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DBMS > Apache Doris vs. Databricks vs. Elasticsearch

System Properties Comparison Apache Doris vs. Databricks vs. Elasticsearch

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Editorial information provided by DB-Engines
NameApache Doris  Xexclude from comparisonDatabricks  Xexclude from comparisonElasticsearch  Xexclude from comparison
DescriptionAn MPP-based analytics DBMS embracing the MySQL protocolThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.A distributed, RESTful modern search and analytics engine based on Apache Lucene infoElasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric
Primary database modelRelational DBMSDocument store
Relational DBMS
Search engine
Vector DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.51
Rank#246  Overall
#115  Relational DBMS
Score102.66
Rank#12  Overall
#2  Document stores
#8  Relational DBMS
Score123.81
Rank#9  Overall
#1  Search engines
#1  Vector DBMS
Websitedoris.apache.org
github.com/­apache/­doris
www.databricks.comwww.elastic.co/­elasticsearch
Technical documentationgithub.com/­apache/­doris/­wikidocs.databricks.comwww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.html
DeveloperApache Software Foundation, originally contributed from BaiduDatabricksElastic
Initial release201720132010
Current release1.2.2, February 20238.6, January 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoElastic License
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava
Server operating systemsLinuxhostedAll OS with a Java VM
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-free infoFlexible type definitions. Once a type is defined, it is persistent
Typing infopredefined data types such as float or dateyesyes
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.noyesno
Secondary indexesyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyeswith Databricks SQLSQL-like query language
APIs and other access methodsJDBC
MySQL client
JDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesJavaPython
R
Scala
.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby
Server-side scripts infoStored proceduresuser defined functionsuser defined functions and aggregatesyes
Triggersnoyes infoby using the 'percolation' feature
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyesyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoES-Hadoop Connector
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency infoSynchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno
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.nonoMemcached and Redis integration
User concepts infoAccess controlfine grained access rights according to SQL-standard

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