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DBMS > Amazon Neptune vs. Apache Phoenix vs. EJDB vs. Spark SQL vs. VoltDB

System Properties Comparison Amazon Neptune vs. Apache Phoenix vs. EJDB vs. Spark SQL vs. VoltDB

Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonApache Phoenix  Xexclude from comparisonEJDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonVoltDB  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudA scale-out RDBMS with evolutionary schema built on Apache HBaseEmbeddable document-store database library with JSON representation of queries (in MongoDB style)Spark SQL is a component on top of 'Spark Core' for structured data processingDistributed In-Memory NewSQL RDBMS infoUsed for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory
Primary database modelGraph DBMS
RDF store
Relational DBMSDocument storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score0.31
Rank#296  Overall
#44  Document stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score1.47
Rank#157  Overall
#73  Relational DBMS
Websiteaws.amazon.com/­neptunephoenix.apache.orggithub.com/­Softmotions/­ejdbspark.apache.org/­sqlwww.voltdb.com
Technical documentationaws.amazon.com/­neptune/­developer-resourcesphoenix.apache.orggithub.com/­Softmotions/­ejdb/­blob/­master/­README.mdspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.voltdb.com
DeveloperAmazonApache Software FoundationSoftmotionsApache Software FoundationVoltDB Inc.
Initial release20172014201220142010
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20193.5.0 ( 2.13), September 202311.3, April 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoGPLv2Open Source infoApache 2.0Open Source infoAGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCScalaJava, C++
Server operating systemshostedLinux
Unix
Windows
server-lessLinux
OS X
Windows
Linux
OS X infofor development
Data schemeschema-freeyes infolate-bound, schema-on-read capabilitiesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyes infostring, integer, double, bool, date, object_idyesyes
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 indexesnoyesnonoyes
SQL infoSupport of SQLnoyesnoSQL-like DML and DDL statementsyes infoonly a subset of SQL 99
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
JDBCin-process shared libraryJDBC
ODBC
Java API
JDBC
RESTful HTTP/JSON API
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
C
C#
C++
Go
Groovy
Java
PHP
Python
Scala
Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby
Java
Python
R
Scala
C#
C++
Erlang infonot officially supported
Go
Java
JavaScript infoNode.js
PHP
Python
Server-side scripts infoStored proceduresnouser defined functionsnonoJava
Triggersnonononono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingnoneyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.Multi-source replication
Source-replica replication
nonenoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoHadoop integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency
Foreign keys infoReferential integrityyes infoRelationships in graphsnono infotypically not needed, however similar functionality with collection joins possiblenono infoFOREIGN KEY constraints are not supported
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnonoACID infoTransactions are executed single-threaded within stored procedures
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoRead/Write Lockingyesyes infoData access is serialized by the server
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyesyes infoSnapshots and command logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancynonoUsers and roles with access to stored procedures

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More resources
Amazon NeptuneApache PhoenixEJDBSpark SQLVoltDB
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