DBMS > Bangdb vs. Geode vs. Google BigQuery vs. Spark SQL vs. SwayDB

System Properties Comparison Bangdb vs. Geode vs. Google BigQuery vs. Spark SQL vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

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

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Large scale data warehouse service with append-only tables

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Document store
Graph DBMS
Time Series DBMS

Key-value store

Relational DBMS

Key-value store

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	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

bangdb.com

geode.apache.org

cloud.google.com/bigquery

spark.apache.org/sql

swaydb.simer.au

Technical documentation

docs.bangdb.com

geode.apache.org/docs

cloud.google.com/bigquery/docs

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

Developer

Sachin Sinha, BangDB

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Google

Apache Software Foundation

Simer Plaha

Initial release

2012

2002

2010

2014

2018

Current release

BangDB 2.0, October 2021

1.1, February 2017

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

Open Source

Apache 2.0

Open Source

GNU Affero GPL V3.0

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++

Java

Scala

Server operating systems

Linux

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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 query language (OQL)

yes

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Java Client API
Memcached protocol
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

.Net
All JVM based languages
C++
Groovy
Java
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
Python
R
Scala

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes, Notifications (with Streaming only)

yes

Cache Event Listeners

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

Sharding

none

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes, on a single node

Since BigQuery is designed for querying data

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

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

User concepts

Access control

yes (enterprise version only)

Access rights per client and object definable

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

Google Cloud Identity & Access Management (IAM)

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