DBMS > Apache Impala vs. Graph Engine vs. LeanXcale vs. Microsoft Azure Table Storage

System Properties Comparison Apache Impala vs. Graph Engine vs. LeanXcale vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Key-value store
Relational DBMS

Wide column store

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

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

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Website

impala.apache.org

www.graphengine.io

www.leanxcale.com

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

impala.apache.org/impala-docs.html

www.graphengine.io/docs/manual

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Microsoft

LeanXcale

Microsoft

Initial release

2013

2010

2015

2012

Current release

4.1.0, June 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

MIT License

commercial

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.

Implementation language

C++

.NET and C

Server operating systems

Linux

.NET

hosted

Data scheme

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-like DML and DDL statements

yes

through Apache Derby

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
F#
Visual Basic

C
Java
Scala

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

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

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Access rights based on private key authentication or shared access signatures

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More resources

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