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DBMS > Apache Phoenix vs. ScyllaDB vs. Spark SQL vs. XTDB

System Properties Comparison Apache Phoenix vs. ScyllaDB vs. Spark SQL vs. XTDB

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonScyllaDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonXTDB infoformerly named Crux  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseCassandra and DynamoDB compatible wide column storeSpark SQL is a component on top of 'Spark Core' for structured data processingA general purpose database with bitemporal SQL and Datalog and graph queries
Primary database modelRelational DBMSWide column storeRelational DBMSDocument store
Secondary database modelsKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score4.75
Rank#68  Overall
#5  Wide column stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.11
Rank#343  Overall
#46  Document stores
Websitephoenix.apache.orgwww.scylladb.comspark.apache.org/­sqlgithub.com/­xtdb/­xtdb
www.xtdb.com
Technical documentationphoenix.apache.orgdocs.scylladb.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.xtdb.com/­docs
DeveloperApache Software FoundationScyllaDBApache Software FoundationJuxt Ltd.
Initial release2014201520142019
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 2019ScyllaDB Open Source 5.4.1, January 20243.5.0 ( 2.13), September 20231.19, September 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoOpen Source (AGPL), commercial license availableOpen Source infoApache 2.0Open Source infoMIT License
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Scylla Cloud: Create real-time applications that run at global scale with Scylla Cloud, the industry’s most powerful NoSQL DBaaS
Implementation languageJavaC++ScalaClojure
Server operating systemsLinux
Unix
Windows
LinuxLinux
OS X
Windows
All OS with a Java 8 (and higher) VM
Linux
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyesyesyes, extensible-data-notation format
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 indexesyesyes infocluster global secondary indicesnoyes
SQL infoSupport of SQLyesSQL-like DML and DDL statements (CQL)SQL-like DML and DDL statementslimited SQL, making use of Apache Calcite
APIs and other access methodsJDBCProprietary protocol (CQL) infocompatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift
JDBC
ODBC
HTTP REST
JDBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby
Java
Python
R
Scala
Clojure
Java
Server-side scripts infoStored proceduresuser defined functionsyes, Luanono
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
selectable replication factor infoRepresentation of geographical distribution of servers is possiblenoneyes, each node contains all data
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual Consistency
Tunable Consistency infocan be individually decided for each write operation
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno infoAtomicity and isolation are supported for single operationsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoin-memory tablesno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users can be defined per objectno
More information provided by the system vendor
Apache PhoenixScyllaDBSpark SQLXTDB infoformerly named Crux
Specific characteristicsScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
» more
Competitive advantagesHighly-performant (efficiently utilizes full resources of a node and network; millions...
» more
Typical application scenariosScyllaDB is ideal for applications that require high throughput and low latency at...
» more
Key customersDiscord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
» more
Market metricsScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
» more
Licensing and pricing modelsScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
» more

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Apache PhoenixScyllaDBSpark SQLXTDB infoformerly named Crux
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