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DBMS > Apache Phoenix vs. LMDB vs. Netezza vs. Spark SQL

System Properties Comparison Apache Phoenix vs. LMDB vs. Netezza vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonLMDB  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseA high performant, light-weight, embedded key-value database libraryData warehouse and analytics appliance part of IBM PureSystemsSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.02
Rank#130  Overall
#63  Relational DBMS
Score2.33
Rank#118  Overall
#21  Key-value stores
Score10.18
Rank#46  Overall
#29  Relational DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Websitephoenix.apache.orgwww.symas.com/­symas-embedded-database-lmdbwww.ibm.com/­products/­netezzaspark.apache.org/­sql
Technical documentationphoenix.apache.orgwww.lmdb.tech/­docspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationSymasIBMApache Software Foundation
Initial release2014201120002014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20190.9.32, January 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open SourcecommercialOpen 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 languageJavaCScala
Server operating systemsLinux
Unix
Windows
Linux
Unix
Windows
Linux infoincluded in applianceLinux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeyesyes
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 indexesyesnoyesno
SQL infoSupport of SQLyesnoyesSQL-like DML and DDL statements
APIs and other access methodsJDBCJDBC
ODBC
OLE DB
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl
C
C++
Fortran
Java
Lua
Perl
Python
R
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnoyesno
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesShardingnoneShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
noneSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.yesyesno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancynoUsers with fine-grained authorization conceptno

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More resources
Apache PhoenixLMDBNetezza infoAlso called PureData System for Analytics by IBMSpark SQL
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