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DBMS > Memcached vs. Microsoft Azure Data Explorer vs. PostgreSQL vs. TimesTen vs. Vertica

System Properties Comparison Memcached vs. Microsoft Azure Data Explorer vs. PostgreSQL vs. TimesTen vs. Vertica

Editorial information provided by DB-Engines
NameMemcached  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonPostgreSQL  Xexclude from comparisonTimesTen  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionIn-memory key-value store, originally intended for cachingFully managed big data interactive analytics platformWidely used open source RDBMS infoDeveloped as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQLIn-Memory RDBMS compatible to OracleCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelKey-value storeRelational DBMS infocolumn orientedRelational DBMS infowith object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.Relational DBMSRelational DBMS infoColumn oriented
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
Document store
Graph DBMS infowith Apache Age
Spatial DBMS
Vector DBMS infowith pgvector extension
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score19.42
Rank#32  Overall
#4  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score645.54
Rank#4  Overall
#4  Relational DBMS
Score1.31
Rank#163  Overall
#74  Relational DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitewww.memcached.orgazure.microsoft.com/­services/­data-explorerwww.postgresql.orgwww.oracle.com/­database/­technologies/­related/­timesten.htmlwww.vertica.com
Technical documentationgithub.com/­memcached/­memcached/­wikidocs.microsoft.com/­en-us/­azure/­data-explorerwww.postgresql.org/­docsdocs.oracle.com/­database/­timesten-18.1vertica.com/­documentation
DeveloperDanga Interactive infooriginally developed by Brad Fitzpatrick for LiveJournalMicrosoftPostgreSQL Global Development Group infowww.postgresql.org/­developerOracle, TimesTen Performance Software, HP infooriginally founded in HP Labs it was acquired by Oracle in 2005OpenText infopreviously Micro Focus and Hewlett Packard
Initial release200320191989 info1989: Postgres, 1996: PostgreSQL19982005
Current release1.6.25, March 2024cloud service with continuous releases16.3, May 202411 Release 2 (11.2.2.8.0)12.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoBSD licensecommercialOpen Source infoBSDcommercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenoyesnonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCCC++
Server operating systemsFreeBSD
Linux
OS X
Unix
Windows
hostedFreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows
Linux
Data schemeschema-freeFixed schema with schema-less datatypes (dynamic)yesyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or datenoyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyesyes
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.yesyes infospecific XML-type available, but no XML query functionality.nono
Secondary indexesnoall fields are automatically indexedyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoKusto Query Language (KQL), SQL subsetyes infostandard with numerous extensionsyesFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsProprietary protocolMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languages.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C
C++
Java
PL/SQL
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoYes, possible languages: KQL, Python, Ruser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.PL/SQLyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesnoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoImplicit feature of the cloud servicepartitioning by range, list and (since PostgreSQL 11) by hashnonehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoRepcached, a Memcached patch, provides this functionallityyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication infoother methods possible by using 3rd party extensionsMulti-source replication
Source-replica replication
Multi-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknonono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynonoyesyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentnoyesyesyes infoby means of logfiles and checkpointsyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlyes infousing SASL (Simple Authentication and Security Layer) protocolAzure Active Directory Authenticationfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
MemcachedMicrosoft Azure Data ExplorerPostgreSQLTimesTenVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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More resources
MemcachedMicrosoft Azure Data ExplorerPostgreSQLTimesTenVertica infoOpenText™ Vertica™
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