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DBMS > Google Cloud Datastore vs. MarkLogic vs. QuestDB vs. Sphinx

System Properties Comparison Google Cloud Datastore vs. MarkLogic vs. QuestDB vs. Sphinx

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonMarkLogic  Xexclude from comparisonQuestDB  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformOperational and transactional Enterprise NoSQL databaseA high performance open source SQL database for time series dataOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelDocument storeDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Time Series DBMSSearch engine
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.49
Rank#79  Overall
#12  Document stores
Score6.50
Rank#56  Overall
#10  Document stores
#1  Native XML DBMS
#1  RDF stores
#5  Search engines
Score2.48
Rank#115  Overall
#9  Time Series DBMS
Score6.03
Rank#60  Overall
#6  Search engines
Websitecloud.google.com/­datastorewww.marklogic.comquestdb.iosphinxsearch.com
Technical documentationcloud.google.com/­datastore/­docsdocs.marklogic.comquestdb.io/­docssphinxsearch.com/­docs
DeveloperGoogleMarkLogic Corp.QuestDB Technology IncSphinx Technologies Inc.
Initial release2008200120142001
Current release11.0, December 20223.5.1, February 2023
License infoCommercial or Open Sourcecommercialcommercial inforestricted free version is availableOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++Java (Zero-GC), C++, RustC++
Server operating systemshostedLinux
OS X
Windows
Linux
macOS
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-freeschema-free infoSchema can be enforcedyes infoschema-free via InfluxDB Line Protocolyes
Typing infopredefined data types such as float or dateyes, details hereyesyesno
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.noyesno
Secondary indexesyesyesnoyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query language (GQL)yes infoSQL92SQL with time-series extensionsSQL-like query language (SphinxQL)
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
Proprietary protocol
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresusing Google App Engineyes infovia XQuery or JavaScriptnono
TriggersCallbacks using the Google Apps Engineyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioning (by timestamps)Sharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosyesSource-replica replication with eventual consistencynone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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.Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID infocan act as a resource manager in an XA/JTA transactionACID for single-table writesno
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, with Range Indexesyes infothrough memory mapped files
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access control at the document and subdocument levelsno
More information provided by the system vendor
Google Cloud DatastoreMarkLogicQuestDBSphinx
Specific characteristicsRelational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
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