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DBMS > Hawkular Metrics vs. Microsoft Azure Data Explorer vs. Tarantool vs. TimescaleDB vs. Valentina Server

System Properties Comparison Hawkular Metrics vs. Microsoft Azure Data Explorer vs. Tarantool vs. TimescaleDB vs. Valentina Server

Editorial information provided by DB-Engines
NameHawkular Metrics  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonTarantool  Xexclude from comparisonTimescaleDB  Xexclude from comparisonValentina Server  Xexclude from comparison
DescriptionHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Fully managed big data interactive analytics platformIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQLObject-relational database and reports server
Primary database modelTime Series DBMSRelational DBMS infocolumn orientedDocument store
Key-value store
Relational DBMS
Time Series DBMSRelational 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
Spatial DBMS infowith Tarantool/GIS extensionRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Score0.17
Rank#327  Overall
#145  Relational DBMS
Websitewww.hawkular.orgazure.microsoft.com/­services/­data-explorerwww.tarantool.iowww.timescale.comwww.valentina-db.net
Technical documentationwww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.microsoft.com/­en-us/­azure/­data-explorerwww.tarantool.io/­en/­docdocs.timescale.comvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperCommunity supported by Red HatMicrosoftVKTimescaleParadigma Software
Initial release20142019200820171999
Current releasecloud service with continuous releases2.10.0, May 20222.15.0, May 20245.7.5
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool EnterpriseOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC and C++C
Server operating systemsLinux
OS X
Windows
hostedBSD
Linux
macOS
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeschema-freeFixed schema with schema-less datatypes (dynamic)Flexible data schema: relational definition for tables with ability to store json-like documents in columnsyesyes
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesstring, double, decimal, uuid, integer, blob, boolean, datetimenumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex 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.noyesnoyes
Secondary indexesnoall fields are automatically indexedyesyesyes
SQL infoSupport of SQLnoKusto Query Language (KQL), SQL subsetFull-featured ANSI SQL supportyes infofull PostgreSQL SQL syntaxyes
APIs and other access methodsHTTP RESTMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Open binary protocolADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ODBC
Supported programming languagesGo
Java
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
Server-side scripts infoStored proceduresnoYes, possible languages: KQL, Python, RLua, C and SQL stored proceduresuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shellyes
Triggersyes infovia Hawkular Alertingyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes, before/after data modification events, on replication events, client session eventsyesyes
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraSharding infoImplicit feature of the cloud serviceSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.yes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on Cassandrayes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
Source-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
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
Immediate Consistency
Foreign keys infoReferential integritynonoyesyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, cooperative multitaskingyes
Durability infoSupport for making data persistentyesyesyes, write ahead loggingyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes, full featured in-memory storage engine with persistencenoyes
User concepts infoAccess controlnoAzure Active Directory AuthenticationAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles
fine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Hawkular MetricsMicrosoft Azure Data ExplorerTarantoolTimescaleDBValentina Server
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Data processing speed and reliability: in-memory synchronous replication
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