DBMS > Ehcache vs. Google BigQuery vs. Splice Machine vs. ToroDB

System Properties Comparison Ehcache vs. Google BigQuery vs. Splice Machine vs. ToroDB

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Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A widely adopted Java cache with tiered storage options

Large scale data warehouse service with append-only tables

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Key-value store

Relational DBMS

Document store

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	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.ehcache.org

cloud.google.com/bigquery

splicemachine.com

github.com/torodb/server

Technical documentation

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

splicemachine.com/how-it-works

Developer

Terracotta Inc, owned by Software AG

Google

Splice Machine

8Kdata

Initial release

2009

2010

2014

2016

Current release

3.10.0, March 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java VM

hosted

Linux
OS X
Solaris
Windows

All OS with a Java 7 VM

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

yes

APIs and other access methods

JCache

RESTful HTTP/JSON API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Java

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Eventual Consistency
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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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, groups and roles according to SQL-standard

Access rights for users and roles

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