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

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

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Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonQuestDB  Xexclude from comparisonSolr  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 high performance open source SQL database for time series dataA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSDocument storeTime Series DBMSSearch engine
Secondary database modelsRelational DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score2.52
Rank#109  Overall
#9  Time Series DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitecloud.google.com/­datastorequestdb.iosolr.apache.org
Technical documentationcloud.google.com/­datastore/­docsquestdb.io/­docssolr.apache.org/­resources.html
DeveloperDrizzle project, originally started by Brian AkerGoogleQuestDB Technology IncApache Software Foundation
Initial release2008200820142006
Current release7.2.4, September 20129.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialOpen Source infoApache 2.0Open Source infoApache Version 2
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 (Zero-GC), C++, RustJava
Server operating systemsFreeBSD
Linux
OS X
hostedLinux
macOS
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesschema-freeyes infoschema-free via InfluxDB Line Protocolyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyes, details hereyesyes infosupports customizable data types and automatic typing
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.nonoyes
Secondary indexesyesyesnoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyes infowith proprietary extensionsSQL-like query language (GQL)SQL with time-series extensionsSolr Parallel SQL Interface
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
Java API
RESTful HTTP/JSON API
Supported programming languagesC
C++
Java
PHP
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnousing Google App EnginenoJava plugins
Triggersno infohooks for callbacks inside the server can be used.Callbacks using the Google Apps Enginenoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioning (by timestamps)Sharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using PaxosSource-replica replication with eventual consistencyyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
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 ConsistencyEventual 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 TransactionsACID for single-table writesoptimistic locking
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.noyes infothrough memory mapped filesyes
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)yes
More information provided by the system vendor
DrizzleGoogle Cloud DatastoreQuestDBSolr
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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
DrizzleGoogle Cloud DatastoreQuestDBSolr
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