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DBMS > Impala vs. Sphinx vs. Yanza

System Properties Comparison Impala vs. Sphinx vs. Yanza

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Editorial information provided by DB-Engines
NameImpala  Xexclude from comparisonSphinx  Xexclude from comparisonYanza  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopOpen source search engine for searching in data from different sources, e.g. relational databasesTime Series DBMS for IoT Applications
Primary database modelRelational DBMSSearch engineTime Series DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score18.71
Rank#38  Overall
#23  Relational DBMS
Score8.01
Rank#56  Overall
#6  Search engines
Score0.00
Rank#360  Overall
#37  Time Series DBMS
Websitewww.cloudera.com/­products/­open-source/­apache-hadoop/­impala.htmlsphinxsearch.comyanza.com
Technical documentationdocs.cloudera.com/­documentation/­enterprise/­latest/­topics/­impala.htmlsphinxsearch.com/­docs
DeveloperClouderaSphinx Technologies Inc.Yanza
Initial release201320012015
Current release4.0.0, July 20213.4.1, July 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoGPL version 2, commercial licence availablecommercial infofree version available
Cloud-based only infoOnly available as a cloud servicenonono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C++
Server operating systemsLinuxFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Windows
Data schemeyesyesschema-free
Typing infopredefined data types such as float or dateyesnono
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono
Secondary indexesyesyes infofull-text index on all search fieldsno
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query language (SphinxQL)no
APIs and other access methodsJDBC
ODBC
Proprietary protocolHTTP API
Supported programming languagesAll languages supporting JDBC/ODBCC++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
any language that supports HTTP calls
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenono
Triggersnonoyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoPartitioning is done manually, search queries against distributed index is supportednone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.no
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosnono

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More resources
ImpalaSphinxYanza
DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

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Recent citations in the news

Cloudera Boosts Hadoop App Development On Impala
10 November 2014, InformationWeek

Cloudera’s Impala brings Hadoop to SQL and BI
25 October 2012, ZDNet

Man Busts Out of Google, Rebuilds Top-Secret Query Machine
24 October 2012, Wired

Cloudera aims to bring real-time queries to Hadoop, big data
24 October 2012, ZDNet

Unravel Data Adds Native Support for Impala and Kafka
29 June 2017, insideBIGDATA

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