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DBMS > Apache Phoenix vs. EJDB vs. Google Cloud Spanner vs. Splice Machine

System Properties Comparison Apache Phoenix vs. EJDB vs. Google Cloud Spanner vs. Splice Machine

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonEJDB  Xexclude from comparisonGoogle Cloud Spanner  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseEmbeddable document-store database library with JSON representation of queries (in MongoDB style)A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSDocument storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score0.31
Rank#296  Overall
#44  Document stores
Score2.84
Rank#100  Overall
#51  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitephoenix.apache.orggithub.com/­Softmotions/­ejdbcloud.google.com/­spannersplicemachine.com
Technical documentationphoenix.apache.orggithub.com/­Softmotions/­ejdb/­blob/­master/­README.mdcloud.google.com/­spanner/­docssplicemachine.com/­how-it-works
DeveloperApache Software FoundationSoftmotionsGoogleSplice Machine
Initial release2014201220172014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20193.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoGPLv2commercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCJava
Server operating systemsLinux
Unix
Windows
server-lesshostedLinux
OS X
Solaris
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes 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.nono
Secondary indexesyesnoyesyes
SQL infoSupport of SQLyesnoyes infoQuery statements complying to ANSI 2011yes
APIs and other access methodsJDBCin-process shared librarygRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
JDBC
Native Spark Datasource
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby
Go
Java
JavaScript (Node.js)
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnonoyes infoJava
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
noneMulti-source replication with 3 replicas for regional instances.Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnoyes infousing Google Cloud DataflowYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynono infotypically not needed, however similar functionality with collection joins possibleyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integrityyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID infoStrict serializable isolationACID
Concurrency infoSupport for concurrent manipulation of datayesyes infoRead/Write Lockingyesyes, 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.yesnoyes
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancynoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and roles according to SQL-standard

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More resources
Apache PhoenixEJDBGoogle Cloud SpannerSplice Machine
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