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DBMS > Coveo vs. Google Cloud Bigtable vs. Google Cloud Datastore vs. Tkrzw

System Properties Comparison Coveo vs. Google Cloud Bigtable vs. Google Cloud Datastore vs. Tkrzw

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Editorial information provided by DB-Engines
NameCoveo  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionAI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code developmentGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelSearch engineKey-value store
Wide column store
Document storeKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.14
Rank#116  Overall
#11  Search engines
Score2.97
Rank#92  Overall
#15  Key-value stores
#8  Wide column stores
Score4.13
Rank#71  Overall
#12  Document stores
Score0.00
Rank#385  Overall
#61  Key-value stores
Websitewww.coveo.comcloud.google.com/­bigtablecloud.google.com/­datastoredbmx.net/­tkrzw
Technical documentationdocs.coveo.comcloud.google.com/­bigtable/­docscloud.google.com/­datastore/­docs
DeveloperCoveoGoogleGoogleMikio Hirabayashi
Initial release2012201520082020
Current release0.9.3, August 2020
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++
Server operating systemshostedhostedhostedLinux
macOS
Data schemehybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configurationschema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateyesnoyes, details hereno
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 indexesyesnoyes
SQL infoSupport of SQLnonoSQL-like query language (GQL)no
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Supported programming languagesC#
Java
JavaScript
Python
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresnonousing Google App Engineno
TriggersyesnoCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodesyesShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesInternal replication in Colossus, and regional replication between two clusters in different zonesMulti-source replication using Paxosnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate Consistency
Foreign keys infoReferential integrityyesnoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesAtomic single-row operationsACID infoSerializable Isolation within Transactions, Read Committed outside of Transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infousing specific database classes
User concepts infoAccess controlgranular access controls, API key management, content filtersAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)no

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More resources
CoveoGoogle Cloud BigtableGoogle Cloud DatastoreTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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