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DBMS > CrateDB vs. Microsoft Azure AI Search vs. Sphinx vs. Warp 10

System Properties Comparison CrateDB vs. Microsoft Azure AI Search vs. Sphinx vs. Warp 10

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonSphinx  Xexclude from comparisonWarp 10  Xexclude from comparison
DescriptionDistributed Database based on LuceneSearch-as-a-service for web and mobile app developmentOpen source search engine for searching in data from different sources, e.g. relational databasesTimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Search engineSearch engineTime Series DBMS
Secondary database modelsRelational DBMSVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score5.59
Rank#63  Overall
#7  Search engines
Score5.98
Rank#56  Overall
#5  Search engines
Score0.07
Rank#349  Overall
#32  Time Series DBMS
Websitecratedb.comazure.microsoft.com/­en-us/­services/­searchsphinxsearch.comwww.warp10.io
Technical documentationcratedb.com/­docslearn.microsoft.com/­en-us/­azure/­searchsphinxsearch.com/­docswww.warp10.io/­content/­02_Getting_started
DeveloperCrateMicrosoftSphinx Technologies Inc.SenX
Initial release2013201520012015
Current releaseV13.5.1, February 2023
License infoCommercial or Open SourceOpen SourcecommercialOpen Source infoGPL version 2, commercial licence availableOpen Source infoApache License 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJavaC++Java
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesschema-free
Typing infopredefined data types such as float or dateyesyesnoyes
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 indexesyesyesyes infofull-text index on all search fieldsno
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitynoSQL-like query language (SphinxQL)no
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
RESTful HTTP APIProprietary protocolHTTP API
Jupyter
WebSocket
Supported programming languages.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
C#
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 (Javascript)nonoyes infoWarpScript
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud serviceSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelyes infoImplicit feature of the cloud servicenoneselectable replication factor infobased on HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate Consistency infobased on HBase
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infounique row identifiers can be used for implementing an optimistic concurrency control strategynonono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlrights management via user accountsyes infousing Azure authenticationnoMandatory use of cryptographic tokens, containing fine-grained authorizations
More information provided by the system vendor
CrateDBMicrosoft Azure AI SearchSphinxWarp 10
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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CrateDBMicrosoft Azure AI SearchSphinxWarp 10
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