DBMS > Apache Ignite vs. Bangdb vs. Google Cloud Spanner

System Properties Comparison Apache Ignite vs. Bangdb vs. Google Cloud Spanner

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Editorial information provided by DB-Engines

Name

Description

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

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

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.

Primary database model

Key-value store
Relational DBMS

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	3.23
Rank	#81	Overall
	#10	Key-value stores
	#44	Relational DBMS

Score	0.11
Rank	#340	Overall
	#48	Document stores
	#33	Graph DBMS
	#31	Time Series DBMS

Score	2.56
Rank	#97	Overall
	#50	Relational DBMS

Website

ignite.apache.org

bangdb.com

cloud.google.com/spanner

Technical documentation

apacheignite.readme.io/docs

docs.bangdb.com

cloud.google.com/spanner/docs

Developer

Apache Software Foundation

Sachin Sinha, BangDB

Google

Initial release

2015

2012

2017

Current release

2.16.0, December 2023

BangDB 2.0, October 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD 3

commercial

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++, Java, .Net

C, C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux

hosted

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

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.

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

SQL like support with command line tool

yes

Query statements complying to ANSI 2011

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Proprietary protocol
RESTful HTTP API

gRPC (using protocol buffers) API
JDBC

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

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Java
Python

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Triggers

yes (cache interceptors and events)

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

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

Multi-source replication with 3 replicas for regional instances.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

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

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

yes, run db with in-memory only mode

User concepts

Access control

Security Hooks for custom implementations

yes (enterprise version only)

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

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More resources

Recent citations in the news

The Apache Software Foundation Announces Apache Ignite® 3.0
24 February 2025, GlobeNewswire

Critical Flaw in Apache Ignite (CVE-2024-52577) Allows Attackers to Execute Code Remotely
19 February 2025, GBHackers News

Critical Apache Ignite Vulnerability Let Attackers Execute Remote Code
19 February 2025, CybersecurityNews

Announcing the Ignite Summit 2025 Conference Schedule
20 February 2025, PR Newswire

Real-time in-memory OLTP and Analytics with Apache Ignite on AWS
14 May 2016, Amazon Web Services

provided by Google News

Google Cloud Introduces HDD Tier for Spanner Database, Cutting Cold Storage Costs by 80%
22 March 2025, infoq.com

New features make Google's Spanner a database for AI
1 August 2024, TechTarget

Google Cloud adds graph processing to Spanner, SQL support to Bigtable
1 August 2024, InfoWorld

Google Cloud adds graph and vector search features to Spanner database
2 August 2024, techzine.eu

Google’s Plan to Make GCP ‘The Best Cloud for Machine Learning’
5 May 2025, SDx Central

provided by Google News

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