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DBMS > Apache Impala vs. Elasticsearch vs. EsgynDB vs. Microsoft Azure Data Explorer

System Properties Comparison Apache Impala vs. Elasticsearch vs. EsgynDB vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonElasticsearch  Xexclude from comparisonEsgynDB  Xexclude from comparisonMicrosoft Azure Data Explorer  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 metricEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionFully managed big data interactive analytics platform
Primary database modelRelational DBMSSearch engineRelational DBMSRelational DBMS infocolumn oriented
Secondary database modelsDocument storeDocument store
Spatial DBMS
Vector DBMS
Document store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
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
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websiteimpala.apache.orgwww.elastic.co/­elasticsearchwww.esgyn.cnazure.microsoft.com/­services/­data-explorer
Technical documentationimpala.apache.org/­impala-docs.htmlwww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.htmldocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaElasticEsgynMicrosoft
Initial release2013201020152019
Current release4.1.0, June 20228.6, January 2023cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoElastic Licensecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaC++, Java
Server operating systemsLinuxAll OS with a Java VMLinuxhosted
Data schemeyesschema-free infoFlexible type definitions. Once a type is defined, it is persistentyesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
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.nononoyes
Secondary indexesyesyes infoAll search fields are automatically indexedyesall fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languageyesKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
ODBC
Java API
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBC.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby
All languages supporting JDBC/ODBC/ADO.Net.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyesJava Stored ProceduresYes, possible languages: KQL, Python, R
Triggersnoyes infoby using the 'percolation' featurenoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesMulti-source replication between multi datacentersyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceES-Hadoop ConnectoryesSpark connector (open source): github.com/­Azure/­azure-kusto-spark
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 ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noMemcached and Redis integrationnono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosfine grained access rights according to SQL-standardAzure Active Directory Authentication

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More resources
Apache ImpalaElasticsearchEsgynDBMicrosoft Azure Data Explorer
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