DBMS > Apache Drill vs. Badger vs. LMDB vs. Oracle NoSQL

System Properties Comparison Apache Drill vs. Badger vs. LMDB vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

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

A high performant, light-weight, embedded key-value database library

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Document store
Relational DBMS

Key-value store

Document store
Key-value store
Relational DBMS

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

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

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

Score	2.09
Rank	#121	Overall
	#20	Key-value stores

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Website

drill.apache.org

github.com/dgraph-io/badger

www.symas.com/symas-embedded-database-lmdb

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

drill.apache.org/docs

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

www.lmdb.tech/doc

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Apache Software Foundation

DGraph Labs

Symas

Oracle

Initial release

2012

2017

2011

Current release

1.20.3, January 2023

0.9.32, January 2024

24.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
OS X
Windows

BSD
Linux
OS X
Solaris
Windows

Linux
Unix
Windows

Linux
Solaris SPARC/x86

Data scheme

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

optional

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

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

Supported programming languages

C++

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C
C#
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

off heap cache

User concepts

Access control

Depending on the underlying data source

Access rights for users and roles

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