DBMS > Badger vs. Brytlyt vs. HEAVY.AI vs. IRONdb

System Properties Comparison Badger vs. Brytlyt vs. HEAVY.AI vs. IRONdb

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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.

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

Primary database model

Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	0.38
Rank	#279	Overall
	#126	Relational DBMS

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Website

github.com/dgraph-io/badger

brytlyt.io

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

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

Technical documentation

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

docs.brytlyt.io

docs.heavy.ai

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

Developer

DGraph Labs

Brytlyt

HEAVY.AI, Inc.

Circonus LLC.

Initial release

2017

2016

2017

Current release

5.0, August 2023

5.10, January 2022

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++ and CUDA

C++ and CUDA

C and C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

Data scheme

schema-free

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

yes

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

APIs and other access methods

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

JDBC
ODBC
Thrift
Vega

HTTP API

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

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

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

yes, in Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Automatic, metric affinity per node

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Multi-source replication

configurable replication factor, datacenter aware

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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More resources

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