DBMS > Bangdb vs. EJDB vs. Google Cloud Spanner vs. Hypertable vs. Machbase Neo

System Properties Comparison Bangdb vs. EJDB vs. Google Cloud Spanner vs. Hypertable vs. Machbase Neo

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

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)

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

An open source BigTable implementation based on distributed file systems such as Hadoop

TimeSeries DBMS for AIoT and BigData

Primary database model

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

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

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Website

bangdb.com

github.com/Softmotions/ejdb

cloud.google.com/spanner

machbase.com

Technical documentation

docs.bangdb.com

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

cloud.google.com/spanner/docs

machbase.com/dbms

Developer

Sachin Sinha, BangDB

Softmotions

Google

Hypertable Inc.

Machbase

Initial release

2012

2017

2009

2013

Current release

BangDB 2.0, October 2021

0.9.8.11, March 2016

V8.0, August 2023

License

Commercial or Open Source

Open Source

BSD 3

Open Source

GPLv2

commercial

Open Source

GNU version 3. Commercial license available

commercial

free test version available

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

Linux
OS X
Windows

an inofficial Windows port is available

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

yes

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

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

SQL like support with command line tool

yes

Query statements complying to ANSI 2011

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

in-process shared library

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

C++ API
Thrift

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Supported programming languages

C
C#
C++
Java
Python

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

Go
Java
JavaScript (Node.js)
Python

C++
Java
Perl
PHP
Python
Ruby

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Server-side scripts

Stored procedures

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

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

Multi-source replication with 3 replicas for regional instances.

selectable replication factor on file system level

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

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

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

Transaction concepts

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

ACID

Strict serializable isolation

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

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

volatile and lookup table

User concepts

Access control

yes (enterprise version only)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple password-based access control

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