DBMS > Google BigQuery vs. Hypertable vs. Immudb vs. IRONdb

System Properties Comparison Google BigQuery vs. Hypertable vs. Immudb vs. IRONdb

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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

An open source immutable (append-only) database with cryptographic verification which makes it tamper-resistant and fully auditable.

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

Primary database model

Secondary database models

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.24
Rank	#305	Overall
	#43	Key-value stores

Website

cloud.google.com/bigquery

github.com/codenotary/immudb
immudb.io

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

Technical documentation

cloud.google.com/bigquery/docs

docs.immudb.io

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

Developer

Google

Hypertable Inc.

Codenotary

Circonus LLC.

Initial release

2010

2009

2020

2017

Current release

0.9.8.11, March 2016

1.2.3, April 2022

V0.10.20, January 2018

License

Commercial or Open Source

commercial

Open Source

GNU version 3. Commercial license available

Open Source

Apache Version 2.0

commercial

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++

C and C++

Server operating systems

hosted

Linux
OS X
Windows

an inofficial Windows port is available

BSD
Linux
macOS
Solaris
Windows
z/OS

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

SQL-like syntax

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

APIs and other access methods

RESTful HTTP/JSON API

C++ API
Thrift

gRPC protocol
PostgreSQL wire protocol

HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Automatic, metric affinity per node

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

configurable replication factor, datacenter aware

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

Foreign keys

Referential integrity

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)

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