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DBMS > Apache Druid vs. HyperSQL vs. Microsoft Azure Data Explorer vs. Oracle NoSQL vs. VoltDB

System Properties Comparison Apache Druid vs. HyperSQL vs. Microsoft Azure Data Explorer vs. Oracle NoSQL vs. VoltDB

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonHyperSQL infoalso known as HSQLDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOracle NoSQL  Xexclude from comparisonVoltDB  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataMultithreaded, transactional RDBMS written in Java infoalso known as HSQLDBFully 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 nodesDistributed In-Memory NewSQL RDBMS infoUsed for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSRelational DBMS infocolumn orientedDocument store
Key-value store
Relational DBMS
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
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score3.23
Rank#93  Overall
#48  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score3.05
Rank#97  Overall
#17  Document stores
#16  Key-value stores
#50  Relational DBMS
Score1.47
Rank#157  Overall
#73  Relational DBMS
Websitedruid.apache.orghsqldb.orgazure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­nosql/­technologies/­nosqlwww.voltdb.com
Technical documentationdruid.apache.org/­docs/­latest/­designhsqldb.org/­web/­hsqlDocsFrame.htmldocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­en/­database/­other-databases/­nosql-database/­index.htmldocs.voltdb.com
DeveloperApache Software Foundation and contributorsMicrosoftOracleVoltDB Inc.
Initial release20122001201920112010
Current release29.0.1, April 20242.7.2, June 2023cloud service with continuous releases24.1, May 202411.3, April 2022
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infobased on BSD licensecommercialOpen Source infoProprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)Open Source infoAGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaJavaJava, C++
Server operating systemsLinux
OS X
Unix
All OS with a Java VM infoEmbedded (into Java applications) and Client-Server operating modeshostedLinux
Solaris SPARC/x86
Linux
OS X infofor development
Data schemeyes infoschema-less columns are supportedyesFixed schema with schema-less datatypes (dynamic)Support Fixed schema and Schema-less deployment with the ability to interoperate between them.yes
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesoptionalyes
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.nonoyesno
Secondary indexesyesyesall fields are automatically indexedyesyes
SQL infoSupport of SQLSQL for queryingyesKusto Query Language (KQL), SQL subsetSQL-like DML and DDL statementsyes infoonly a subset of SQL 99
APIs and other access methodsJDBC
RESTful HTTP/JSON API
HTTP API infoJDBC via HTTP
JDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
RESTful HTTP APIJava API
JDBC
RESTful HTTP/JSON API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
All languages supporting JDBC/ODBC
Java
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C#
Go
Java
JavaScript (Node.js)
Python
C#
C++
Erlang infonot officially supported
Go
Java
JavaScript infoNode.js
PHP
Python
Server-side scripts infoStored proceduresnoJava, SQLYes, possible languages: KQL, Python, RnoJava
Triggersnoyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynono
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basednoneSharding infoImplicit feature of the cloud serviceShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesnoneyes 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 featureMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkwith Hadoop integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency infodepending on configuration
Foreign keys infoReferential integritynoyesnonono infoFOREIGN KEY constraints are not supported
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoconfigurable infoACID within a storage node (=shard)ACID infoTransactions are executed single-threaded within stored procedures
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes infoData access is serialized by the server
Durability infoSupport for making data persistentyesyesyesyesyes infoSnapshots and command logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnoyes infooff heap cache
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemfine grained access rights according to SQL-standardAzure Active Directory AuthenticationAccess rights for users and rolesUsers and roles with access to stored procedures

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More resources
Apache DruidHyperSQL infoalso known as HSQLDBMicrosoft Azure Data ExplorerOracle NoSQLVoltDB
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