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DBMS > GridGain vs. Sphinx vs. SWC-DB vs. Vertica

System Properties Comparison GridGain vs. Sphinx vs. SWC-DB vs. Vertica

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Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonSphinx  Xexclude from comparisonSWC-DB infoSuper Wide Column Database  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteOpen source search engine for searching in data from different sources, e.g. relational databasesA high performance, scalable Wide Column DBMSCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelKey-value store
Relational DBMS
Search engineWide column storeRelational DBMS infoColumn oriented
Secondary database modelsTime Series DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.55
Rank#150  Overall
#26  Key-value stores
#70  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Score0.08
Rank#364  Overall
#13  Wide column stores
Score10.06
Rank#42  Overall
#26  Relational DBMS
Websitewww.gridgain.comsphinxsearch.comgithub.com/­kashirin-alex/­swc-db
www.swcdb.org
www.vertica.com
Technical documentationwww.gridgain.com/­docs/­index.htmlsphinxsearch.com/­docsvertica.com/­documentation
DeveloperGridGain Systems, Inc.Sphinx Technologies Inc.Alex KashirinOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2007200120202005
Current releaseGridGain 8.5.13.5.1, February 20230.5, April 202112.0.3, January 2023
License infoCommercial or Open SourcecommercialOpen Source infoGPL version 2, commercial licence availableOpen Source infoGPL V3commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, C++, .NetC++C++C++
Server operating systemsLinux
OS X
Solaris
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
LinuxLinux
Data schemeyesyesschema-freeYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesnoyes
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.yesnono
Secondary indexesyesyes infofull-text index on all search fieldsNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLSQL-like query language (SphinxQL)SQL-like query languageFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Proprietary protocolProprietary protocol
Thrift
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C++C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)nonoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyes (cache interceptors and events)nonoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)noneMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)nonono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infoThe original contents of fields are not stored in the Sphinx index.yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlSecurity Hooks for custom implementationsnofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
GridGainSphinxSWC-DB infoSuper Wide Column DatabaseVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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GridGainSphinxSWC-DB infoSuper Wide Column DatabaseVertica infoOpenText™ Vertica™
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