DBMS > Bangdb vs. Ingres vs. Spark SQL

System Properties Comparison Bangdb vs. Ingres vs. Spark SQL

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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

Well established RDBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational 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	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

bangdb.com

www.actian.com/databases/ingres

spark.apache.org/sql

Technical documentation

docs.bangdb.com

docs.actian.com/ingres

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Sachin Sinha, BangDB

Actian Corporation

Apache Software Foundation

Initial release

2012

1974

originally developed at University Berkely in early 1970s

2014

Current release

BangDB 2.0, October 2021

11.2, May 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Scala

Server operating systems

Linux

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol
RESTful HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

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

horizontal partitioning

Ingres Star to access multiple databases simultaneously

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

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

Ingres Replicator

none

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

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

MVCC

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

User concepts

Access control

yes (enterprise version only)

fine grained access rights according to SQL-standard

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