DBMS > Axibase vs. Badger vs. etcd vs. HEAVY.AI vs. NSDb

System Properties Comparison Axibase vs. Badger vs. etcd vs. HEAVY.AI vs. NSDb

Editorial information provided by DB-Engines

Name

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

axibase.com/docs/atsd/finance

github.com/dgraph-io/badger

etcd.io
github.com/etcd-io/etcd

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

nsdb.io

Technical documentation

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

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

docs.heavy.ai

nsdb.io/Architecture

Developer

Axibase Corporation

DGraph Labs

HEAVY.AI, Inc.

Initial release

2013

2017

2016

2017

Current release

15585

3.4, August 2019

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 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2.0

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

Java

C++ and CUDA

Java, Scala

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
Windows

experimental

Linux

Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

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

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

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

gRPC
JSON over HTTP

JDBC
ODBC
Thrift
Vega

gRPC
HTTP REST
WebSocket

Supported programming languages

Go
Java
PHP
Python
R
Ruby

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

All languages supporting JDBC/ODBC/Thrift
Python

Java
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication

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

Foreign keys

Referential integrity

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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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