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DBMS > Google Cloud Bigtable vs. GreptimeDB vs. Solr vs. Sphinx

System Properties Comparison Google Cloud Bigtable vs. GreptimeDB vs. Solr vs. Sphinx

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonGreptimeDB  Xexclude from comparisonSolr  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.An open source Time Series DBMS built for increased scalability, high performance and efficiencyA widely used distributed, scalable search engine based on Apache LuceneOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelKey-value store
Wide column store
Time Series DBMSSearch engineSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score0.12
Rank#351  Overall
#34  Time Series DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Score5.95
Rank#55  Overall
#5  Search engines
Websitecloud.google.com/­bigtablegreptime.comsolr.apache.orgsphinxsearch.com
Technical documentationcloud.google.com/­bigtable/­docsdocs.greptime.comsolr.apache.org/­resources.htmlsphinxsearch.com/­docs
DeveloperGoogleGreptime Inc.Apache Software FoundationSphinx Technologies Inc.
Initial release2015202220062001
Current release9.6.1, May 20243.5.1, February 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoApache Version 2Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageRustJavaC++
Server operating systemshostedAndroid
Docker
FreeBSD
Linux
macOS
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-freeschema-free, schema definition possibleyes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or datenoyesyes infosupports customizable data types and automatic typingno
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.nonoyes
Secondary indexesnoyesyes infoAll search fields are automatically indexedyes infofull-text index on all search fields
SQL infoSupport of SQLnoyesSolr Parallel SQL InterfaceSQL-like query language (SphinxQL)
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
gRPC
HTTP API
JDBC
Java API
RESTful HTTP/JSON API
Proprietary protocol
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Erlang
Go
Java
JavaScript
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnoPythonJava pluginsno
Triggersnoyes infoUser configurable commands triggered on index changesno
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 nodesInternal replication in Colossus, and regional replication between two clusters in different zonesyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsoptimistic lockingno
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.noyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Simple rights management via user accountsyesno
More information provided by the system vendor
Google Cloud BigtableGreptimeDBSolrSphinx
Specific characteristicsGreptimeDB is a SQL & Python-enabled timeseries database system built from scratch...
» more
Competitive advantages- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
» more
Typical application scenariosFor IoT industries, GreptimeDB can seamless integrate with message queues and other...
» more
Key customersGreptime's clients span multiple sectors including IoT, connected vehicles, and energy...
» more
Market metricsGreptimeDB has garnered global recognition by topping GitHub trends following its...
» more
Licensing and pricing modelsGreptimeDB: open source, distributed, cloud-native TSDB; supports Hybrid Time-series...
» more

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Google Cloud BigtableGreptimeDBSolrSphinx
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