DBMS > Apache Druid vs. Apache Impala vs. Apache Kylin vs. LevelDB

System Properties Comparison Apache Druid vs. Apache Impala vs. Apache Kylin vs. LevelDB

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Analytic DBMS for Hadoop

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

Secondary database models

Document store

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Website

druid.apache.org

impala.apache.org

kylin.apache.org

github.com/google/leveldb

Technical documentation

druid.apache.org/docs/latest/design

impala.apache.org/impala-docs.html

kylin.apache.org/docs

github.com/google/leveldb/blob/main/doc/index.md

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation, originally contributed from eBay Inc

Google

Initial release

2012

2013

2015

2011

Current release

29.0.1, April 2024

4.1.0, June 2022

3.1.0, July 2020

1.23, February 2021

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Java

C++

Server operating systems

Linux
OS X
Unix

Linux

Illumos
Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

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

SQL for querying

SQL-like DML and DDL statements

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

In-memory capabilities

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

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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