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DBMS > Apache Impala vs. Apache Phoenix vs. Bangdb vs. Solr

System Properties Comparison Apache Impala vs. Apache Phoenix vs. Bangdb vs. Solr

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonApache Phoenix  Xexclude from comparisonBangdb  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopA scale-out RDBMS with evolutionary schema built on Apache HBaseConverged and high performance database for device data, events, time series, document and graphA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSRelational DBMSDocument store
Graph DBMS
Time Series DBMS
Search engine
Secondary database modelsDocument storeSpatial DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score0.16
Rank#338  Overall
#47  Document stores
#32  Graph DBMS
#31  Time Series DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websiteimpala.apache.orgphoenix.apache.orgbangdb.comsolr.apache.org
Technical documentationimpala.apache.org/­impala-docs.htmlphoenix.apache.orgdocs.bangdb.comsolr.apache.org/­resources.html
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationSachin Sinha, BangDBApache Software Foundation
Initial release2013201420122006
Current release4.1.0, June 20225.0-HBase2, July 2018 and 4.15-HBase1, December 2019BangDB 2.0, October 20219.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0Open Source infoBSD 3Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaC, C++Java
Server operating systemsLinuxLinux
Unix
Windows
LinuxAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyes infolate-bound, schema-on-read capabilitiesschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyes: string, long, double, int, geospatial, stream, eventsyes infosupports customizable data types and automatic typing
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.nononoyes
Secondary indexesyesyesyes infosecondary, composite, nested, reverse, geospatialyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSQL like support with command line toolSolr Parallel SQL Interface
APIs and other access methodsJDBC
ODBC
JDBCProprietary protocol
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
C#
C++
Java
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functionsnoJava plugins
Triggersnonoyes, Notifications (with Streaming only)yes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication
Source-replica replication
selectable replication factor, Knob for CAP (enterprise version only)yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceHadoop integrationnospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual ConsistencyTunable consistency, set CAP knob accordinglyEventual Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyes, optimistic concurrency controlyes
Durability infoSupport for making data persistentyesyesyes, implements WAL (Write ahead log) as wellyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes, run db with in-memory only modeyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyyes (enterprise version only)yes

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More resources
Apache ImpalaApache PhoenixBangdbSolr
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