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

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

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHugeGraph  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonQuestDB  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 DBMSSearch-as-a-service for web and mobile app developmentA high performance open source SQL database for time series dataOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelDocument store
Relational DBMS
Graph DBMSSearch engineTime Series DBMSSearch engine
Secondary database modelsVector DBMSRelational 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
Score5.59
Rank#63  Overall
#7  Search engines
Score2.52
Rank#109  Overall
#9  Time Series DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websitewww.databricks.comgithub.com/­hugegraph
hugegraph.apache.org
azure.microsoft.com/­en-us/­services/­searchquestdb.iosphinxsearch.com
Technical documentationdocs.databricks.comhugegraph.apache.org/­docslearn.microsoft.com/­en-us/­azure/­searchquestdb.io/­docssphinxsearch.com/­docs
DeveloperDatabricksBaiduMicrosoftQuestDB Technology IncSphinx Technologies Inc.
Initial release20132018201520142001
Current release0.9V13.5.1, February 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercialOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava (Zero-GC), C++, RustC++
Server operating systemshostedLinux
macOS
Unix
hostedLinux
macOS
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesyes infoschema-free via InfluxDB Line Protocolyes
Typing infopredefined data types such as float or dateyesyesyesno
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 indexyesnoyes infofull-text index on all search fields
SQL infoSupport of SQLwith Databricks SQLnonoSQL with time-series extensionsSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP API
TinkerPop Gremlin
RESTful HTTP APIHTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
Proprietary protocol
Supported programming languagesPython
R
Scala
Groovy
Java
Python
C#
Java
JavaScript
Python
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
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 HBaseSharding infoImplicit feature of the cloud servicehorizontal partitioning (by timestamps)Sharding 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 HBaseyes infoImplicit feature of the cloud serviceSource-replica replication with eventual consistencynone
MapReduce infoOffers an API for user-defined Map/Reduce methodsvia hugegraph-sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infoedges in graphnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID for single-table writesno
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.noyesnoyes infothrough memory mapped files
User concepts infoAccess controlUsers, roles and permissionsyes infousing Azure authenticationno
More information provided by the system vendor
DatabricksHugeGraphMicrosoft Azure AI SearchQuestDBSphinx
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
» more
Relational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
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