DBMS > Bangdb vs. ClickHouse vs. CrateDB vs. jBASE

System Properties Comparison Bangdb vs. ClickHouse vs. CrateDB vs. jBASE

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Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

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.

Distributed Database based on Lucene

A robust multi-value DBMS comprising development tools and middleware

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Multivalue DBMS

Secondary database models

Spatial DBMS

Time Series DBMS

Relational DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#10	Vector DBMS

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

Website

bangdb.com

clickhouse.com

cratedb.com

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

Technical documentation

docs.bangdb.com

clickhouse.com/docs

cratedb.com/docs

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

Developer

Sachin Sinha, BangDB

Clickhouse Inc.

Crate

Rocket Software (formerly Zumasys)

Initial release

2012

2016

2013

1991

Current release

BangDB 2.0, October 2021

v24.4.2.141-stable, June 2024

5.7

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0

Open Source

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.

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Implementation language

C, C++

C++

Java

Server operating systems

Linux

FreeBSD
Linux
macOS

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

AIX
Linux
Windows

Data scheme

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes

optional

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

Close to ANSI SQL (SQL/JSON + extensions)

yes, but no triggers and constraints, and PostgreSQL compatibility

Embedded SQL for jBASE in BASIC

APIs and other access methods

Proprietary protocol
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

C
C#
C++
Java
Python

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

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

.Net
Basic
Jabbascript
Java

Server-side scripts

Stored procedures

yes

user defined functions (Javascript)

yes

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

key based and custom

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

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

Configurable replication on table/partition-level

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Eventual Consistency
Read-after-write consistency on record level

Foreign keys

Referential integrity

Transaction concepts

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

ACID

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

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.

rights management via user accounts

Access rights can be defined down to the item level

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