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DBMS > Apache Impala vs. GridDB vs. Microsoft Azure Table Storage vs. XTDB

System Properties Comparison Apache Impala vs. GridDB vs. Microsoft Azure Table Storage vs. XTDB

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonGridDB  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonXTDB infoformerly named Crux  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopScalable in-memory time series database optimized for IoT and Big DataA Wide Column Store for rapid development using massive semi-structured datasetsA general purpose database with bitemporal SQL and Datalog and graph queries
Primary database modelRelational DBMSTime Series DBMSWide column storeDocument store
Secondary database modelsDocument storeKey-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
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Score0.11
Rank#343  Overall
#46  Document stores
Websiteimpala.apache.orggriddb.netazure.microsoft.com/­en-us/­services/­storage/­tablesgithub.com/­xtdb/­xtdb
www.xtdb.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.griddb.netwww.xtdb.com/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaToshiba CorporationMicrosoftJuxt Ltd.
Initial release2013201320122019
Current release4.1.0, June 20225.1, August 20221.19, September 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercialOpen Source infoMIT License
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++Clojure
Server operating systemsLinuxLinuxhostedAll OS with a Java 8 (and higher) VM
Linux
Data schemeyesyesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesyes, extensible-data-notation format
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 indexesyesyesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL92, SQL-like TQL (Toshiba Query Language)nolimited SQL, making use of Apache Calcite
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
RESTful HTTP APIHTTP REST
JDBC
Supported programming languagesAll languages supporting JDBC/ODBCC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Clojure
Java
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenonono
Triggersnoyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes, each node contains all data
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID at container leveloptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users can be defined per databaseAccess rights based on private key authentication or shared access signatures
More information provided by the system vendor
Apache ImpalaGridDBMicrosoft Azure Table StorageXTDB infoformerly named Crux
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 ImpalaGridDBMicrosoft Azure Table StorageXTDB infoformerly named Crux
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