DBMS > Badger vs. ClickHouse vs. dBASE vs. HEAVY.AI vs. Hive

System Properties Comparison Badger vs. ClickHouse vs. dBASE vs. HEAVY.AI vs. Hive

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

dBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Primary database model

Key-value store

Relational DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

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

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	16.20
Rank	#37	Overall
	#23	Relational DBMS

Score	11.18
Rank	#44	Overall
	#28	Relational DBMS

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Website

github.com/dgraph-io/badger

clickhouse.com

www.dbase.com

github.com/heavyai/heavydb
www.heavy.ai

hive.apache.org

Technical documentation

godoc.org/github.com/dgraph-io/badger

clickhouse.com/docs

www.dbase.com/support/knowledgebase

docs.heavy.ai

cwiki.apache.org/confluence/display/Hive/Home

Developer

DGraph Labs

Clickhouse Inc.

Asthon Tate

HEAVY.AI, Inc.

Apache Software Foundation

initially developed by Facebook

Initial release

2017

2016

1979

2016

2012

Current release

v23.12.1.1368-stable, December 2023

dBASE 2019, 2019

5.10, January 2022

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2

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.

Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
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ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.

Implementation language

C++

C++ and CUDA

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS

DOS

dBase Classic
Windows

dBase Pro

Linux

All OS with a Java VM

Data scheme

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

SQL-like DML and DDL statements

APIs and other access methods

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

none

The IDE can access other DBMS or ODBC-sources.

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
Thrift

Supported programming languages

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

dBase proprietary IDE

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

The IDE can access stored procedures in other database systems.

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

key based and custom

none

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

none

Multi-source replication

selectable replication factor

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

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

not for dBase internal data, but IDE does support transactions when accessing external DBMS

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Access rights for users and roles

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

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