DBMS > Datomic vs. EJDB vs. Elasticsearch vs. Lovefield vs. Teradata Aster

System Properties Comparison Datomic vs. EJDB vs. Elasticsearch vs. Lovefield vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Embeddable relational database for web apps written in pure JavaScript

Platform for big data analytics on multistructured data sources and types

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

www.datomic.com

github.com/Softmotions/ejdb

www.elastic.co/elasticsearch

google.github.io/lovefield

Technical documentation

docs.datomic.com

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

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

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

Developer

Cognitect

Softmotions

Elastic

Google

Teradata

Initial release

2012

2010

2014

2005

Current release

1.0.6735, June 2023

8.6, January 2023

2.1.12, February 2017

License

Commercial or Open Source

commercial

limited edition free

Open Source

GPLv2

Open Source

Elastic License

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

Java, Clojure

Java

JavaScript

Server operating systems

All OS with a Java VM

server-less

All OS with a Java VM

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

tested with Chrome, Firefox, IE, Safari

Linux

Data scheme

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

in Aster File Store

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

RESTful HTTP API

in-process shared library

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Clojure
Java

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

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

JavaScript

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

R packages

Triggers

By using transaction functions

yes

by using the 'percolation' feature

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

none

yes

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency or Eventual Consistency depending on configuration

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

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

recommended only for testing and development

Memcached and Redis integration

yes

using MemoryDB

User concepts

Access control

fine grained access rights according to SQL-standard

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