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DBMS > Apache Impala vs. Elasticsearch vs. FoundationDB vs. Spark SQL vs. Tkrzw

System Properties Comparison Apache Impala vs. Elasticsearch vs. FoundationDB vs. Spark SQL vs. Tkrzw

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonElasticsearch  Xexclude from comparisonFoundationDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.
DescriptionAnalytic DBMS for HadoopA 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 metricOrdered key-value store. Core features are complimented by layers.Spark SQL is a component on top of 'Spark Core' for structured data processingA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelRelational DBMSSearch engineDocument store infosupported via specific layer
Key-value store
Relational DBMS infosupported via specific SQL-layer
Relational DBMSKey-value store
Secondary database modelsDocument storeDocument store
Spatial DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score14.03
Rank#40  Overall
#24  Relational DBMS
Score134.78
Rank#7  Overall
#1  Search engines
Score1.07
Rank#188  Overall
#31  Document stores
#28  Key-value stores
#86  Relational DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Score0.09
Rank#354  Overall
#51  Key-value stores
Websiteimpala.apache.orgwww.elastic.co/­elasticsearchgithub.com/­apple/­foundationdbspark.apache.org/­sqldbmx.net/­tkrzw
Technical documentationimpala.apache.org/­impala-docs.htmlwww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.htmlapple.github.io/­foundationdbspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaElasticFoundationDBApache Software FoundationMikio Hirabayashi
Initial release20132010201320142020
Current release4.1.0, June 20228.6, January 20236.2.28, November 20203.5.0 ( 2.13), September 20230.9.3, August 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoElastic LicenseOpen Source infoApache 2.0Open Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaC++ScalaC++
Server operating systemsLinuxAll OS with a Java VMLinux
OS X
Windows
Linux
OS X
Windows
Linux
macOS
Data schemeyesschema-free infoFlexible type definitions. Once a type is defined, it is persistentschema-free infosome layers support schemasyesschema-free
Typing infopredefined data types such as float or dateyesyesno infosome layers support typingyesno
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.nononono
Secondary indexesyesyes infoAll search fields are automatically indexednono
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languagesupported in specific SQL layer onlySQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
Java API
RESTful HTTP/JSON API
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby
.Net
C
C++
Go
Java
JavaScript infoNode.js
PHP
Python
Ruby
Swift
Java
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyesin SQL-layer onlynono
Triggersnoyes infoby using the 'percolation' featurenonono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesyesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceES-Hadoop Connectornono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency infoSynchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, allLinearizable consistencyImmediate Consistency
Foreign keys infoReferential integritynonoin SQL-layer onlynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDno
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.noMemcached and Redis integrationnoyes infousing specific database classes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosnonono

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More resources
Apache ImpalaElasticsearchFoundationDBSpark SQLTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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