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DBMS > Microsoft Azure AI Search vs. Spark SQL vs. Sphinx vs. Tarantool

System Properties Comparison Microsoft Azure AI Search vs. Spark SQL vs. Sphinx vs. Tarantool

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Editorial information provided by DB-Engines
NameMicrosoft Azure AI Search  Xexclude from comparisonSpark SQL  Xexclude from comparisonSphinx  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionSearch-as-a-service for web and mobile app developmentSpark SQL is a component on top of 'Spark Core' for structured data processingOpen source search engine for searching in data from different sources, e.g. relational databasesIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelSearch engineRelational DBMSSearch engineDocument store
Key-value store
Relational DBMS
Secondary database modelsVector DBMSSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.59
Rank#63  Overall
#7  Search engines
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websiteazure.microsoft.com/­en-us/­services/­searchspark.apache.org/­sqlsphinxsearch.comwww.tarantool.io
Technical documentationlearn.microsoft.com/­en-us/­azure/­searchspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsphinxsearch.com/­docswww.tarantool.io/­en/­doc
DeveloperMicrosoftApache Software FoundationSphinx Technologies Inc.VK
Initial release2015201420012008
Current releaseV13.5.0 ( 2.13), September 20233.5.1, February 20232.10.0, May 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence availableOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC++C and C++
Server operating systemshostedLinux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
BSD
Linux
macOS
Data schemeyesyesyesFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyesyesnostring, double, decimal, uuid, integer, blob, boolean, datetime
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.nonono
Secondary indexesyesnoyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsSQL-like query language (SphinxQL)Full-featured ANSI SQL support
APIs and other access methodsRESTful HTTP APIJDBC
ODBC
Proprietary protocolOpen binary protocol
Supported programming languagesC#
Java
JavaScript
Python
Java
Python
R
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresnononoLua, C and SQL stored procedures
Triggersnononoyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceyes, utilizing Spark CoreSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud servicenonenoneAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyCasual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, cooperative multitasking
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlyes infousing Azure authenticationnonoAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources
Microsoft Azure AI SearchSpark SQLSphinxTarantool
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