DBMS > Apache Impala vs. Datomic vs. EJDB vs. Graph Engine vs. Lovefield

System Properties Comparison Apache Impala vs. Datomic vs. EJDB vs. Graph Engine vs. Lovefield

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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 in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Relational DBMS

Document store

Graph DBMS
Key-value store

Relational DBMS

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Website

impala.apache.org

www.datomic.com

github.com/Softmotions/ejdb

www.graphengine.io

google.github.io/lovefield

Technical documentation

impala.apache.org/impala-docs.html

docs.datomic.com

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

www.graphengine.io/docs/manual

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cognitect

Softmotions

Microsoft

Google

Initial release

2013

2012

2010

2014

Current release

4.1.0, June 2022

1.0.7075, December 2023

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

Open Source

GPLv2

Open Source

MIT License

Open Source

Apache 2.0

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++

Java, Clojure

.NET and C

JavaScript

Server operating systems

Linux

All OS with a Java VM

server-less

.NET

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

tested with Chrome, Firefox, IE, Safari

Data scheme

yes

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

in-process shared library

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

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

C#
C++
F#
Visual Basic

JavaScript

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Transaction Functions

yes

Triggers

By using transaction functions

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

But extensive use of caching in the application peers

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

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

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

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

yes

using MemoryDB

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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