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DBMS > Apache IoTDB vs. BigObject vs. Google Cloud Firestore vs. Sphinx

System Properties Comparison Apache IoTDB vs. BigObject vs. Google Cloud Firestore vs. Sphinx

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonBigObject  Xexclude from comparisonGoogle Cloud Firestore  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkAnalytic DBMS for real-time computations and queriesCloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.Open source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSRelational DBMS infoa hierachical model (tree) can be imposedDocument storeSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.19
Rank#329  Overall
#146  Relational DBMS
Score7.36
Rank#53  Overall
#9  Document stores
Score5.95
Rank#55  Overall
#5  Search engines
Websiteiotdb.apache.orgbigobject.iofirebase.google.com/­products/­firestoresphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.bigobject.iofirebase.google.com/­docs/­firestoresphinxsearch.com/­docs
DeveloperApache Software FoundationBigObject, Inc.GoogleSphinx Technologies Inc.
Initial release2018201520172001
Current release1.1.0, April 20233.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infofree community edition availablecommercialOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++
Server operating systemsAll OS with a Java VM (>= 1.8)Linux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
hostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesno
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.nonono
Secondary indexesyesyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languageSQL-like DML and DDL statementsnoSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
Native API
fluentd
ODBC
RESTful HTTP API
Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
Proprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyesLuayes, Firebase Rules & Cloud Functionsno
Triggersyesnoyes, with Cloud Functionsno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasnoneMulti-source replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparknoUsing Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
noneImmediate Consistency
Foreign keys infoReferential integritynoyes infoautomatically between fact table and dimension tablesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoyesno
Concurrency infoSupport for concurrent manipulation of datayesyes infoRead/write lock on objects (tables, trees)yesyes
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.yesyes
User concepts infoAccess controlyesnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.no

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More resources
Apache IoTDBBigObjectGoogle Cloud FirestoreSphinx
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