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DBMS > Apache Druid vs. Spark SQL vs. Yanza

System Properties Comparison Apache Druid vs. Spark SQL vs. Yanza

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonSpark SQL  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataSpark SQL is a component on top of 'Spark Core' for structured data processingTime Series DBMS for IoT Applications
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSTime Series 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
Score19.15
Rank#33  Overall
#20  Relational DBMS
Websitedruid.apache.orgspark.apache.org/­sqlyanza.com
Technical documentationdruid.apache.org/­docs/­latest/­designspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation and contributorsApache Software FoundationYanza
Initial release201220142015
Current release29.0.1, April 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoApache 2.0commercial infofree version available
Cloud-based only infoOnly available as a cloud servicenonono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScala
Server operating systemsLinux
OS X
Unix
Linux
OS X
Windows
Windows
Data schemeyes infoschema-less columns are supportedyesschema-free
Typing infopredefined data types such as float or dateyesyesno
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 indexesyesnono
SQL infoSupport of SQLSQL for queryingSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
Java
Python
R
Scala
any language that supports HTTP calls
Server-side scripts infoStored proceduresnonono
Triggersnonoyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemnono

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More resources
Apache DruidSpark SQLYanza
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