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DBMS > Apache Druid vs. Google Cloud Firestore vs. QuestDB vs. Sphinx

System Properties Comparison Apache Druid vs. Google Cloud Firestore vs. QuestDB vs. Sphinx

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGoogle Cloud Firestore  Xexclude from comparisonQuestDB  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataCloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.A 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 modelRelational DBMS
Time Series DBMS
Document storeTime Series DBMSSearch engine
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.29
Rank#95  Overall
#49  Relational DBMS
#7  Time Series DBMS
Score8.96
Rank#48  Overall
#8  Document stores
Score2.48
Rank#115  Overall
#9  Time Series DBMS
Score6.03
Rank#60  Overall
#6  Search engines
Websitedruid.apache.orgfirebase.google.com/­products/­firestorequestdb.iosphinxsearch.com
Technical documentationdruid.apache.org/­docs/­latest/­designfirebase.google.com/­docs/­firestorequestdb.io/­docssphinxsearch.com/­docs
DeveloperApache Software Foundation and contributorsGoogleQuestDB Technology IncSphinx Technologies Inc.
Initial release2012201720142001
Current release29.0.1, April 20243.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Source infoApache 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

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Implementation languageJavaJava (Zero-GC), C++, RustC++
Server operating systemsLinux
OS X
Unix
hostedLinux
macOS
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyes infoschema-less columns are supportedschema-freeyes infoschema-free via InfluxDB Line Protocolyes
Typing infopredefined data types such as float or dateyesyesyesno
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.nonono
Secondary indexesyesyesnoyes infofull-text index on all search fields
SQL infoSupport of SQLSQL for queryingnoSQL with time-series extensionsSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
RESTful HTTP/JSON API
Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
Proprietary protocol
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
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 proceduresnoyes, Firebase Rules & Cloud Functionsnono
Triggersnoyes, with Cloud Functionsnono
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardinghorizontal partitioning (by timestamps)Sharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesMulti-source replicationSource-replica replication with eventual consistencynone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoUsing Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyesACID 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 infothrough memory mapped files
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.no
More information provided by the system vendor
Apache DruidGoogle Cloud FirestoreQuestDBSphinx
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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