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DBMS > Apache Phoenix vs. RocksDB vs. ScyllaDB vs. Splice Machine

System Properties Comparison Apache Phoenix vs. RocksDB vs. ScyllaDB vs. Splice Machine

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonRocksDB  Xexclude from comparisonScyllaDB  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseEmbeddable persistent key-value store optimized for fast storage (flash and RAM)Cassandra and DynamoDB compatible wide column storeOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSKey-value storeWide column storeRelational DBMS
Secondary database modelsKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.90
Rank#125  Overall
#59  Relational DBMS
Score2.84
Rank#97  Overall
#16  Key-value stores
Score4.15
Rank#69  Overall
#5  Wide column stores
Score0.54
Rank#244  Overall
#114  Relational DBMS
Websitephoenix.apache.orgrocksdb.orgwww.scylladb.comsplicemachine.com
Technical documentationphoenix.apache.orggithub.com/­facebook/­rocksdb/­wikidocs.scylladb.comsplicemachine.com/­how-it-works
DeveloperApache Software FoundationFacebook, Inc.ScyllaDBSplice Machine
Initial release2014201320152014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20199.4.0, June 2024ScyllaDB Open Source 6.0.2, July 20243.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoBSDOpen Source infoOpen Source (AGPL), commercial license availableOpen Source infoAGPL 3.0, commercial license available
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++C++Java
Server operating systemsLinux
Unix
Windows
LinuxLinuxLinux
OS X
Solaris
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesnoyesyes
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 indexesyesnoyes infocluster global secondary indicesyes
SQL infoSupport of SQLyesnoSQL-like DML and DDL statements (CQL)yes
APIs and other access methodsJDBCC++ API
Java API
Proprietary protocol (CQL) infocompatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift
JDBC
Native Spark Datasource
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
C++
Go
Java
Perl
Python
Ruby
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
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnoyes, Luayes infoJava
Triggersnonoyes
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioningShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yesselectable replication factor infoRepresentation of geographical distribution of servers is possibleMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnonoYes, via Full Spark Integration
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
Immediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesno infoAtomicity and isolation are supported for single operationsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes infoin-memory tablesyes
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancynoAccess rights for users can be defined per objectAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
Apache PhoenixRocksDBScyllaDBSplice Machine
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 PhoenixRocksDBScyllaDBSplice Machine
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