DBMS > Ehcache vs. Google BigQuery vs. HarperDB vs. Newts vs. Quasardb

System Properties Comparison Ehcache vs. Google BigQuery vs. HarperDB vs. Newts vs. Quasardb

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Time Series DBMS based on Cassandra

Distributed, high-performance timeseries database

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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

www.ehcache.org

cloud.google.com/bigquery

www.harperdb.io

opennms.github.io/newts

quasar.ai

Technical documentation

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

docs.harperdb.io/docs

github.com/OpenNMS/newts/wiki

doc.quasar.ai/master

Developer

Terracotta Inc, owned by Software AG

Google

HarperDB

OpenNMS Group

quasardb

Initial release

2009

2010

2017

2014

2009

Current release

3.10.0, March 2022

3.1, August 2021

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

free community edition available

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

Node.js

Java

C++

Server operating systems

All OS with a Java VM

hosted

Linux
OS X

Linux
OS X
Windows

BSD
Linux
OS X
Windows

Data scheme

schema-free

yes

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

yes

integer and binary

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

with tags

SQL

Support of SQL

yes

SQL-like data manipulation statements

SQL-like query language

APIs and other access methods

JCache

RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP REST
Java API

HTTP API

Supported programming languages

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Custom Functions

since release 3.1

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

none

A table resides as a whole on one (or more) nodes in a cluster

Sharding

based on Cassandra

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

the nodes on which a table resides can be defined

selectable replication factor

based on Cassandra

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Since BigQuery is designed for querying data

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

yes, using LMDB

yes

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

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 roles

Cryptographically strong user authentication and audit trail

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