DBMS > Apache Drill vs. Badger vs. EJDB vs. Google Cloud Bigtable

System Properties Comparison Apache Drill vs. Badger vs. EJDB vs. Google Cloud Bigtable

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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.

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Primary database model

Document store
Relational DBMS

Key-value store

Document store

Key-value store
Wide column store

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Website

drill.apache.org

github.com/dgraph-io/badger

github.com/Softmotions/ejdb

cloud.google.com/bigtable

Technical documentation

drill.apache.org/docs

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

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

cloud.google.com/bigtable/docs

Developer

Apache Software Foundation

DGraph Labs

Softmotions

Google

Initial release

2012

2017

2012

2015

Current release

1.20.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

GPLv2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Server operating systems

Linux
OS X
Windows

BSD
Linux
OS X
Solaris
Windows

server-less

hosted

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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 SELECT statement is SQL:2003 compliant

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

in-process shared library

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Supported programming languages

C++

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

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

Internal replication in Colossus, and regional replication between two clusters in different zones

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

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

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

User concepts

Access control

Depending on the underlying data source

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources

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