DBMS > Apache Impala vs. LeanXcale vs. Memcached vs. Snowflake

System Properties Comparison Apache Impala vs. LeanXcale vs. Memcached vs. Snowflake

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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

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

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Key-value store

Relational DBMS

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	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

github.com/memcached/memcached/wiki

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

LeanXcale

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Snowflake Computing Inc.

Initial release

2013

2015

2003

2014

Current release

4.1.0, June 2022

1.6.25, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

BSD license

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

Linux

FreeBSD
Linux
OS X
Unix
Windows

hosted

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes

APIs and other access methods

JDBC
ODBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Proprietary protocol

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
Java
Scala

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Repcached, a Memcached patch, provides this functionallity

yes

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

Transaction concepts

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

ACID

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

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, user roles and pluggable authentication

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