DBMS > Axibase vs. Badger vs. HEAVY.AI vs. Hyprcubd vs. Microsoft Azure Table Storage

System Properties Comparison Axibase vs. Badger vs. HEAVY.AI vs. Hyprcubd vs. Microsoft Azure Table Storage

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

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

Serverless Time Series DBMS

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Website

axibase.com/docs/atsd/finance

github.com/dgraph-io/badger

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

hyprcubd.com (offline)

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

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

docs.heavy.ai

Developer

Axibase Corporation

DGraph Labs

HEAVY.AI, Inc.

Hyprcubd, Inc.

Microsoft

Initial release

2013

2017

2016

2012

Current release

15585

5.10, January 2022

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

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

Java

C++ and CUDA

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

Linux

hosted

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

time, int, uint, float, 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

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

gRPC (https)

RESTful HTTP API

Supported programming languages

Go
Java
PHP
Python
R
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Round robin

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

optimistic locking

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

token access

Access rights based on private key authentication or shared access signatures

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