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DBMS > Apache IoTDB vs. DuckDB vs. Microsoft Azure AI Search vs. Tarantool

System Properties Comparison Apache IoTDB vs. DuckDB vs. Microsoft Azure AI Search vs. Tarantool

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDuckDB  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkAn embeddable, in-process, column-oriented SQL OLAP RDBMSSearch-as-a-service for web and mobile app developmentIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelTime Series DBMSRelational 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
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score4.57
Rank#74  Overall
#40  Relational DBMS
Score5.59
Rank#63  Overall
#7  Search engines
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websiteiotdb.apache.orgduckdb.orgazure.microsoft.com/­en-us/­services/­searchwww.tarantool.io
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlduckdb.org/­docslearn.microsoft.com/­en-us/­azure/­searchwww.tarantool.io/­en/­doc
DeveloperApache Software FoundationMicrosoftVK
Initial release2018201820152008
Current release1.1.0, April 20230.10, February 2024V12.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoMIT LicensecommercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C and C++
Server operating systemsAll OS with a Java VM (>= 1.8)server-lesshostedBSD
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 dateyesyesyesstring, 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.nononono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like query languageyesnoFull-featured ANSI SQL support
APIs and other access methodsJDBC
Native API
Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC
RESTful HTTP APIOpen binary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C# info3rd party driver
C++
Crystal info3rd party driver
Go info3rd party driver
Java
Lisp info3rd party driver
Python
R
Ruby info3rd party driver
Rust
Swift
Zig info3rd party driver
C#
Java
JavaScript
Python
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresyesnonoLua, C and SQL stored procedures
Triggersyesnonoyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneSharding infoImplicit feature of the cloud serviceSharding, 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 nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasnoneyes infoImplicit feature of the cloud serviceAsynchronous 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 methodsIntegration with Hadoop and Sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate 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 datanoACIDnoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yesyes, cooperative multitasking
Durability infoSupport for making data persistentyesyesyesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnoyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlyesnoyes infousing Azure authenticationAccess 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
Apache IoTDBDuckDBMicrosoft Azure AI SearchTarantool
DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021,  Vladimir Perepelytsya, Tarantool (sponsor) 

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