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DBMS > etcd vs. FatDB vs. LevelDB vs. Spark SQL

System Properties Comparison etcd vs. FatDB vs. LevelDB vs. Spark SQL

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Editorial information provided by DB-Engines
Nameetcd  Xexclude from comparisonFatDB  Xexclude from comparisonLevelDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionA distributed reliable key-value storeA .NET NoSQL DBMS that can integrate with and extend SQL Server.Embeddable fast key-value storage library that provides an ordered mapping from string keys to string valuesSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelKey-value storeDocument store
Key-value store
Key-value storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.25
Rank#54  Overall
#5  Key-value stores
Score2.35
Rank#111  Overall
#19  Key-value stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteetcd.io
github.com/­etcd-io/­etcd
github.com/­google/­leveldbspark.apache.org/­sql
Technical documentationetcd.io/­docs
github.com/­etcd-io/­etcd/­tree/­master/­Documentation
github.com/­google/­leveldb/­blob/­main/­doc/­index.mdspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperFatCloudGoogleApache Software Foundation
Initial release201220112014
Current release3.4, August 20191.23, February 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoBSDOpen Source infoApache 2.0
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 languageGoC#C++Scala
Server operating systemsFreeBSD
Linux
Windows infoexperimental
WindowsIllumos
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeschema-freeschema-freeschema-freeyes
Typing infopredefined data types such as float or datenoyesnoyes
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 indexesnoyesnono
SQL infoSupport of SQLnono infoVia inetgration in SQL ServernoSQL-like DML and DDL statements
APIs and other access methodsgRPC
JSON over HTTP
.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
Supported programming languages.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl
C#C++
Go
Java info3rd party binding
JavaScript (Node.js) info3rd party binding
Python info3rd party binding
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoyes infovia applicationsnono
Triggersyes, watching key changesyes infovia applicationsnono
Partitioning methods infoMethods for storing different data on different nodesShardingnoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesUsing Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.selectable replication factornonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infowith automatic compression on writesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlnono infoCan implement custom security layer via applicationsnono

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More resources
etcdFatDBLevelDBSpark SQL
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