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DBMS > etcd vs. EXASOL vs. Faircom DB vs. IRONdb vs. Spark SQL

System Properties Comparison etcd vs. EXASOL vs. Faircom DB vs. IRONdb vs. Spark SQL

Editorial information provided by DB-Engines
Nameetcd  Xexclude from comparisonEXASOL  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonIRONdb  Xexclude from comparisonSpark SQL  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionA distributed reliable key-value storeHigh-performance, in-memory, MPP database specifically designed for in-memory analytics.Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicitySpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelKey-value storeRelational DBMSKey-value store
Relational DBMS
Time Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.64
Rank#54  Overall
#5  Key-value stores
Score2.25
Rank#120  Overall
#57  Relational DBMS
Score0.24
Rank#311  Overall
#44  Key-value stores
#140  Relational DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Websiteetcd.io
github.com/­etcd-io/­etcd
www.exasol.comwww.faircom.com/­products/­faircom-dbwww.circonus.com/solutions/time-series-database/spark.apache.org/­sql
Technical documentationetcd.io/­docs
github.com/­etcd-io/­etcd/­tree/­master/­Documentation
www.exasol.com/­resourcesdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmldocs.circonus.com/irondb/category/getting-startedspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperExasolFairCom CorporationCirconus LLC.Apache Software Foundation
Initial release2000197920172014
Current release3.4, August 2019V12, November 2020V0.10.20, January 20183.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercial infoRestricted, free version availablecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoANSI C, C++C and C++Scala
Server operating systemsFreeBSD
Linux
Windows infoexperimental
AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
LinuxLinux
OS X
Windows
Data schemeschema-freeyesschema free, schema optional, schema required, partial schema,schema-freeyes
Typing infopredefined data types such as float or datenoyesyes, ANSI SQL Types, JSON, typed binary structuresyes infotext, numeric, histogramsyes
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.nonononono
Secondary indexesnoyesyesnono
SQL infoSupport of SQLnoyesyes, ANSI SQL with proprietary extensionsSQL-like query language (Circonus Analytics Query Language: CAQL)SQL-like DML and DDL statements
APIs and other access methodsgRPC
JSON over HTTP
.Net
JDBC
ODBC
WebSocket
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
HTTP APIJDBC
ODBC
Supported programming languages.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl
Java
Lua
Python
R
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsyes info.Net, JavaScript, C/C++yes, in Luano
Triggersyes, watching key changesyesyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningAutomatic, metric affinity per nodeyes, 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.yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).configurable replication factor, datacenter awarenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoHadoop integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Immediate consistency per node, eventual consistency across nodes
Foreign keys infoReferential integritynoyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDtunable from ACID to Eventually Consistentnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesYes, tunable from durable to delayed durability to in-memoryyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesnono
User concepts infoAccess controlnoAccess rights for users, groups and roles according to SQL-standardFine grained access rights according to SQL-standard with additional protections for filesnono

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More resources
etcdEXASOLFaircom DB infoformerly c-treeACEIRONdbSpark SQL
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