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DBMS > Apache Druid vs. Graph Engine vs. Hawkular Metrics vs. Microsoft Azure Data Explorer vs. Oracle NoSQL

System Properties Comparison Apache Druid vs. Graph Engine vs. Hawkular Metrics vs. Microsoft Azure Data Explorer vs. Oracle NoSQL

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOracle NoSQL  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataA distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Fully managed big data interactive analytics platformA multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes
Primary database modelRelational DBMS
Time Series DBMS
Graph DBMS
Key-value store
Time Series DBMSRelational DBMS infocolumn orientedDocument store
Key-value store
Relational DBMS
Secondary database modelsDocument 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
Score3.34
Rank#88  Overall
#48  Relational DBMS
#7  Time Series DBMS
Score0.61
Rank#240  Overall
#21  Graph DBMS
#35  Key-value stores
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score2.95
Rank#100  Overall
#17  Document stores
#17  Key-value stores
#50  Relational DBMS
Websitedruid.apache.orgwww.graphengine.iowww.hawkular.orgazure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­nosql/­technologies/­nosql
Technical documentationdruid.apache.org/­docs/­latest/­designwww.graphengine.io/­docs/­manualwww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­en/­database/­other-databases/­nosql-database/­index.html
DeveloperApache Software Foundation and contributorsMicrosoftCommunity supported by Red HatMicrosoftOracle
Initial release20122010201420192011
Current release29.0.1, April 2024cloud service with continuous releases23.3, December 2023
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoMIT LicenseOpen Source infoApache 2.0commercialOpen Source infoProprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava.NET and CJavaJava
Server operating systemsLinux
OS X
Unix
.NETLinux
OS X
Windows
hostedLinux
Solaris SPARC/x86
Data schemeyes infoschema-less columns are supportedyesschema-freeFixed schema with schema-less datatypes (dynamic)Support Fixed schema and Schema-less deployment with the ability to interoperate between them.
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-typesoptional
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.nononoyesno
Secondary indexesyesnoall fields are automatically indexedyes
SQL infoSupport of SQLSQL for queryingnonoKusto Query Language (KQL), SQL subsetSQL-like DML and DDL statements
APIs and other access methodsJDBC
RESTful HTTP/JSON API
RESTful HTTP APIHTTP RESTMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C#
C++
F#
Visual Basic
Go
Java
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C#
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresnoyesnoYes, possible languages: KQL, Python, Rno
Triggersnonoyes infovia Hawkular Alertingyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedhorizontal partitioningSharding infobased on CassandraSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesselectable replication factor infobased on Cassandrayes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkwith Hadoop integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Eventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency infodepending on configuration
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonononoconfigurable infoACID within a storage node (=shard)
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesoptional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnonoyes infooff heap cache
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemnoAzure Active Directory AuthenticationAccess rights for users and roles

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More resources
Apache DruidGraph Engine infoformer name: TrinityHawkular MetricsMicrosoft Azure Data ExplorerOracle NoSQL
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