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DBMS > Amazon Aurora vs. Apache Druid vs. Hawkular Metrics vs. Spark SQL

System Properties Comparison Amazon Aurora vs. Apache Druid vs. Hawkular Metrics vs. Spark SQL

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Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonApache Druid  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMS
Time Series DBMS
Time Series DBMSRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.57
Rank#51  Overall
#32  Relational DBMS
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score0.08
Rank#366  Overall
#39  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteaws.amazon.com/­rds/­auroradruid.apache.orgwww.hawkular.orgspark.apache.org/­sql
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmldruid.apache.org/­docs/­latest/­designwww.hawkular.org/­hawkular-metrics/­docs/­user-guidespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazonApache Software Foundation and contributorsCommunity supported by Red HatApache Software Foundation
Initial release2015201220142014
Current release29.0.1, April 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache license v2Open Source infoApache 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaScala
Server operating systemshostedLinux
OS X
Unix
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeyesyes infoschema-less columns are supportedschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.yesnonono
Secondary indexesyesyesnono
SQL infoSupport of SQLyesSQL for queryingnoSQL-like DML and DDL statements
APIs and other access methodsADO.NET
JDBC
ODBC
JDBC
RESTful HTTP/JSON API
HTTP RESTJDBC
ODBC
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Clojure
JavaScript
PHP
Python
R
Ruby
Scala
Go
Java
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresyesnonono
Triggersyesnoyes infovia Hawkular Alertingno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningSharding infomanual/auto, time-basedSharding infobased on Cassandrayes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes, via HDFS, S3 or other storage enginesselectable replication factor infobased on Cassandranone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonono
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.yesnonono
User concepts infoAccess controlfine grained access rights according to SQL-standardRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemnono

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More resources
Amazon AuroraApache DruidHawkular MetricsSpark SQL
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