DBMS > Apache Impala vs. EsgynDB vs. Sphinx vs. TempoIQ

System Properties Comparison Apache Impala vs. EsgynDB vs. Sphinx vs. TempoIQ

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Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Open source search engine for searching in data from different sources, e.g. relational databases

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Primary database model

Relational DBMS

Search engine

Time Series DBMS

Secondary database models

Document store

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

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

impala.apache.org

www.esgyn.cn

sphinxsearch.com

tempoiq.com (offline)

Technical documentation

impala.apache.org/impala-docs.html

sphinxsearch.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Esgyn

Sphinx Technologies Inc.

TempoIQ

Initial release

2013

2015

2001

2012

Current release

4.1.0, June 2022

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GPL version 2, commercial licence available

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++

C++, Java

C++

Server operating systems

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

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

full-text index on all search fields

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

Proprietary protocol

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/ADO.Net

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

Triggers

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication between multi datacenters

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

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

The original contents of fields are not stored in the Sphinx index.

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

simple authentication-based access control

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More resources

DB-Engines blog posts

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