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DBMS > Amazon DocumentDB vs. Apache Phoenix vs. Bangdb vs. Spark SQL

System Properties Comparison Amazon DocumentDB vs. Apache Phoenix vs. Bangdb vs. Spark SQL

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonApache Phoenix  Xexclude from comparisonBangdb  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceA scale-out RDBMS with evolutionary schema built on Apache HBaseConverged and high performance database for device data, events, time series, document and graphSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument storeRelational DBMSDocument store
Graph DBMS
Time Series DBMS
Relational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.89
Rank#137  Overall
#24  Document stores
Score2.02
Rank#130  Overall
#63  Relational DBMS
Score0.15
Rank#334  Overall
#45  Document stores
#31  Graph DBMS
#31  Time Series DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Websiteaws.amazon.com/­documentdbphoenix.apache.orgbangdb.comspark.apache.org/­sql
Technical documentationaws.amazon.com/­documentdb/­resourcesphoenix.apache.orgdocs.bangdb.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationSachin Sinha, BangDBApache Software Foundation
Initial release2019201420122014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 2019BangDB 2.0, October 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoBSD 3Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++Scala
Server operating systemshostedLinux
Unix
Windows
LinuxLinux
OS X
Windows
Data schemeschema-freeyes infolate-bound, schema-on-read capabilitiesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes: string, long, double, int, geospatial, stream, eventsyes
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.nononono
Secondary indexesyesyesyes infosecondary, composite, nested, reverse, geospatialno
SQL infoSupport of SQLnoyesSQL like support with command line toolSQL-like DML and DDL statements
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBCProprietary protocol
RESTful HTTP API
JDBC
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
C
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
C#
C++
Java
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsnono
Triggersnonoyes, Notifications (with Streaming only)no
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasMulti-source replication
Source-replica replication
selectable replication factor, Knob for CAP (enterprise version only)none
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)Hadoop integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual ConsistencyTunable consistency, set CAP knob accordingly
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsACIDACIDno
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.yesyes, run db with in-memory only modeno
User concepts infoAccess controlAccess rights for users and rolesAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyyes (enterprise version only)no

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More resources
Amazon DocumentDBApache PhoenixBangdbSpark SQL
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