DBMS > Amazon DocumentDB vs. Bangdb vs. Manticore Search vs. Vertica

System Properties Comparison Amazon DocumentDB vs. Bangdb vs. Manticore Search vs. Vertica

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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

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store

Document store
Graph DBMS
Time Series DBMS

Search engine

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS

Time Series DBMS

using the Manticore Columnar Library

Spatial DBMS
Time Series DBMS

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	0.07
Rank	#346	Overall
	#47	Document stores
	#35	Graph DBMS
	#32	Time Series DBMS

Score	0.23
Rank	#301	Overall
	#21	Search engines

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

aws.amazon.com/documentdb

bangdb.com

manticoresearch.com

www.vertica.com

Technical documentation

aws.amazon.com/documentdb/resources

docs.bangdb.com

manual.manticoresearch.com

vertica.com/documentation

Developer

Sachin Sinha, BangDB

Manticore Software

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2019

2012

2017

2005

Current release

BangDB 2.0, October 2021

6.0, February 2023

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

BSD 3

Open Source

GPL version 2

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C++

Server operating systems

hosted

Linux

FreeBSD
Linux
macOS
Windows

Linux

Data scheme

schema-free

Fixed schema

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

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

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

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.

Can index from XML

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

full-text index on all search fields

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Proprietary protocol
RESTful HTTP API

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

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Java
Python

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

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, Notifications (with Streaming only)

yes, called Custom Alerts

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

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

horizontal partitioning, hierarchical partitioning

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)

Synchronous replication based on Galera library

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

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.

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

User concepts

Access control

Access rights for users and roles

yes (enterprise version only)

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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