DBMS > Amazon Aurora vs. Apache Impala vs. Drizzle vs. OpenSearch vs. QuestDB

System Properties Comparison Amazon Aurora vs. Apache Impala vs. Drizzle vs. OpenSearch vs. QuestDB

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

MySQL and PostgreSQL compatible cloud service by Amazon

Analytic DBMS for Hadoop

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

A distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene

A high performance open source SQL database for time series data

Primary database model

Relational DBMS

Search engine

Time Series DBMS

Secondary database models

Document store

Vector DBMS

Relational DBMS

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	16.36
Rank	#37	Overall
	#4	Search engines

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Website

aws.amazon.com/rds/aurora

impala.apache.org

github.com/opensearch-project
opensearch.org

questdb.io

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

impala.apache.org/impala-docs.html

opensearch.org/docs/latest

questdb.io/docs

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Drizzle project, originally started by Brian Aker

Amazon Web Services

QuestDB Technology Inc

Initial release

2015

2013

2008

2021

2014

Current release

4.1.0, June 2022

7.2.4, September 2012

2.5.0, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GNU GPL

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

C++

Java

Java (Zero-GC), C++, Rust

Server operating systems

hosted

Linux

FreeBSD
Linux
OS X

All OS with a Java VM

Linux
macOS
Windows

Data scheme

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

schema-free via InfluxDB Line Protocol

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

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

with proprietary extensions

SQL-like query language

SQL with time-series extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

JDBC

Java API
RESTful HTTP/JSON API

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC

C
C++
Java
PHP

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

hooks for callbacks inside the server can be used.

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning (by timestamps)

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Multi-source replication
Source-replica replication

yes

Source-replica replication with eventual consistency

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

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID for single-table writes

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.

yes

through memory mapped files

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

More information provided by the system vendor

Specific characteristics

OpenSearch is a highly scalable and extensible open-source software suite for search,...
» more

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

Distributed as fully open source under Apache License, Version 2.0 (ALv2), OpenSearch...
» more

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

The OpenSearch platform is used across a broad range of search , analytics, and observability...
» more

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

The OpenSearch Project offers open source software licensed under Apache License...
» more

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.