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DBMS > Apache Druid vs. EsgynDB vs. LokiJS vs. Vertica

System Properties Comparison Apache Druid vs. EsgynDB vs. LokiJS vs. Vertica

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonEsgynDB  Xexclude from comparisonLokiJS  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionIn-memory JavaScript DBMSCloud 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 DBMS
Time Series DBMS
Relational DBMSDocument storeRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.34
Rank#88  Overall
#48  Relational DBMS
#7  Time Series DBMS
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score0.43
Rank#264  Overall
#42  Document stores
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitedruid.apache.orgwww.esgyn.cngithub.com/­techfort/­LokiJSwww.vertica.com
Technical documentationdruid.apache.org/­docs/­latest/­designtechfort.github.io/­LokiJSvertica.com/­documentation
DeveloperApache Software Foundation and contributorsEsgynOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2012201520142005
Current release29.0.1, April 202412.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Sourcecommercial 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 languageJavaC++, JavaJavaScriptC++
Server operating systemsLinux
OS X
Unix
Linuxserver-less, requires a JavaScript environment (browser, Node.js)Linux
Data schemeyes infoschema-less columns are supportedyesschema-freeYes, 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 dateyesyesnoyes
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 indexesyesyesyes infovia viewsNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL for queryingyesnoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
JavaScript APIADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
All languages supporting JDBC/ODBC/ADO.NetJavaScriptC#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoJava Stored ProceduresView functions in JavaScriptyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnonoyesyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingnonehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesMulti-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 methodsnoyesyesno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencynoneImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno infoatomic operations within a single collection possibleACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes infoSerialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemfine grained access rights according to SQL-standardnofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Apache DruidEsgynDBLokiJSVertica infoOpenText™ Vertica™
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...
» more
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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More resources
Apache DruidEsgynDBLokiJSVertica infoOpenText™ Vertica™
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