DBMS > Google BigQuery vs. Ignite vs. IRONdb vs. MySQL vs. Splice Machine

System Properties Comparison Google BigQuery vs. Ignite vs. IRONdb vs. MySQL vs. Splice Machine

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Large scale data warehouse service with append-only tables

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.

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Widely used open source RDBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

cloud.google.com/bigquery

ignite.apache.org

www.circonus.com/solutions/time-series-database/

www.mysql.com

splicemachine.com

Technical documentation

cloud.google.com/bigquery/docs

apacheignite.readme.io/docs

docs.circonus.com/irondb/category/getting-started

dev.mysql.com/doc

splicemachine.com/how-it-works

Developer

Google

Apache Software Foundation

Circonus LLC.

Oracle

since 2010, originally MySQL AB, then Sun

Splice Machine

Initial release

2010

2015

2017

1995

2014

Current release

Apache Ignite 2.6

V0.10.20, January 2018

8.4.0, April 2024

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

C++, Java, .Net

C and C++

Java

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL

Support of SQL

yes

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

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

with proprietary extensions

yes

APIs and other access methods

RESTful HTTP/JSON API

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

HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

yes, in Lua

yes

proprietary syntax

yes

Java

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Automatic, metric affinity per node

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

configurable replication factor, datacenter aware

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

Users with fine-grained authorization concept

no user groups or roles

Access rights for users, groups and roles according to SQL-standard

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