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DBMS > Apache Druid vs. Postgres-XL vs. PostgreSQL vs. TDSQL for MySQL vs. Vertica

System Properties Comparison Apache Druid vs. Postgres-XL vs. PostgreSQL vs. TDSQL for MySQL vs. Vertica

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonPostgres-XL  Xexclude from comparisonPostgreSQL  Xexclude from comparisonTDSQL for MySQL  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 dataBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresWidely used open source RDBMS infoDeveloped as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQLA high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.Cloud 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 DBMSRelational 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
Spatial DBMS
Document store
Graph DBMS infowith Apache Age
Spatial DBMS
Vector DBMS infowith pgvector extension
Document store
Spatial DBMS
Spatial 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
Score0.49
Rank#256  Overall
#117  Relational DBMS
Score645.54
Rank#4  Overall
#4  Relational DBMS
Score0.85
Rank#205  Overall
#95  Relational DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitedruid.apache.orgwww.postgres-xl.orgwww.postgresql.orgwww.tencentcloud.com/­products/­dcdbwww.vertica.com
Technical documentationdruid.apache.org/­docs/­latest/­designwww.postgres-xl.org/­documentationwww.postgresql.org/­docswww.tencentcloud.com/­document/­product/­1042vertica.com/­documentation
DeveloperApache Software Foundation and contributorsPostgreSQL Global Development Group infowww.postgresql.org/­developerTencentOpenText infopreviously Micro Focus and Hewlett Packard
Initial release20122014 infosince 2012, originally named StormDB1989 info1989: Postgres, 1996: PostgreSQL20132005
Current release29.0.1, April 202410 R1, October 201816.3, May 202412.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoMozilla public licenseOpen Source infoBSDcommercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononoyesno infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
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Implementation languageJavaCCC++
Server operating systemsLinux
OS X
Unix
Linux
macOS
FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
hostedLinux
Data schemeyes infoschema-less columns are supportedyesyesyesYes, 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 dateyesyesyesyesyes
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.noyes infoXML type, but no XML query functionalityyes infospecific XML-type available, but no XML query functionality.nono
Secondary indexesyesyesyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL for queryingyes infodistributed, parallel query executionyes infostandard with numerous extensionsyesFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
ADO.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
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C
C#
C++
Java
PHP
Python
Ruby
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnouser defined functionsuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.yesyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyesyesyesyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedhorizontal partitioningpartitioning by range, list and (since PostgreSQL 11) by hashAutomatic shardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesSource-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 methodsnonononono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoMVCCACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonononono
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-standardfine grained access rights according to SQL-standardUsers with fine-grained authorization conceptfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Apache DruidPostgres-XLPostgreSQLTDSQL for MySQLVertica 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,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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More resources
Apache DruidPostgres-XLPostgreSQLTDSQL for MySQLVertica infoOpenText™ Vertica™
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