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DBMS > Apache Phoenix vs. atoti vs. Datomic vs. Spark SQL

System Properties Comparison Apache Phoenix vs. atoti vs. Datomic vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonatoti  Xexclude from comparisonDatomic  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseAn in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilitySpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSObject oriented DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score0.56
Rank#245  Overall
#10  Object oriented DBMS
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitephoenix.apache.orgatoti.iowww.datomic.comspark.apache.org/­sql
Technical documentationphoenix.apache.orgdocs.atoti.iodocs.datomic.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationActiveViamCognitectApache Software Foundation
Initial release201420122014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20191.0.6735, June 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infofree versions availablecommercial infolimited edition freeOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaJava, ClojureScala
Server operating systemsLinux
Unix
Windows
All OS with a Java VMLinux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyes
Typing infopredefined data types such as float or dateyesyesyes
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 indexesyesyesno
SQL infoSupport of SQLyesMultidimensional Expressions (MDX)noSQL-like DML and DDL statements
APIs and other access methodsJDBCRESTful HTTP APIJDBC
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
Clojure
Java
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsPythonyes infoTransaction Functionsno
TriggersnoBy using transaction functionsno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding, horizontal partitioningnone infoBut extensive use of caching in the application peersyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
none infoBut extensive use of caching in the application peersnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yesyes
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes inforecommended only for testing and developmentno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancynono

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More resources
Apache PhoenixatotiDatomicSpark SQL
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