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DBMS > Apache Druid vs. HarperDB vs. Manticore Search vs. Microsoft Azure Data Explorer vs. TerminusDB

System Properties Comparison Apache Druid vs. HarperDB vs. Manticore Search vs. Microsoft Azure Data Explorer vs. TerminusDB

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonHarperDB  Xexclude from comparisonManticore Search  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonTerminusDB infoformer name was DataChemist  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataUltra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.Multi-storage database for search, including full-text search.Fully managed big data interactive analytics platformScalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships
Primary database modelRelational DBMS
Time Series DBMS
Document storeSearch engineRelational DBMS infocolumn orientedGraph DBMS
Secondary database modelsTime Series DBMS infousing the Manticore Columnar LibraryDocument 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
Document store
RDF store
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
Score0.55
Rank#248  Overall
#38  Document stores
Score0.22
Rank#312  Overall
#21  Search engines
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.17
Rank#325  Overall
#29  Graph DBMS
Websitedruid.apache.orgwww.harperdb.iomanticoresearch.comazure.microsoft.com/­services/­data-explorerterminusdb.com
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.harperdb.io/­docsmanual.manticoresearch.comdocs.microsoft.com/­en-us/­azure/­data-explorerterminusdb.github.io/­terminusdb/­#
DeveloperApache Software Foundation and contributorsHarperDBManticore SoftwareMicrosoftDataChemist Ltd.
Initial release20122017201720192018
Current release29.0.1, April 20243.1, August 20216.0, February 2023cloud service with continuous releases11.0.0, January 2023
License infoCommercial or Open SourceOpen Source infoApache license v2commercial infofree community edition availableOpen Source infoGPL version 2commercialOpen Source infoGPL V3
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaNode.jsC++Prolog, Rust
Server operating systemsLinux
OS X
Unix
Linux
OS X
FreeBSD
Linux
macOS
Windows
hostedLinux
Data schemeyes infoschema-less columns are supporteddynamic schemaFixed schemaFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyes infoJSON data typesInt, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Booleanyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.nonoCan index from XMLyesno
Secondary indexesyesyesyes infofull-text index on all search fieldsall fields are automatically indexed
SQL infoSupport of SQLSQL for queryingSQL-like data manipulation statementsSQL-like query languageKusto Query Language (KQL), SQL subsetSQL-like query language (WOQL)
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket
Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
OWL
RESTful HTTP API
WOQL (Web Object Query Language)
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
JavaScript
Python
Server-side scripts infoStored proceduresnoCustom Functions infosince release 3.1user defined functionsYes, possible languages: KQL, Python, Ryes
Triggersnononoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedA table resides as a whole on one (or more) nodes in a clusterSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding infoImplicit feature of the cloud serviceGraph Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyes infothe nodes on which a table resides can be definedSynchronous replication based on Galera libraryyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Journaling Streams
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of specific operationsyes infoisolated transactions for atomic changes and binary logging for safe writesnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes, using LMDByes infoThe original contents of fields are not stored in the Manticore index.yesyes infoin-memory journaling
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users and rolesnoAzure Active Directory AuthenticationRole-based access control

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More resources
Apache DruidHarperDBManticore SearchMicrosoft Azure Data ExplorerTerminusDB infoformer name was DataChemist
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