DBMS > Google BigQuery vs. Hypertable vs. IRONdb vs. KairosDB vs. TimescaleDB

System Properties Comparison Google BigQuery vs. Hypertable vs. IRONdb vs. KairosDB vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

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

Description

Large scale data warehouse service with append-only tables

An open source BigTable implementation based on distributed file systems such as Hadoop

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

Distributed Time Series DBMS based on Cassandra or H2

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Relational DBMS

Wide column store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

cloud.google.com/bigquery

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

github.com/kairosdb/kairosdb

www.timescale.com

Technical documentation

cloud.google.com/bigquery/docs

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

kairosdb.github.io

docs.timescale.com

Developer

Google

Hypertable Inc.

Circonus LLC.

Timescale

Initial release

2010

2009

2017

2013

2017

Current release

0.9.8.11, March 2016

V0.10.20, January 2018

1.2.2, November 2018

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

GNU version 3. Commercial license available

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

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 and C++

Java

Server operating systems

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

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

yes

full PostgreSQL SQL syntax

APIs and other access methods

RESTful HTTP/JSON API

C++ API
Thrift

HTTP API

Graphite protocol
HTTP REST
Telnet API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
Perl
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
JavaScript

Node.js
PHP
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in Lua

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Automatic, metric affinity per node

Sharding

based on Cassandra

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

configurable replication factor, datacenter aware

selectable replication factor

based on Cassandra

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

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.

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

simple password-based access control

fine grained access rights according to SQL-standard

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