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DBMS > Drizzle vs. Google Cloud Datastore vs. NSDb vs. Sphinx

System Properties Comparison Drizzle vs. Google Cloud Datastore vs. NSDb vs. Sphinx

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Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonNSDb  Xexclude from comparisonSphinx  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelRelational DBMSDocument storeTime Series DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Websitecloud.google.com/­datastorensdb.iosphinxsearch.com
Technical documentationcloud.google.com/­datastore/­docsnsdb.io/­Architecturesphinxsearch.com/­docs
DeveloperDrizzle project, originally started by Brian AkerGoogleSphinx Technologies Inc.
Initial release2008200820172001
Current release7.2.4, September 20123.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialOpen Source infoApache Version 2.0Open Source infoGPL version 2, commercial licence available
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 languageC++Java, ScalaC++
Server operating systemsFreeBSD
Linux
OS X
hostedLinux
macOS
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesschema-freeyes
Typing infopredefined data types such as float or dateyesyes, details hereyes: int, bigint, decimal, stringno
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 indexesyesyesall fields are automatically indexedyes infofull-text index on all search fields
SQL infoSupport of SQLyes infowith proprietary extensionsSQL-like query language (GQL)SQL-like query languageSQL-like query language (SphinxQL)
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
gRPC
HTTP REST
WebSocket
Proprietary protocol
Supported programming languagesC
C++
Java
PHP
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnousing Google App Enginenono
Triggersno infohooks for callbacks inside the server can be used.Callbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using Paxosnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownono
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.Eventual Consistency
Foreign keys infoReferential integrityyesyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesUsing Apache Luceneyes 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.no
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)no

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More resources
DrizzleGoogle Cloud DatastoreNSDbSphinx
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