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DBMS > Blueflood vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Percona Server for MongoDB vs. Tarantool

System Properties Comparison Blueflood vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Percona Server for MongoDB vs. Tarantool

Editorial information provided by DB-Engines
NameBlueflood  Xexclude from comparisonHazelcast  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonPercona Server for MongoDB  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionScalable TimeSeries DBMS based on CassandraA widely adopted in-memory data gridFully managed big data interactive analytics platformA drop-in replacement for MongoDB Community Edition with enterprise-grade features.In-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelTime Series DBMSKey-value storeRelational DBMS infocolumn orientedDocument storeDocument store
Key-value store
Relational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12Document 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
Spatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#346  Overall
#33  Time Series DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.60
Rank#246  Overall
#39  Document stores
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Websiteblueflood.iohazelcast.comazure.microsoft.com/­services/­data-explorerwww.percona.com/­mongodb/­software/­percona-server-for-mongodbwww.tarantool.io
Technical documentationgithub.com/­rax-maas/­blueflood/­wikihazelcast.org/­imdg/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.percona.com/­percona-distribution-for-mongodbwww.tarantool.io/­en/­doc
DeveloperRackspaceHazelcastMicrosoftPerconaVK
Initial release20132008201920152008
Current release5.3.6, November 2023cloud service with continuous releases3.4.10-2.10, November 20172.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoGPL Version 2Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC++C and C++
Server operating systemsLinux
OS X
All OS with a Java VMhostedLinuxBSD
Linux
macOS
Data schemepredefined schemeschema-freeFixed schema with schema-less datatypes (dynamic)schema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columns
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-typesyesstring, double, decimal, uuid, integer, blob, boolean, datetime
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.noyes infothe object must implement a serialization strategyyesnono
Secondary indexesnoyesall fields are automatically indexedyesyes
SQL infoSupport of SQLnoSQL-like query languageKusto Query Language (KQL), SQL subsetnoFull-featured ANSI SQL support
APIs and other access methodsHTTP RESTJCache
JPA
Memcached protocol
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
proprietary protocol using JSONOpen binary protocol
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresnoyes infoEvent Listeners, Executor ServicesYes, possible languages: KQL, Python, RJavaScriptLua, C and SQL stored procedures
Triggersnoyes infoEventsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynoyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraShardingSharding infoImplicit feature of the cloud serviceShardingSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on Cassandrayes infoReplicated Mapyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Casual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoone or two-phase-commit; repeatable reads; read commitednonoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, cooperative multitasking
Durability infoSupport for making data persistentyesyesyesyesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnoyes infovia In-Memory Engineyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlnoRole-based access controlAzure Active Directory AuthenticationAccess rights for users and rolesAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources
BluefloodHazelcastMicrosoft Azure Data ExplorerPercona Server for MongoDBTarantool
DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021,  Vladimir Perepelytsya, Tarantool (sponsor) 

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