DBMS > Apache Impala vs. Drizzle vs. HyperSQL vs. KeyDB

System Properties Comparison Apache Impala vs. Drizzle vs. HyperSQL vs. KeyDB

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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

Analytic DBMS for Hadoop

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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Primary database model

Relational DBMS

Key-value store

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	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Website

impala.apache.org

hsqldb.org

github.com/Snapchat/KeyDB
keydb.dev

Technical documentation

impala.apache.org/impala-docs.html

hsqldb.org/web/hsqlDocsFrame.html

docs.keydb.dev

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Drizzle project, originally started by Brian Aker

EQ Alpha Technology Ltd.

Initial release

2013

2008

2001

2019

Current release

4.1.0, June 2022

7.2.4, September 2012

2.7.2, June 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GNU GPL

Open Source

based on BSD license

Open Source

BSD-3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

Linux

FreeBSD
Linux
OS X

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC

JDBC

HTTP API

JDBC via HTTP
JDBC
ODBC

Proprietary protocol

RESP - REdis Serialization Protoco

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

All languages supporting JDBC/ODBC
Java

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java, SQL

Lua

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

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

Eventual Consistency

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Optimistic locking, atomic execution of commands blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

simple password-based access control and ACL

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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Recent citations in the news

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

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HyperSQL DataBase flaw leaves library vulnerable to RCE
24 October 2022, The Daily Swig

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14 November 2022, TheServerSide.com

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12 May 2022, TechCrunch

Snap Acquires KeyDB for Open-Source Services
17 May 2022, XR Today

Garnet–open-source faster cache-store speeds up applications, services
18 March 2024, microsoft.com

Dragonfly 1.0 Released For What Claims To Be The World's Fastest In-Memory Data Store
20 March 2023, Phoronix

Microsoft open-sources Garnet cache-store -- a Redis rival?
19 March 2024, The Stack

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