DBMS > Badger vs. HEAVY.AI vs. IRONdb vs. Microsoft Azure Cosmos DB vs. Teradata

System Properties Comparison Badger vs. HEAVY.AI vs. IRONdb vs. Microsoft Azure Cosmos DB vs. Teradata

Editorial information provided by DB-Engines

Name

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

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

Globally distributed, horizontally scalable, multi-model database service

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Key-value store

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

github.com/dgraph-io/badger

github.com/heavyai/heavydb
www.heavy.ai

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

azure.microsoft.com/services/cosmos-db

www.teradata.com

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.heavy.ai

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

learn.microsoft.com/azure/cosmos-db

docs.teradata.com

Developer

DGraph Labs

HEAVY.AI, Inc.

Circonus LLC.

Microsoft

Teradata

Initial release

2017

2016

2017

2014

1984

Current release

5.10, January 2022

V0.10.20, January 2018

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

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++ and CUDA

C and C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

hosted

hosted
Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

JSON types

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

All properties auto-indexed by default

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

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

SQL-like query language

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
ODBC
Thrift
Vega

HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes, in Lua

JavaScript

yes

UDFs, stored procedures, table functions in parallel

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Automatic, metric affinity per node

Sharding

Implicit feature of the cloud service

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

configurable replication factor, datacenter aware

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.