DBMS > EJDB vs. LevelDB vs. Lovefield vs. TimescaleDB vs. Yaacomo

System Properties Comparison EJDB vs. LevelDB vs. Lovefield vs. TimescaleDB 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)

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Embeddable relational database for web apps written in pure JavaScript

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

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

Primary database model

Secondary database models

Relational DBMS

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

github.com/Softmotions/ejdb

github.com/google/leveldb

google.github.io/lovefield

www.timescale.com

yaacomo.com

Technical documentation

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

github.com/google/leveldb/blob/main/doc/index.md

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

docs.timescale.com

Developer

Softmotions

Google

Timescale

Q2WEB GmbH

Initial release

2012

2011

2014

2017

2009

Current release

1.23, February 2021

2.1.12, February 2017

2.15.0, May 2024

License

Commercial or Open Source

Open Source

GPLv2

Open Source

BSD

Open Source

Apache 2.0

Open Source

Apache 2.0

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

C++

JavaScript

Server operating systems

server-less

Illumos
Linux
OS X
Windows

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

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex 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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

in-process shared library

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

Supported programming languages

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

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

JavaScript

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, across time and space (hash partitioning) attributes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication with hot standby and reads on replicas

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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

fine grained access rights according to SQL-standard

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