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DBMS > Coveo vs. GeoMesa vs. Spark SQL vs. Tigris

System Properties Comparison Coveo vs. GeoMesa vs. Spark SQL vs. Tigris

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Editorial information provided by DB-Engines
NameCoveo  Xexclude from comparisonGeoMesa  Xexclude from comparisonSpark SQL  Xexclude from comparisonTigris  Xexclude from comparison
DescriptionAI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code developmentGeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.Spark SQL is a component on top of 'Spark Core' for structured data processingA horizontally scalable, ACID transactional, document database available both as a fully managed cloud service and for deployment on self-managed infrastructure
Primary database modelSearch engineSpatial DBMSRelational DBMSDocument store
Key-value store
Search engine
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.11
Rank#118  Overall
#11  Search engines
Score0.86
Rank#205  Overall
#4  Spatial DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.09
Rank#363  Overall
#49  Document stores
#54  Key-value stores
#22  Search engines
#38  Time Series DBMS
Websitewww.coveo.comwww.geomesa.orgspark.apache.org/­sqlwww.tigrisdata.com
Technical documentationdocs.coveo.comwww.geomesa.org/­documentation/­stable/­user/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.tigrisdata.com/­docs
DeveloperCoveoCCRi and othersApache Software FoundationTigris Data, Inc.
Initial release2012201420142022
Current release5.0.0, May 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache License 2.0Open Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaScala
Server operating systemshostedLinux
OS X
Windows
Linux
macOS
Windows
Data schemehybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configurationyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nononono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLnonoSQL-like DML and DDL statementsno
APIs and other access methodsRESTful HTTP APIJDBC
ODBC
CLI Client
gRPC
RESTful HTTP API
Supported programming languagesC#
Java
JavaScript
Python
Java
Python
R
Scala
Go
Java
JavaScript (Node.js)
Server-side scripts infoStored proceduresnononono
Triggersyesnonono
Partitioning methods infoMethods for storing different data on different nodesyesdepending on storage layeryes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesdepending on storage layernoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistencydepending on storage layerImmediate Consistency
Foreign keys infoReferential integrityyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes, using FoundationDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.depending on storage layerno
User concepts infoAccess controlgranular access controls, API key management, content filtersyes infodepending on the DBMS used for storagenoAccess rights for users and roles

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More resources
CoveoGeoMesaSpark SQLTigris
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