DBMS > Badger vs. BigchainDB vs. EJDB vs. Hawkular Metrics vs. Spark SQL

System Properties Comparison Badger vs. BigchainDB vs. EJDB vs. Hawkular Metrics vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

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

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/dgraph-io/badger

www.bigchaindb.com

github.com/Softmotions/ejdb

www.hawkular.org

spark.apache.org/sql

Technical documentation

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

bigchaindb.readthedocs.io/en/latest

github.com/Softmotions/ejdb/blob/master/README.md

www.hawkular.org/hawkular-metrics/docs/user-guide

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

DGraph Labs

Softmotions

Community supported by Red Hat

Apache Software Foundation

Initial release

2017

2016

2012

2014

Current release

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

AGPL v3

Open Source

GPLv2

Open Source

Apache 2.0

Open Source

Apache 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

Python

Java

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

server-less

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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 DML and DDL statements

APIs and other access methods

CLI Client
RESTful HTTP API

in-process shared library

HTTP REST

JDBC
ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Go
Java
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

based on Cassandra

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

none

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

yes

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More resources

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