DBMS > Ehcache vs. Google BigQuery vs. Rockset

System Properties Comparison Ehcache vs. Google BigQuery vs. Rockset

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Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

Large scale data warehouse service with append-only tables

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Key-value store

Relational DBMS

Document store

Secondary database models

Relational DBMS
Search engine

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

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Website

www.ehcache.org

cloud.google.com/bigquery

rockset.com

Technical documentation

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

docs.rockset.com

Developer

Terracotta Inc, owned by Software AG

Google

Rockset

Initial release

2009

2010

2019

Current release

3.10.0, March 2022

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

All OS with a Java VM

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

dynamic typing

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.

ingestion from XML files supported

Secondary indexes

all fields are automatically indexed

SQL

Support of SQL

yes

Read-only SQL queries, including JOINs

APIs and other access methods

JCache

RESTful HTTP/JSON API

HTTP REST

Supported programming languages

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

none

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Eventual 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

Since BigQuery is designed for querying data

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and organizations can be defined via Rockset console

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