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DBMS > EsgynDB vs. MonetDB vs. Vertica vs. VictoriaMetrics

System Properties Comparison EsgynDB vs. MonetDB vs. Vertica vs. VictoriaMetrics

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Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonMonetDB  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparisonVictoriaMetrics  Xexclude from comparison
DescriptionEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionA relational database management system that stores data in columnsCloud 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.A fast, cost-effective and scalable Time Series DBMS and monitoring solution
Primary database modelRelational DBMSRelational DBMSRelational DBMS infoColumn orientedTime Series DBMS
Secondary database modelsDocument store
Spatial DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score1.72
Rank#141  Overall
#64  Relational DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Score1.23
Rank#172  Overall
#15  Time Series DBMS
Websitewww.esgyn.cnwww.monetdb.orgwww.vertica.comvictoriametrics.com
Technical documentationwww.monetdb.org/­Documentationvertica.com/­documentationdocs.victoriametrics.com
github.com/­VictoriaMetrics/­VictoriaMetrics/­wiki
DeveloperEsgynMonetDB BVOpenText infopreviously Micro Focus and Hewlett PackardVictoriaMetrics
Initial release2015200420052018
Current releaseDec2023 (11.49), December 202312.0.3, January 2023v1.91, May 2023
License infoCommercial or Open SourcecommercialOpen Source infoMozilla Public License 2.0commercial infoLimited community edition freeOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containersno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++, JavaCC++Go
Server operating systemsLinuxFreeBSD
Linux
OS X
Solaris
Windows
LinuxFreeBSD
Linux
macOS
OpenBSD
Data schemeyesyesYes, 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 dateyesyesyes
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.nonono
Secondary indexesyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLyesyes infoSQL 2003 with some extensionsFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.no
APIs and other access methodsADO.NET
JDBC
ODBC
JDBC
native C library infoMAPI library (MonetDB application programming interface)
ODBC
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.NetC
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresJava Stored Proceduresyes, in SQL, C, Ryes, PostgreSQL PL/pgSQL, with minor differencesno
Triggersnoyesyes, called Custom Alertsno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding via remote tableshorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication between multi datacentersnone infoSource-replica replication available in experimental statusMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.Synchronous replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono infoBi-directional Spark integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlfine 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
EsgynDBMonetDBVertica infoOpenText™ Vertica™VictoriaMetrics
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
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,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
» more

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EsgynDBMonetDBVertica infoOpenText™ Vertica™VictoriaMetrics
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