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DBMS > Blueflood vs. Google Cloud Datastore vs. Machbase Neo vs. Spark SQL vs. Sphinx

System Properties Comparison Blueflood vs. Google Cloud Datastore vs. Machbase Neo vs. Spark SQL vs. Sphinx

Editorial information provided by DB-Engines
NameBlueflood  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonMachbase Neo infoFormer name was Infiniflux  Xexclude from comparisonSpark SQL  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionScalable TimeSeries DBMS based on CassandraAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformTimeSeries DBMS for AIoT and BigDataSpark SQL is a component on top of 'Spark Core' for structured data processingOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSDocument storeTime Series DBMSRelational DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#346  Overall
#33  Time Series DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score0.17
Rank#337  Overall
#30  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Websiteblueflood.iocloud.google.com/­datastoremachbase.comspark.apache.org/­sqlsphinxsearch.com
Technical documentationgithub.com/­rax-maas/­blueflood/­wikicloud.google.com/­datastore/­docsmachbase.com/­dbmsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsphinxsearch.com/­docs
DeveloperRackspaceGoogleMachbaseApache Software FoundationSphinx Technologies Inc.
Initial release20132008201320142001
Current releaseV8.0, August 20233.5.0 ( 2.13), September 20233.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercial infofree test version availableOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaCScalaC++
Server operating systemsLinux
OS X
hostedLinux
macOS
Windows
Linux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemepredefined schemeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyes, 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.nononono
Secondary indexesnoyesyesnoyes infofull-text index on all search fields
SQL infoSupport of SQLnoSQL-like query language (GQL)SQL-like query languageSQL-like DML and DDL statementsSQL-like query language (SphinxQL)
APIs and other access methodsHTTP RESTgRPC (using protocol buffers) API
RESTful HTTP/JSON API
gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
JDBC
ODBC
Proprietary protocol
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Go
Java
JavaScript
PHP infovia ODBC
Python
R infovia ODBC
Scala
Java
Python
R
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnousing Google App Enginenonono
TriggersnoCallbacks using the Google Apps Enginenonono
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraShardingShardingyes, utilizing Spark CoreSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on CassandraMulti-source replication using Paxosselectable replication factornonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
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 pathsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnonono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesnoyesyes 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.nonoyes infovolatile and lookup tableno
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)simple password-based access controlnono

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More resources
BluefloodGoogle Cloud DatastoreMachbase Neo infoFormer name was InfinifluxSpark SQLSphinx
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