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DBMS > etcd vs. Spark SQL vs. Tarantool vs. Yanza

System Properties Comparison etcd vs. Spark SQL vs. Tarantool vs. Yanza

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Editorial information provided by DB-Engines
Nameetcd  Xexclude from comparisonSpark SQL  Xexclude from comparisonTarantool  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionA distributed reliable key-value storeSpark SQL is a component on top of 'Spark Core' for structured data processingIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsTime Series DBMS for IoT Applications
Primary database modelKey-value storeRelational DBMSDocument store
Key-value store
Relational DBMS
Time Series DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.03
Rank#54  Overall
#5  Key-value stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Websiteetcd.io
github.com/­etcd-io/­etcd
spark.apache.org/­sqlwww.tarantool.ioyanza.com
Technical documentationetcd.io/­docs
github.com/­etcd-io/­etcd/­tree/­master/­Documentation
spark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.tarantool.io/­en/­doc
DeveloperApache Software FoundationVKYanza
Initial release201420082015
Current release3.4, August 20193.5.0 ( 2.13), September 20232.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache 2.0Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprisecommercial infofree version available
Cloud-based only infoOnly available as a cloud servicenononono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoScalaC and C++
Server operating systemsFreeBSD
Linux
Windows infoexperimental
Linux
OS X
Windows
BSD
Linux
macOS
Windows
Data schemeschema-freeyesFlexible data schema: relational definition for tables with ability to store json-like documents in columnsschema-free
Typing infopredefined data types such as float or datenoyesstring, double, decimal, uuid, integer, blob, boolean, datetimeno
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 indexesnonoyesno
SQL infoSupport of SQLnoSQL-like DML and DDL statementsFull-featured ANSI SQL supportno
APIs and other access methodsgRPC
JSON over HTTP
JDBC
ODBC
Open binary protocolHTTP API
Supported programming languages.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl
Java
Python
R
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
any language that supports HTTP calls
Server-side scripts infoStored proceduresnonoLua, C and SQL stored proceduresno
Triggersyes, watching key changesnoyes, before/after data modification events, on replication events, client session eventsyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark CoreSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.none
Replication methods infoMethods for redundantly storing data on multiple nodesUsing Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.noneAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
none
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
Immediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsno
Concurrency infoSupport for concurrent manipulation of datayesyesyes, cooperative multitaskingyes
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 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
no

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More resources
etcdSpark SQLTarantoolYanza
DB-Engines blog posts

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

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