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DBMS > Apache IoTDB vs. Cubrid vs. Google Cloud Bigtable vs. Google Cloud Spanner vs. Sphinx

System Properties Comparison Apache IoTDB vs. Cubrid vs. Google Cloud Bigtable vs. Google Cloud Spanner vs. Sphinx

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonCubrid  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGoogle Cloud Spanner  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 FlinkCUBRID is an open-source SQL-based relational database management system with object extensions for OLTPGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A 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.Open source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSRelational DBMSKey-value store
Wide column store
Relational DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1.20
Rank#169  Overall
#78  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score2.89
Rank#103  Overall
#52  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websiteiotdb.apache.orgcubrid.com (korean)
cubrid.org (english)
cloud.google.com/­bigtablecloud.google.com/­spannersphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcubrid.org/­manualscloud.google.com/­bigtable/­docscloud.google.com/­spanner/­docssphinxsearch.com/­docs
DeveloperApache Software FoundationCUBRID Corporation, CUBRID FoundationGoogleGoogleSphinx Technologies Inc.
Initial release20182008201520172001
Current release1.1.0, April 202311.0, January 20213.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0commercialcommercialOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenonoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++, JavaC++
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
Windows
hostedhostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesnoyesno
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 indexesyesyesnoyesyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languageyesnoyes infoQuery statements complying to ANSI 2011SQL-like query language (SphinxQL)
APIs and other access methodsJDBC
Native API
ADO.NET
JDBC
ODBC
OLE DB
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
gRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
Proprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C#
C++
Go
Java
JavaScript (Node.js)
Python
Go
Java
JavaScript (Node.js)
Python
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyesJava Stored Proceduresnonono
Triggersyesyesnonono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneShardingShardingSharding 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 replicasSource-replica replicationInternal replication in Colossus, and regional replication between two clusters in different zonesMulti-source replication with 3 replicas for regional instances.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknoyesyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency
Foreign keys infoReferential integritynoyesnoyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDAtomic single-row operationsACID infoStrict serializable isolationno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes 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.yesnonono
User concepts infoAccess controlyesfine grained access rights according to SQL-standardAccess 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
Apache IoTDBCubridGoogle Cloud BigtableGoogle Cloud SpannerSphinx
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