, owned by Software AG

Apache Software Foundation

initially developed by Facebook

Initial release

2009

2012

2014

2023

Current release

3.10.0, March 2022

3.1.3, April 2022

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2

Open Source

commercial

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

Java

JavaScript

C++

Server operating systems

All OS with a Java VM

server-less, requires a JavaScript environment (browser, Node.js)

Linux
macOS

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

via views

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JCache

JDBC
ODBC
Thrift

JavaScript API

RESTful HTTP API

Supported programming languages

Java

C++
Java
PHP
Python

JavaScript

C++
Java
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

View functions in JavaScript

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

selectable replication factor

none

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

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

supports JTA and can work as an XA resource

atomic operations within a single collection possible

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

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

Role based access control and fine grained access rights

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More resources

DB-Engines blog posts

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