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DBMS > Dgraph vs. EsgynDB vs. IBM Db2 Event Store vs. Vertica

System Properties Comparison Dgraph vs. EsgynDB vs. IBM Db2 Event Store vs. Vertica

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Editorial information provided by DB-Engines
NameDgraph  Xexclude from comparisonEsgynDB  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionDistributed and scalable native Graph DBMSEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionDistributed Event Store optimized for Internet of Things use casesCloud 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 modelGraph DBMSRelational DBMSEvent Store
Time Series DBMS
Relational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.39
Rank#155  Overall
#15  Graph DBMS
Score0.15
Rank#325  Overall
#144  Relational DBMS
Score0.18
Rank#315  Overall
#2  Event Stores
#26  Time Series DBMS
Score9.62
Rank#42  Overall
#26  Relational DBMS
Websitedgraph.iowww.esgyn.cnwww.ibm.com/­products/­db2-event-storewww.vertica.com
Technical documentationdgraph.io/­docswww.ibm.com/­docs/­en/­db2-event-storevertica.com/­documentation
DeveloperDgraph Labs, Inc.EsgynIBMOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2016201520172005
Current release2.012.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercial infofree developer edition availablecommercial 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 languageGoC++, JavaC and C++C++
Server operating systemsLinux
OS X
Windows
LinuxLinux infoLinux, macOS, Windows for the developer additionLinux
Data schemeschema-freeyesyesYes, 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 dateyesyesyesyes
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.nononono
Secondary indexesyesyesnoNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoyesyes infothrough the embedded Spark runtimeFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsGraphQL query language
gRPC (using protocol buffers) API
HTTP API
ADO.NET
JDBC
ODBC
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
All languages supporting JDBC/ODBC/ADO.NetC
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoJava Stored Proceduresyesyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnononoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesyesShardingShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSynchronous replication via RaftMulti-source replication between multi datacentersActive-active shard replicationMulti-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 methodsnoyesnono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesNo - written data is immutableyes
Durability infoSupport for making data persistentyesyesYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlno infoPlanned for future releasesfine 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

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More resources
DgraphEsgynDBIBM Db2 Event StoreVertica infoOpenText™ Vertica™
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