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

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

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonEsgynDB  Xexclude from comparisonGridDB  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionScalable in-memory time series database optimized for IoT and Big DataOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelRelational DBMSRelational DBMSTime Series DBMSSearch engine
Secondary database modelsDocument storeKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score14.03
Rank#40  Overall
#24  Relational DBMS
Score0.23
Rank#319  Overall
#141  Relational DBMS
Score2.02
Rank#132  Overall
#11  Time Series DBMS
Score6.03
Rank#60  Overall
#6  Search engines
Websiteimpala.apache.orgwww.esgyn.cngriddb.netsphinxsearch.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.griddb.netsphinxsearch.com/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaEsgynToshiba CorporationSphinx Technologies Inc.
Initial release2013201520132001
Current release4.1.0, June 20225.1, August 20223.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++, JavaC++C++
Server operating systemsLinuxLinuxLinuxFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyes infonumerical, string, blob, geometry, boolean, timestampno
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.nonono
Secondary indexesyesyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSQL92, SQL-like TQL (Toshiba Query Language)SQL-like query language (SphinxQL)
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
ODBC
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Proprietary protocol
Supported programming languagesAll languages supporting JDBC/ODBCAll languages supporting JDBC/ODBC/ADO.NetC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceJava Stored Proceduresnono
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication between multi datacentersSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate consistency within container, eventual consistency across containers
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID at container levelno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosfine grained access rights according to SQL-standardAccess rights for users can be defined per databaseno
More information provided by the system vendor
Apache ImpalaEsgynDBGridDBSphinx
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Apache ImpalaEsgynDBGridDBSphinx
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