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DBMS > EsgynDB vs. SQream DB vs. StarRocks vs. Vertica

System Properties Comparison EsgynDB vs. SQream DB vs. StarRocks vs. Vertica

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Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonSQream DB  Xexclude from comparisonStarRocks  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionEnterprise-class SQL-on-Hadoop solution, powered by Apache Trafodiona GPU-based, columnar RDBMS for big data analytics workloadsAn open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics infoForked from Apache DorisCloud 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 modelRelational DBMSRelational DBMSRelational DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial 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
Score0.74
Rank#224  Overall
#103  Relational DBMS
Score0.93
Rank#198  Overall
#93  Relational DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Websitewww.esgyn.cnsqream.comwww.starrocks.iowww.vertica.com
Technical documentationdocs.sqream.comdocs.starrocks.io/­en-us/­latest/­introduction/­StarRocks_introvertica.com/­documentation
DeveloperEsgynSQream TechnologiesThe Linux Foundation infosince Feb 2023OpenText infopreviously Micro Focus and Hewlett Packard
Initial release2015201720202005
Current release2022.1.6, December 20222.5.3, March 202312.0.3, January 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2.0commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononono 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 languageC++, JavaC++, CUDA, Haskell, Java, ScalaC++, JavaC++
Server operating systemsLinuxLinuxLinuxLinux
Data schemeyesyesyesYes, 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 dateyesyes, ANSI Standard SQL Typesyesyes
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 indexesyesnoyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLyesyesyesFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsADO.NET
JDBC
ODBC
.Net
JDBC
ODBC
JDBC
MySQL protocol
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.NetC++
Java
JavaScript (Node.js)
Python
JavaC#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresJava Stored Proceduresuser defined functions in Pythonuser defined functionsyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnononoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal and vertical partitioninghorizontal partitioning (by range and hash)horizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication between multi datacentersnoneMulti-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 methodsyesnonono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID
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-standardRole based access control and fine grained access rightsfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
EsgynDBSQream DBStarRocksVertica 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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EsgynDBSQream DBStarRocksVertica infoOpenText™ Vertica™
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