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DBMS > Apache IoTDB vs. Google Cloud Datastore vs. Sphinx vs. SurrealDB

System Properties Comparison Apache IoTDB vs. Google Cloud Datastore vs. Sphinx vs. SurrealDB

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSphinx  Xexclude from comparisonSurrealDB  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 FlinkAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformOpen source search engine for searching in data from different sources, e.g. relational databasesA fully ACID transactional, developer-friendly, multi-model DBMS
Primary database modelTime Series DBMSDocument storeSearch engineDocument store
Graph DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score4.13
Rank#71  Overall
#12  Document stores
Score5.97
Rank#56  Overall
#5  Search engines
Score1.11
Rank#174  Overall
#30  Document stores
#17  Graph DBMS
Websiteiotdb.apache.orgcloud.google.com/­datastoresphinxsearch.comsurrealdb.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcloud.google.com/­datastore/­docssphinxsearch.com/­docssurrealdb.com/­docs
DeveloperApache Software FoundationGoogleSphinx Technologies Inc.SurrealDB Ltd
Initial release2018200820012022
Current release1.1.0, April 20233.5.1, February 2023v1.5.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoGPL version 2, commercial licence availableOpen Source
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++Rust
Server operating systemsAll OS with a Java VM (>= 1.8)hostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
macOS
Windows
Data schemeyesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyes, details herenoyes
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
Secondary indexesyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languageSQL-like query language (GQL)SQL-like query language (SphinxQL)SQL-like query language
APIs and other access methodsJDBC
Native API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Proprietary protocolGraphQL
RESTful HTTP API
WebSocket
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Deno
Go
JavaScript (Node.js)
Rust
Server-side scripts infoStored proceduresyesusing Google App Engineno
TriggersyesCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingSharding 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 replicasMulti-source replication using Paxosnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
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.
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.yesno
User concepts infoAccess controlyesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noyes, based on authentication and database rules

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More resources
Apache IoTDBGoogle Cloud DatastoreSphinxSurrealDB
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