DBMS > Bangdb vs. EJDB vs. Google BigQuery vs. Manticore Search

System Properties Comparison Bangdb vs. EJDB vs. Google BigQuery vs. Manticore Search

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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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Large scale data warehouse service with append-only tables

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

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

Time Series DBMS

using the Manticore Columnar Library

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	0.31
Rank	#296	Overall
	#44	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Website

bangdb.com

github.com/Softmotions/ejdb

cloud.google.com/bigquery

manticoresearch.com

Technical documentation

docs.bangdb.com

github.com/Softmotions/ejdb/blob/master/README.md

cloud.google.com/bigquery/docs

manual.manticoresearch.com

Developer

Sachin Sinha, BangDB

Softmotions

Google

Manticore Software

Initial release

2012

2010

2017

Current release

BangDB 2.0, October 2021

6.0, February 2023

License

Commercial or Open Source

Open Source

BSD 3

Open Source

GPLv2

commercial

Open Source

GPL version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C++

Server operating systems

Linux

server-less

hosted

FreeBSD
Linux
macOS
Windows

Data scheme

schema-free

yes

Fixed schema

Typing

predefined data types such as float or date

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

yes

string, integer, double, bool, date, object_id

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

yes

full-text index on all search fields

SQL

Support of SQL

SQL like support with command line tool

yes

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

in-process shared library

RESTful HTTP/JSON API

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

Supported programming languages

C
C#
C++
Java
Python

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

Triggers

yes, Notifications (with Streaming only)

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

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

Synchronous replication based on Galera library

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

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

yes

isolated transactions for atomic changes and binary logging for safe writes

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Read/Write Locking

yes

Durability

Support for making data persistent

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

yes (enterprise version only)

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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