DBMS > EJDB vs. HarperDB vs. Lovefield vs. Sphinx vs. Yaacomo

System Properties Comparison EJDB vs. HarperDB vs. Lovefield vs. Sphinx vs. Yaacomo

Editorial information provided by DB-Engines

Name

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

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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.

Embeddable relational database for web apps written in pure JavaScript

Open source search engine for searching in data from different sources, e.g. relational databases

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

Primary database model

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

github.com/Softmotions/ejdb

www.harperdb.io

google.github.io/lovefield

sphinxsearch.com

yaacomo.com

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

docs.harperdb.io/docs

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

sphinxsearch.com/docs

Developer

Softmotions

HarperDB

Google

Sphinx Technologies Inc.

Q2WEB GmbH

Initial release

2012

2017

2014

2001

2009

Current release

3.1, August 2021

2.1.12, February 2017

3.5.1, February 2023

License

Commercial or Open Source

Open Source

GPLv2

commercial

free community edition available

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Node.js

JavaScript

C++

Server operating systems

server-less

Linux
OS X

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

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Android
Linux
Windows

Data scheme

schema-free

dynamic schema

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

JSON data types

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.

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like data manipulation statements

SQL-like query language

via JavaScript builder pattern

SQL-like query language (SphinxQL)

yes

APIs and other access methods

in-process shared library

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Proprietary protocol

JDBC
ODBC

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
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

JavaScript

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

the nodes on which a table resides can be defined

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

yes

The original contents of fields are not stored in the Sphinx index.

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

Access rights for users and roles

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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