DBMS > Ehcache vs. Lovefield vs. PlanetScale vs. Snowflake vs. Stardog

System Properties Comparison Ehcache vs. Lovefield vs. PlanetScale vs. Snowflake vs. Stardog

Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

Embeddable relational database for web apps written in pure JavaScript

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Cloud-based data warehousing service for structured and semi-structured data

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Key-value store

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Document store
Spatial DBMS

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

Score	4.79
Rank	#66	Overall
	#8	Key-value stores

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	1.09
Rank	#178	Overall
	#82	Relational DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	1.93
Rank	#121	Overall
	#10	Graph DBMS
	#6	RDF stores

Website

www.ehcache.org

google.github.io/lovefield

planetscale.com

www.snowflake.com

www.stardog.com

Technical documentation

www.ehcache.org/documentation

github.com/google/lovefield/blob/master/docs/spec_index.md

planetscale.com/docs

docs.snowflake.net/manuals/index.html

docs.stardog.com

Developer

Terracotta Inc, owned by Software AG

Google

PlanetScale

Snowflake Computing Inc.

Stardog-Union

Initial release

2009

2014

2020

2014

2010

Current release

3.10.0, March 2022

2.1.12, February 2017

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

JavaScript

Java

Server operating systems

All OS with a Java VM

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

tested with Chrome, Firefox, IE, Safari

Docker
Linux
macOS

hosted

Linux
macOS
Windows

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

schema-free and OWL/RDFS-schema support

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.

yes

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

with proprietary extensions

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JCache

ADO.NET
JDBC
MySQL protocol
ODBC

CLI Client
JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Java

JavaScript

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript (Node.js)
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

Cache Event Listeners

Using read-only observers

yes

similar concept for controling cloud resources

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

none

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

none

Multi-source replication
Source-replica replication

yes

Multi-source replication in HA-Cluster

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)

Eventual Consistency across shards
Immediate Consistency within a shard

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

relationships in graphs

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

Users with fine-grained authorization concept

no user groups or roles

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights for users and roles

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