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DBMS > Elasticsearch vs. Sphinx vs. Vertica

System Properties Comparison Elasticsearch vs. Sphinx vs. Vertica

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Editorial information provided by DB-Engines
NameElasticsearch  Xexclude from comparisonSphinx  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionA distributed, RESTful modern search and analytics engine based on Apache Lucene infoElasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metricOpen source search engine for searching in data from different sources, e.g. relational databasesCloud 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 modelSearch engineSearch engineRelational DBMS infoColumn oriented
Secondary database modelsDocument store
Spatial DBMS
Vector DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score134.79
Rank#7  Overall
#1  Search engines
Score6.06
Rank#61  Overall
#6  Search engines
Score11.76
Rank#44  Overall
#28  Relational DBMS
Websitewww.elastic.co/­elasticsearchsphinxsearch.comwww.vertica.com
Technical documentationwww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.htmlsphinxsearch.com/­docsvertica.com/­documentation
DeveloperElasticSphinx Technologies Inc.OpenText infopreviously Micro Focus and Hewlett Packard
Initial release201020012005
Current release8.6, January 20233.5.1, February 202312.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoElastic LicenseOpen Source infoGPL version 2, commercial licence availablecommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono 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 languageJavaC++C++
Server operating systemsAll OS with a Java VMFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
Data schemeschema-free infoFlexible type definitions. Once a type is defined, it is persistentyesYes, 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.nono
Secondary indexesyes infoAll search fields are automatically indexedyes infofull-text index on all search fieldsNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL-like query languageSQL-like query language (SphinxQL)Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJava API
RESTful HTTP/JSON API
Proprietary protocolADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languages.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyesnoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyes infoby using the 'percolation' featurenoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedhorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesnoneMulti-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 methodsES-Hadoop Connectornono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infoSynchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, allImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
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.Memcached and Redis integrationno
User concepts infoAccess controlnofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
ElasticsearchSphinxVertica 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,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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More resources
ElasticsearchSphinxVertica infoOpenText™ Vertica™
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