DBMS > Apache Impala vs. Brytlyt vs. Drizzle vs. JaguarDB vs. ReductStore

System Properties Comparison Apache Impala vs. Brytlyt vs. Drizzle vs. JaguarDB vs. ReductStore

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

Analytic DBMS for Hadoop

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

Performant, highly scalable DBMS for AI and IoT applications

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Time Series DBMS

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

impala.apache.org

brytlyt.io

www.jaguardb.com

github.com/reductstore
www.reduct.store

Technical documentation

impala.apache.org/impala-docs.html

docs.brytlyt.io

www.jaguardb.com/support.html

www.reduct.store/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Brytlyt

Drizzle project, originally started by Brian Aker

DataJaguar, Inc.

ReductStore LLC

Initial release

2013

2016

2008

2015

2023

Current release

4.1.0, June 2022

5.0, August 2023

7.2.4, September 2012

3.3 July 2023

1.9, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GNU GPL

Open Source

GPL V3.0

Open Source

Business Source License 1.1

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C, C++ and CUDA

C++

the server part. Clients available in other languages

C++, Rust

Server operating systems

Linux

Linux
OS X
Windows

FreeBSD
Linux
OS X

Linux

Docker
Linux
macOS
Windows

Data scheme

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC

JDBC
ODBC

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C
C++
Java
PHP

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in PL/pgSQL

Triggers

yes

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

Multi-source replication
Source-replica replication

Multi-source 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

Eventual Consistency

Immediate Consistency

Eventual Consistency

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

rights management via user accounts

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More resources

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Recent citations in the news

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Apache Impala becomes Top-Level Project
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28 November 2017, Datanami

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22 December 2021, Silicon Canals

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31 January 2023, PR Newswire

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