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DBMS > Drizzle vs. Google Cloud Datastore vs. Solr vs. YDB

System Properties Comparison Drizzle vs. Google Cloud Datastore vs. Solr vs. YDB

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Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSolr  Xexclude from comparisonYDB  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 PlatformA widely used distributed, scalable search engine based on Apache LuceneA distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API
Primary database modelRelational DBMSDocument storeSearch engineDocument store
Relational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score42.91
Rank#24  Overall
#3  Search engines
Score0.35
Rank#278  Overall
#43  Document stores
#127  Relational DBMS
Websitecloud.google.com/­datastoresolr.apache.orggithub.com/­ydb-platform/­ydb
ydb.tech
Technical documentationcloud.google.com/­datastore/­docssolr.apache.org/­resources.htmlydb.tech/­en/­docs
DeveloperDrizzle project, originally started by Brian AkerGoogleApache Software FoundationYandex
Initial release2008200820062019
Current release7.2.4, September 20129.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialOpen Source infoApache Version 2Open Source infoApache 2.0; commercial license 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
Server operating systemsFreeBSD
Linux
OS X
hostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
Data schemeyesschema-freeyes infoDynamic Fields enables on-the-fly addition of new fieldsFlexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateyesyes, details hereyes infosupports customizable data types and automatic typingyes
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 indexesyesyesyes infoAll search fields are automatically indexedyes
SQL infoSupport of SQLyes infowith proprietary extensionsSQL-like query language (GQL)Solr Parallel SQL InterfaceSQL-like query language (YQL)
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP/JSON API
RESTful HTTP API (DynamoDB compatible)
Supported programming languagesC
C++
Java
PHP
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Go
Java
JavaScript (Node.js)
PHP
Python
Server-side scripts infoStored proceduresnousing Google App EngineJava pluginsno
Triggersno infohooks for callbacks inside the server can be used.Callbacks using the Google Apps Engineyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using PaxosyesActive-passive shard replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
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 ConsistencyImmediate 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 Transactionsoptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
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)yesAccess rights defined for Yandex Cloud users

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More resources
DrizzleGoogle Cloud DatastoreSolrYDB
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