DBMS > Apache Impala vs. Coveo vs. Elasticsearch vs. LevelDB vs. MySQL

System Properties Comparison Apache Impala vs. Coveo vs. Elasticsearch vs. LevelDB vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

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

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Document store

Document store
Spatial DBMS
Vector DBMS

Document store
Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	2.11
Rank	#118	Overall
	#11	Search engines

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

impala.apache.org

www.coveo.com

www.elastic.co/elasticsearch

github.com/google/leveldb

www.mysql.com

Technical documentation

impala.apache.org/impala-docs.html

docs.coveo.com

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

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

dev.mysql.com/doc

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Coveo

Elastic

Google

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2013

2012

2010

2011

1995

Current release

4.1.0, June 2022

8.6, January 2023

1.23, February 2021

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Elastic License

Open Source

BSD

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Java

C++

C and C++

Server operating systems

Linux

hosted

All OS with a Java VM

Illumos
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

schema-free

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

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

All languages supporting JDBC/ODBC

C#
Java
JavaScript
Python

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

Triggers

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

not for MyISAM storage engine

Transaction concepts

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

yes

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

Memcached and Redis integration

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

granular access controls, API key management, content filters

Users with fine-grained authorization concept

no user groups or roles

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