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DBMS > Apache Impala vs. Elasticsearch vs. GridDB vs. OushuDB vs. Spark SQL

System Properties Comparison Apache Impala vs. Elasticsearch vs. GridDB vs. OushuDB vs. Spark SQL

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonElasticsearch  Xexclude from comparisonGridDB  Xexclude from comparisonOushuDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
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 metricScalable in-memory time series database optimized for IoT and Big DataA data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learningSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSSearch engineTime Series DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument storeDocument store
Spatial DBMS
Vector DBMS
Key-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score135.35
Rank#7  Overall
#1  Search engines
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score0.04
Rank#363  Overall
#154  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteimpala.apache.orgwww.elastic.co/­elasticsearchgriddb.netwww.oushu.com/­product/­oushuDBspark.apache.org/­sql
Technical documentationimpala.apache.org/­impala-docs.htmlwww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.htmldocs.griddb.netwww.oushu.com/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaElasticToshiba CorporationOushuApache Software Foundation
Initial release2013201020132014
Current release4.1.0, June 20228.6, January 20235.1, August 20224.0.1, August 20203.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoElastic LicenseOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercialOpen Source infoApache 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++Scala
Server operating systemsLinuxAll OS with a Java VMLinuxLinuxLinux
OS X
Windows
Data schemeyesschema-free infoFlexible type definitions. Once a type is defined, it is persistentyesyesyes
Typing infopredefined data types such as float or dateyesyesyes infonumerical, string, blob, geometry, boolean, timestampyesyes
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 indexedyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languageSQL92, SQL-like TQL (Toshiba Query Language)Full-featured ANSI SQL supportSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
Java API
RESTful HTTP/JSON API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
ODBC
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C++
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyesnoyesno
Triggersnoyes infoby using the 'percolation' featureyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyesyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceES-Hadoop ConnectorConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsHadoop integration
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, allImmediate consistency within container, eventual consistency across containers
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID at container levelACIDno
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 integrationyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users can be defined per databaseKerberos, SSL and role based accessno
More information provided by the system vendor
Apache ImpalaElasticsearchGridDBOushuDBSpark SQL
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
Apache ImpalaElasticsearchGridDBOushuDBSpark SQL
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