DBMS > Badger vs. Bangdb vs. Spark SQL

System Properties Comparison Badger vs. Bangdb vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

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

Primary database model

Key-value store

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	0.14
Rank	#331	Overall
	#49	Key-value stores

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/dgraph-io/badger

bangdb.com

spark.apache.org/sql

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.bangdb.com

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

Developer

DGraph Labs

Sachin Sinha, BangDB

Apache Software Foundation

Initial release

2017

2012

2014

Current release

BangDB 2.0, October 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD 3

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

BSD
Linux
OS X
Solaris
Windows

Linux

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

SQL

Support of SQL

SQL like support with command line tool

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

yes, Notifications (with Streaming only)

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

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Tunable consistency, set CAP knob accordingly

Foreign keys

Referential integrity

Transaction concepts

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

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

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)

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