DB-EnginesInfluxDB: Focus on building software with an easy-to-use serverless, scalable time series platformEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Datomic vs. Heroic vs. LeanXcale vs. Manticore Search vs. Microsoft Azure Data Explorer

System Properties Comparison Datomic vs. Heroic vs. LeanXcale vs. Manticore Search vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonHeroic  Xexclude from comparisonLeanXcale  Xexclude from comparisonManticore Search  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchA highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesMulti-storage database for search, including full-text search.Fully managed big data interactive analytics platform
Primary database modelRelational DBMSTime Series DBMSKey-value store
Relational DBMS
Search engineRelational DBMS infocolumn oriented
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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score0.46
Rank#265  Overall
#22  Time Series DBMS
Score0.36
Rank#280  Overall
#40  Key-value stores
#129  Relational DBMS
Score0.29
Rank#302  Overall
#21  Search engines
Score3.80
Rank#81  Overall
#43  Relational DBMS
Websitewww.datomic.comgithub.com/­spotify/­heroicwww.leanxcale.commanticoresearch.comazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.datomic.comspotify.github.io/­heroicmanual.manticoresearch.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperCognitectSpotifyLeanXcaleManticore SoftwareMicrosoft
Initial release20122014201520172019
Current release1.0.7075, December 20236.0, February 2023cloud service with continuous releases
License infoCommercial or Open Sourcecommercial infolimited edition freeOpen Source infoApache 2.0commercialOpen Source infoGPL version 2commercial
Cloud-based only infoOnly available as a cloud servicenonononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureJavaC++
Server operating systemsAll OS with a Java VMFreeBSD
Linux
macOS
Windows
hosted
Data schemeyesschema-freeyesFixed schemaFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesInt, 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-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.nonoCan index from XMLyes
Secondary indexesyesyes infovia Elasticsearchyes infofull-text index on all search fieldsall fields are automatically indexed
SQL infoSupport of SQLnonoyes infothrough Apache DerbySQL-like query languageKusto Query Language (KQL), SQL subset
APIs and other access methodsRESTful HTTP APIHQL (Heroic Query Language, a JSON-based language)
HTTP API
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesClojure
Java
C
Java
Scala
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyes infoTransaction Functionsnouser defined functionsYes, possible languages: KQL, Python, R
TriggersBy using transaction functionsnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersyesSynchronous replication based on Galera libraryyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDyes infoisolated transactions for atomic changes and binary logging for safe writesno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes infoThe original contents of fields are not stored in the Manticore index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes inforecommended only for testing and developmentnoyesno
User concepts infoAccess controlnonoAzure Active Directory Authentication

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
DatomicHeroicLeanXcaleManticore SearchMicrosoft Azure Data Explorer
Recent citations in the news

Stanchion Turns SQLite Into A Column Store
15 February 2024, iProgrammer

Nubank buys firm behind Clojure programming language
28 July 2020, Finextra

Architecting Software for Leverage
13 November 2021, InfoQ.com

TerminusDB Takes on Data Collaboration with a git-Like Approach
1 December 2020, The New Stack

Brazil’s Nubank acquires US software firm Cognitect, creator of Clojure and Datomic
24 July 2020, LatamList

provided by Google News

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

Integrating Manticore Search with Apache Superset
8 August 2023, hackernoon.com

Clickhouse vs Elasticsearch vs Manticore Search Query Times With a 1.7B NYC Taxi Rides Benchmark
1 June 2022, hackernoon.com

Manticore is a Faster Alternative to Elasticsearch in C++
25 July 2022, hackernoon.com

8 Google Alternatives: How to Search Crypto, the Dark Web, More
1 February 2023, Gizmodo

Highlighting in Search Results
24 May 2020, hackernoon.com

provided by Google News

We’re retiring Azure Time Series Insights on 7 July 2024 – transition to Azure Data Explorer | Azure updates
31 May 2024, Microsoft

Update records in a Kusto Database (public preview)
20 February 2024, Microsoft

Public Preview: Azure Data Explorer connector for Apache Flink
8 January 2024, Microsoft

Announcing General Availability to migrate Virtual Network injected Azure Data Explorer Cluster to Private Endpoints ...
5 February 2024, Microsoft

New Features for graph-match KQL Operator: Enhanced Pattern Matching and Cycle Control | Azure updates
24 January 2024, Microsoft

provided by Google News



Share this page

Featured Products

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Present your product here