DBMS > Apache Impala vs. LeanXcale vs. Lovefield vs. Memcached vs. MySQL

System Properties Comparison Apache Impala vs. LeanXcale vs. Lovefield vs. Memcached vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Embeddable relational database for web apps written in pure JavaScript

In-memory key-value store, originally intended for caching

Widely used open source RDBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store

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	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	18.08
Rank	#32	Overall
	#4	Key-value stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

impala.apache.org

www.leanxcale.com

google.github.io/lovefield

www.memcached.org

www.mysql.com

Technical documentation

impala.apache.org/impala-docs.html

github.com/google/lovefield/blob/master/docs/spec_index.md

github.com/memcached/memcached/wiki

dev.mysql.com/doc

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

LeanXcale

Google

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2013

2015

2014

2003

1995

Current release

4.1.0, June 2022

2.1.12, February 2017

1.6.27, May 2024

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

Open Source

BSD license

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

JavaScript

C and C++

Server operating systems

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
OS X
Unix
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

through Apache Derby

SQL-like query language

via JavaScript builder pattern

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Proprietary protocol

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

All languages supporting JDBC/ODBC

C
Java
Scala

JavaScript

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

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

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Repcached, a Memcached patch, provides this functionallity

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

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

using SASL (Simple Authentication and Security Layer) protocol

Users with fine-grained authorization concept

no user groups or roles

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