DBMS > Amazon DocumentDB vs. Bangdb vs. Cassandra vs. Hawkular Metrics vs. Manticore Search

System Properties Comparison Amazon DocumentDB vs. Bangdb vs. Cassandra vs. Hawkular Metrics vs. Manticore Search

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

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

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Multi-storage database for search, including full-text search.

Primary database model

Document store

Document store
Graph DBMS
Time Series DBMS

Wide column store

Time Series DBMS

Search engine

Secondary database models

Spatial DBMS

Vector DBMS

starting with release V5

Time Series DBMS

using the Manticore Columnar Library

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Website

aws.amazon.com/documentdb

Technical documentation

aws.amazon.com/documentdb/resources

docs.bangdb.com

cassandra.apache.org/doc/latest

www.hawkular.org/hawkular-metrics/docs/user-guide

manual.manticoresearch.com

Developer

Sachin Sinha, BangDB

Apache Software Foundation

Apache top level project, originally developped by Facebook

Community supported by Red Hat

Manticore Software

Initial release

2019

2012

2008

2014

2017

Current release

BangDB 2.0, October 2021

4.1.3, July 2023

6.0, February 2023

License

Commercial or Open Source

commercial

Open Source

BSD 3

Open Source

Apache version 2

Open Source

Apache 2.0

Open Source

GPL version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

C, C++

Java

C++

Server operating systems

hosted

Linux

BSD
Linux
OS X
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Windows

Data scheme

schema-free

Fixed schema

Typing

predefined data types such as float or date

yes

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

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

restricted

only equality queries, not always the best performing solution

yes

full-text index on all search fields

SQL

Support of SQL

SQL like support with command line tool

SQL-like SELECT, DML and DDL statements (CQL)

SQL-like query language

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Proprietary protocol
RESTful HTTP API

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

HTTP REST

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Java
Python

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

Go
Java
Python
Ruby

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Server-side scripts

Stored procedures

user defined functions

Triggers

yes, Notifications (with Streaming only)

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

no "single point of failure"

Sharding

based on Cassandra

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

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

selectable replication factor

Representation of geographical distribution of servers is possible

selectable replication factor

based on Cassandra

Synchronous replication based on Galera library

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic single-document operations

ACID

Atomicity and isolation are supported for single operations

yes

isolated transactions for atomic changes and binary logging for safe writes

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

The original contents of fields are not stored in the Manticore index.

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

User concepts

Access control

Access rights for users and roles

yes (enterprise version only)

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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