DBMS > 4D vs. Apache Impala vs. BoltDB vs. Datomic vs. Heroic

System Properties Comparison 4D vs. Apache Impala vs. BoltDB vs. Datomic vs. Heroic

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Analytic DBMS for Hadoop

An embedded key-value store for Go.

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Secondary database models

Document store

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Website

www.4d.com

impala.apache.org

github.com/boltdb/bolt

www.datomic.com

github.com/spotify/heroic

Technical documentation

developer.4d.com

impala.apache.org/impala-docs.html

docs.datomic.com

spotify.github.io/heroic

Developer

4D, Inc

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cognitect

Spotify

Initial release

1984

2013

2012

2014

Current release

v20, April 2023

4.1.0, June 2022

1.0.6735, June 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

MIT License

commercial

limited edition free

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

Java

Server operating systems

OS X
Windows

Linux

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Data scheme

yes

schema-free

yes

schema-free

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

Secondary indexes

yes

via Elasticsearch

SQL

Support of SQL

yes

close to SQL 92

SQL-like DML and DDL statements

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC

RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

4D proprietary IDE
PHP

All languages supporting JDBC/ODBC

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Transaction Functions

Triggers

yes

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

But extensive use of caching in the application peers

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

none

But extensive use of caching in the application peers

yes

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

Immediate Consistency

Eventual Consistency

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

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

User concepts

Access control

Users and groups

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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