DBMS > AgensGraph vs. Apache Impala vs. Badger vs. Drizzle

System Properties Comparison AgensGraph vs. Apache Impala vs. Badger vs. Drizzle

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Analytic DBMS for Hadoop

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

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

Website

bitnine.net/agensgraph

impala.apache.org

github.com/dgraph-io/badger

Technical documentation

bitnine.net/documentation

impala.apache.org/impala-docs.html

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

Developer

Bitnine Global Inc.

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DGraph Labs

Drizzle project, originally started by Brian Aker

Initial release

2016

2013

2017

2008

Current release

2.1, December 2018

4.1.0, June 2022

7.2.4, September 2012

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

GNU GPL

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++

Server operating systems

Linux
OS X
Windows

Linux

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X

Data scheme

depending on used data model

yes

schema-free

yes

Typing

predefined data types such as float or date

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

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

Cypher Query Language
JDBC

JDBC
ODBC

JDBC

Supported programming languages

C
Java
JavaScript
Python

All languages supporting JDBC/ODBC

C
C++
Java
PHP

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

none

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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