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DBMS > Databricks vs. GridGain vs. Postgres-XL vs. Sphinx

System Properties Comparison Databricks vs. GridGain vs. Postgres-XL vs. Sphinx

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonGridGain  Xexclude from comparisonPostgres-XL  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.GridGain is an in-memory computing platform, built on Apache IgniteBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelDocument store
Relational DBMS
Key-value store
Relational DBMS
Relational DBMSSearch engine
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score0.49
Rank#256  Overall
#117  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websitewww.databricks.comwww.gridgain.comwww.postgres-xl.orgsphinxsearch.com
Technical documentationdocs.databricks.comwww.gridgain.com/­docs/­index.htmlwww.postgres-xl.org/­documentationsphinxsearch.com/­docs
DeveloperDatabricksGridGain Systems, Inc.Sphinx Technologies Inc.
Initial release201320072014 infosince 2012, originally named StormDB2001
Current releaseGridGain 8.5.110 R1, October 20183.5.1, February 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoMozilla public licenseOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, C++, .NetCC++
Server operating systemshostedLinux
OS X
Solaris
Windows
Linux
macOS
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesyes
Typing infopredefined data types such as float or dateyesyesno
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.yesyesyes infoXML type, but no XML query functionality
Secondary indexesyesyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLwith Databricks SQLANSI-99 for query and DML statements, subset of DDLyes infodistributed, parallel query executionSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Proprietary protocol
Supported programming languagesPython
R
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes (compute grid and cache interceptors can be used instead)user defined functionsno
Triggersyes (cache interceptors and events)yesno
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioningSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes (replicated cache)none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)nono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infoMVCCno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlSecurity Hooks for custom implementationsfine grained access rights according to SQL-standardno
More information provided by the system vendor
DatabricksGridGainPostgres-XLSphinx
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksGridGainPostgres-XLSphinx
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