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DBMS > Hazelcast vs. LevelDB vs. Manticore Search vs. Microsoft Azure Data Explorer vs. Sqrrl

System Properties Comparison Hazelcast vs. LevelDB vs. Manticore Search vs. Microsoft Azure Data Explorer vs. Sqrrl

Editorial information provided by DB-Engines
NameHazelcast  Xexclude from comparisonLevelDB  Xexclude from comparisonManticore Search  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSqrrl  Xexclude from comparison
Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.
DescriptionA widely adopted in-memory data gridEmbeddable fast key-value storage library that provides an ordered mapping from string keys to string valuesMulti-storage database for search, including full-text search.Fully managed big data interactive analytics platformAdaptable, secure NoSQL built on Apache Accumulo
Primary database modelKey-value storeKey-value storeSearch engineRelational DBMS infocolumn orientedDocument store
Graph DBMS
Key-value store
Wide column store
Secondary database modelsDocument store infoJSON support with IMDG 3.12Time 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
Score5.97
Rank#57  Overall
#6  Key-value stores
Score2.35
Rank#111  Overall
#19  Key-value stores
Score0.22
Rank#312  Overall
#21  Search engines
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitehazelcast.comgithub.com/­google/­leveldbmanticoresearch.comazure.microsoft.com/­services/­data-explorersqrrl.com
Technical documentationhazelcast.org/­imdg/­docsgithub.com/­google/­leveldb/­blob/­main/­doc/­index.mdmanual.manticoresearch.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperHazelcastGoogleManticore SoftwareMicrosoftAmazon infooriginally Sqrrl Data, Inc.
Initial release20082011201720192012
Current release5.3.6, November 20231.23, February 20216.0, February 2023cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availableOpen Source infoBSDOpen Source infoGPL version 2commercialcommercial
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 languageJavaC++C++Java
Server operating systemsAll OS with a Java VMIllumos
Linux
OS X
Windows
FreeBSD
Linux
macOS
Windows
hostedLinux
Data schemeschema-freeschema-freeFixed schemaFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyesnoInt, 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.yes infothe object must implement a serialization strategynoCan index from XMLyes
Secondary indexesyesnoyes infofull-text index on all search fieldsall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like query languagenoSQL-like query languageKusto Query Language (KQL), SQL subsetno
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C++
Go
Java info3rd party binding
JavaScript (Node.js) info3rd party binding
Python info3rd party binding
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Actionscript
C infousing GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk
Server-side scripts infoStored proceduresyes infoEvent Listeners, Executor Servicesnouser defined functionsYes, possible languages: KQL, Python, Rno
Triggersyes infoEventsnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding infoImplicit feature of the cloud serviceSharding infomaking use of Hadoop
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated MapnoneSynchronous replication based on Galera libraryyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.selectable replication factor infomaking use of Hadoop
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency infoDocument store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataone or two-phase-commit; repeatable reads; read commitednoyes infoisolated transactions for atomic changes and binary logging for safe writesnoAtomic updates per row, document, or graph entity
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes infowith automatic compression on writesyes infoThe original contents of fields are not stored in the Manticore index.yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlRole-based access controlnonoAzure Active Directory AuthenticationCell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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More resources
HazelcastLevelDBManticore SearchMicrosoft Azure Data ExplorerSqrrl
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