DBMS > Lovefield vs. Microsoft Azure Synapse Analytics vs. Oracle Berkeley DB vs. Splice Machine vs. Yaacomo

System Properties Comparison Lovefield vs. Microsoft Azure Synapse Analytics vs. Oracle Berkeley DB vs. Splice Machine vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Embeddable relational database for web apps written in pure JavaScript

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Widely used in-process key-value store

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

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

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

google.github.io/lovefield

azure.microsoft.com/services/synapse-analytics

www.oracle.com/database/technologies/related/berkeleydb.html

splicemachine.com

yaacomo.com

Technical documentation

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

docs.microsoft.com/azure/synapse-analytics

docs.oracle.com/cd/E17076_05/html/index.html

splicemachine.com/how-it-works

Developer

Google

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Splice Machine

Q2WEB GmbH

Initial release

2014

2016

1994

2014

2009

Current release

2.1.12, February 2017

18.1.40, May 2020

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

commercial license available

Open Source

AGPL 3.0, commercial license 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

JavaScript

C++

C, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

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

tested with Chrome, Firefox, IE, Safari

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Solaris
Windows

Android
Linux
Windows

Data scheme

yes

schema-free

yes

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

JDBC
ODBC

Supported programming languages

JavaScript

C#
Java
PHP

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

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

Server-side scripts

Stored procedures

Transact SQL

yes

Java

Triggers

Using read-only observers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Source-replica replication

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

Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

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

yes

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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