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DBMS > Apache Phoenix vs. HugeGraph vs. Prometheus vs. QuestDB vs. Spark SQL

System Properties Comparison Apache Phoenix vs. HugeGraph vs. Prometheus vs. QuestDB vs. Spark SQL

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonHugeGraph  Xexclude from comparisonPrometheus  Xexclude from comparisonQuestDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseA fast-speed and highly-scalable Graph DBMSOpen-source Time Series DBMS and monitoring systemA high performance open source SQL database for time series dataSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSGraph DBMSTime Series DBMSTime Series DBMSRelational DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score7.69
Rank#50  Overall
#3  Time Series DBMS
Score2.70
Rank#105  Overall
#8  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitephoenix.apache.orggithub.com/­hugegraph
hugegraph.apache.org
prometheus.ioquestdb.iospark.apache.org/­sql
Technical documentationphoenix.apache.orghugegraph.apache.org/­docsprometheus.io/­docsquestdb.io/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationBaiduQuestDB Technology IncApache Software Foundation
Initial release20142018201520142014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20190.93.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0Open Source infoApache 2.0Open Source infoApache 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaGoJava (Zero-GC), C++, RustScala
Server operating systemsLinux
Unix
Windows
Linux
macOS
Unix
Linux
Windows
Linux
macOS
Windows
Linux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyesyes infoschema-free via InfluxDB Line Protocolyes
Typing infopredefined data types such as float or dateyesyesNumeric data onlyyesyes
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 infoImport of XML data possiblenono
Secondary indexesyesyes infoalso supports composite index and range indexnonono
SQL infoSupport of SQLyesnonoSQL with time-series extensionsSQL-like DML and DDL statements
APIs and other access methodsJDBCJava API
RESTful HTTP API
TinkerPop Gremlin
RESTful HTTP/JSON APIHTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
Groovy
Java
Python
.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsasynchronous Gremlin script jobsnonono
Triggersnonononono
Partitioning methods infoMethods for storing different data on different nodesShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseShardinghorizontal partitioning (by timestamps)yes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yes infodepending on used storage backend, e.g. Cassandra and HBaseyes infoby FederationSource-replica replication with eventual consistencynone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationvia hugegraph-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual ConsistencynoneImmediate Consistency
Foreign keys infoReferential integritynoyes infoedges in graphnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID for single-table writesno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnoyes infothrough memory mapped filesno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyUsers, roles and permissionsnono
More information provided by the system vendor
Apache PhoenixHugeGraphPrometheusQuestDBSpark SQL
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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More resources
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