DBMS > Bangdb vs. CrateDB vs. Greenplum vs. RocksDB

System Properties Comparison Bangdb vs. CrateDB vs. Greenplum vs. RocksDB

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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

Distributed Database based on Lucene

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	8.08
Rank	#48	Overall
	#31	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

Technical documentation

docs.bangdb.com

cratedb.com/docs

docs.greenplum.org

github.com/facebook/rocksdb/wiki

Developer

Sachin Sinha, BangDB

Crate

Pivotal Software Inc.

Facebook, Inc.

Initial release

2012

2013

2005

2013

Current release

BangDB 2.0, October 2021

7.0.0, September 2023

9.2.1, May 2024

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0

Open Source

BSD

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, C++

Java

C++

Server operating systems

Linux

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux

Data scheme

schema-free

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

yes

schema-free

Typing

predefined data types such as float or date

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

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

since Version 4.2

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

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

JDBC
ODBC

C++ API
Java API

Supported programming languages

C
C#
C++
Java
Python

.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

C
Java
Perl
Python
R

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

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

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

Configurable replication on table/partition-level

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

yes

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

yes

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)

rights management via user accounts

fine grained access rights according to SQL-standard

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