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DBMS > BoltDB vs. etcd vs. Tarantool vs. Vitess

System Properties Comparison BoltDB vs. etcd vs. Tarantool vs. Vitess

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Editorial information provided by DB-Engines
NameBoltDB  Xexclude from comparisonetcd  Xexclude from comparisonTarantool  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionAn embedded key-value store for Go.A distributed reliable key-value storeIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelKey-value storeKey-value storeDocument store
Key-value store
Relational DBMS
Relational DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extensionDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.80
Rank#215  Overall
#31  Key-value stores
Score7.03
Rank#54  Overall
#5  Key-value stores
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Score0.88
Rank#203  Overall
#95  Relational DBMS
Websitegithub.com/­boltdb/­boltetcd.io
github.com/­etcd-io/­etcd
www.tarantool.iovitess.io
Technical documentationetcd.io/­docs
github.com/­etcd-io/­etcd/­tree/­master/­Documentation
www.tarantool.io/­en/­docvitess.io/­docs
DeveloperVKThe Linux Foundation, PlanetScale
Initial release201320082013
Current release3.4, August 20192.10.0, May 202215.0.2, December 2022
License infoCommercial or Open SourceOpen Source infoMIT LicenseOpen Source infoApache Version 2.0Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool EnterpriseOpen Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoGoC and C++Go
Server operating systemsBSD
Linux
OS X
Solaris
Windows
FreeBSD
Linux
Windows infoexperimental
BSD
Linux
macOS
Docker
Linux
macOS
Data schemeschema-freeschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columnsyes
Typing infopredefined data types such as float or datenonostring, double, decimal, uuid, integer, blob, boolean, datetimeyes
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 indexesnonoyesyes
SQL infoSupport of SQLnonoFull-featured ANSI SQL supportyes infowith proprietary extensions
APIs and other access methodsgRPC
JSON over HTTP
Open binary protocolADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesGo.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnonoLua, C and SQL stored proceduresyes infoproprietary syntax
Triggersnoyes, watching key changesyes, before/after data modification events, on replication events, client session eventsyes
Partitioning methods infoMethods for storing different data on different nodesnoneSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.Sharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneUsing Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate 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
Eventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynonoyesyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyes, cooperative multitaskingyes infotable locks or row locks depending on storage engine
Durability infoSupport for making data persistentyesyesyes, write ahead loggingyes
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 persistenceyes
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
Users with fine-grained authorization concept infono user groups or roles

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More resources
BoltDBetcdTarantoolVitess
DB-Engines blog posts

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

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