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DBMS > Databricks vs. HugeGraph vs. InfinityDB vs. Microsoft Azure AI Search vs. Sphinx

System Properties Comparison Databricks vs. HugeGraph vs. InfinityDB vs. Microsoft Azure AI Search vs. Sphinx

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHugeGraph  Xexclude from comparisonInfinityDB  Xexclude from comparisonMicrosoft Azure AI Search  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.A fast-speed and highly-scalable Graph DBMSA Java embedded Key-Value Store which extends the Java Map interfaceSearch-as-a-service for web and mobile app developmentOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelDocument store
Relational DBMS
Graph DBMSKey-value storeSearch engineSearch engine
Secondary database modelsVector 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
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score0.00
Rank#378  Overall
#57  Key-value stores
Score5.59
Rank#63  Overall
#7  Search engines
Score5.98
Rank#56  Overall
#5  Search engines
Websitewww.databricks.comgithub.com/­hugegraph
hugegraph.apache.org
boilerbay.comazure.microsoft.com/­en-us/­services/­searchsphinxsearch.com
Technical documentationdocs.databricks.comhugegraph.apache.org/­docsboilerbay.com/­infinitydb/­manuallearn.microsoft.com/­en-us/­azure/­searchsphinxsearch.com/­docs
DeveloperDatabricksBaiduBoiler Bay Inc.MicrosoftSphinx Technologies Inc.
Initial release20132018200220152001
Current release0.94.0V13.5.1, February 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercialcommercialOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesnonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC++
Server operating systemshostedLinux
macOS
Unix
All OS with a Java VMhostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyes infonested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgradeyesyes
Typing infopredefined data types such as float or dateyesyes infoall Java primitives, Date, CLOB, BLOB, huge sparse arraysyesno
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.yesnonono
Secondary indexesyesyes infoalso supports composite index and range indexno infomanual creation possible, using inversions based on multi-value capabilityyesyes infofull-text index on all search fields
SQL infoSupport of SQLwith Databricks SQLnononoSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP API
TinkerPop Gremlin
Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/­docs/­ItemSpaceDataStructures.htm)
RESTful HTTP APIProprietary protocol
Supported programming languagesPython
R
Scala
Groovy
Java
Python
JavaC#
Java
JavaScript
Python
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresuser defined functions and aggregatesasynchronous Gremlin script jobsnonono
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesyes infodepending on used storage backend, e.g. Cassandra and HBasenoneSharding infoImplicit feature of the cloud serviceSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes infodepending on used storage backend, e.g. Cassandra and HBasenoneyes infoImplicit feature of the cloud servicenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsvia hugegraph-sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate Consistency infoREAD-COMMITTED or SERIALIZEDImmediate Consistency
Foreign keys infoReferential integrityyes infoedges in graphno infomanual creation possible, using inversions based on multi-value capabilitynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infoOptimistic locking for transactions; no isolation for bulk loadsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes 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.noyesnono
User concepts infoAccess controlUsers, roles and permissionsnoyes infousing Azure authenticationno
More information provided by the system vendor
DatabricksHugeGraphInfinityDBMicrosoft Azure AI SearchSphinx
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksHugeGraphInfinityDBMicrosoft Azure AI SearchSphinx
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