DBMS > Google BigQuery vs. IRONdb vs. NSDb vs. SwayDB vs. Transbase

System Properties Comparison Google BigQuery vs. IRONdb vs. NSDb vs. SwayDB vs. Transbase

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

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

cloud.google.com/bigquery

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

nsdb.io

swaydb.simer.au

www.transaction.de/en/products/transbase.html

Technical documentation

cloud.google.com/bigquery/docs

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

nsdb.io/Architecture

www.transaction.de/en/products/transbase/features.html

Developer

Google

Circonus LLC.

Simer Plaha

Transaction Software GmbH

Initial release

2010

2017

2018

1987

Current release

V0.10.20, January 2018

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GNU Affero GPL V3.0

commercial

free development license

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

Java, Scala

Scala

C and C++

Server operating systems

hosted

Linux

Linux
macOS

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

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

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

HTTP API

gRPC
HTTP REST
WebSocket

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java
Scala

Java
Kotlin
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

Atomic execution of operations

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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)

fine grained access rights according to SQL-standard

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