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DBMS > Apache Kylin vs. DuckDB vs. Hypertable vs. Tarantool

System Properties Comparison Apache Kylin vs. DuckDB vs. Hypertable vs. Tarantool

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Editorial information provided by DB-Engines
NameApache Kylin  Xexclude from comparisonDuckDB  Xexclude from comparisonHypertable  Xexclude from comparisonTarantool  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionA distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stackAn embeddable, in-process, column-oriented SQL OLAP RDBMSAn open source BigTable implementation based on distributed file systems such as HadoopIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelRelational DBMSRelational DBMSWide column storeDocument store
Key-value store
Relational DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#172  Overall
#79  Relational DBMS
Score4.57
Rank#74  Overall
#40  Relational DBMS
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websitekylin.apache.orgduckdb.orgwww.tarantool.io
Technical documentationkylin.apache.org/­docsduckdb.org/­docswww.tarantool.io/­en/­doc
DeveloperApache Software Foundation, originally contributed from eBay IncHypertable Inc.VK
Initial release2015201820092008
Current release3.1.0, July 20200.10, February 20240.9.8.11, March 20162.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoMIT LicenseOpen Source infoGNU version 3. Commercial license availableOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C++C and C++
Server operating systemsLinuxserver-lessLinux
OS X
Windows infoan inofficial Windows port is available
BSD
Linux
macOS
Data schemeyesyesschema-freeFlexible 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 indexesyesyesrestricted infoonly exact value or prefix value scansyes
SQL infoSupport of SQLANSI SQL for queries (using Apache Calcite)yesnoFull-featured ANSI SQL support
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC
C++ API
Thrift
Open binary protocol
Supported programming languagesC
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
Perl
PHP
Python
Ruby
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresnonoLua, C and SQL stored procedures
Triggersnonoyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding, 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 nodesnoneselectable replication factor on file system levelAsynchronous 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 methodsyesnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate 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 integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID, 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.noyesyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlnonoAccess 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 KylinDuckDBHypertableTarantool
DB-Engines blog posts

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

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