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DBMS > Google Cloud Spanner vs. RRDtool vs. Sphinx vs. TDengine

System Properties Comparison Google Cloud Spanner vs. RRDtool vs. Sphinx vs. TDengine

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Editorial information provided by DB-Engines
NameGoogle Cloud Spanner  Xexclude from comparisonRRDtool  Xexclude from comparisonSphinx  Xexclude from comparisonTDengine  Xexclude from comparison
DescriptionA horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database. infoThe data is stored in a circular buffer, thus the system storage footprint remains constant over time.Open source search engine for searching in data from different sources, e.g. relational databasesTime Series DBMS and big data platform
Primary database modelRelational DBMSTime Series DBMSSearch engineTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.89
Rank#103  Overall
#52  Relational DBMS
Score1.87
Rank#136  Overall
#11  Time Series DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Score2.60
Rank#107  Overall
#8  Time Series DBMS
Websitecloud.google.com/­spanneross.oetiker.ch/­rrdtoolsphinxsearch.comgithub.com/­taosdata/­TDengine
tdengine.com
Technical documentationcloud.google.com/­spanner/­docsoss.oetiker.ch/­rrdtool/­docsphinxsearch.com/­docsdocs.tdengine.com
DeveloperGoogleTobias OetikerSphinx Technologies Inc.TDEngine, previously Taos Data
Initial release2017199920012019
Current release1.8.0, 20223.5.1, February 20233.0, August 2022
License infoCommercial or Open SourcecommercialOpen Source infoGPL V2 and FLOSSOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL V3, also commercial editions available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC infoImplementations in Java (e.g. RRD4J) and C# availableC++C
Server operating systemshostedHP-UX
Linux
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesNumeric data onlynoyes
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 infoExporting into and restoring from XML files possibleno
Secondary indexesyesnoyes infofull-text index on all search fieldsno
SQL infoSupport of SQLyes infoQuery statements complying to ANSI 2011noSQL-like query language (SphinxQL)Standard SQL with extensions for time-series applications
APIs and other access methodsgRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
in-process shared library
Pipes
Proprietary protocolJDBC
RESTful HTTP API
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
C infowith librrd library
C# infowith a different implementation of RRDTool
Java infowith a different implementation of RRDTool
JavaScript (Node.js) infowith a different implementation of RRDTool
Lua
Perl
PHP infowith a wrapper library
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust
Server-side scripts infoStored proceduresnononono
Triggersnononoyes, via alarm monitoring
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication with 3 replicas for regional instances.nonenoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistencynone
Foreign keys infoReferential integrityyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoStrict serializable isolationnono
Concurrency infoSupport for concurrent manipulation of datayesyes infoby using the rrdcached daemonyesyes
Durability infoSupport for making data persistentyesyesyes 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.noyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nonoyes
More information provided by the system vendor
Google Cloud SpannerRRDtoolSphinxTDengine
Specific characteristicsTDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more
Competitive advantagesHigh Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more
Typical application scenariosTDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more
Market metricsTDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more
Licensing and pricing modelsTDengine OSS is an open source, cloud native time series database. It includes built-in...
» more
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More resources
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