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DBMS > Amazon DocumentDB vs. Databricks vs. Sphinx vs. Splice Machine

System Properties Comparison Amazon DocumentDB vs. Databricks vs. Sphinx vs. Splice Machine

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonDatabricks  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Open source search engine for searching in data from different sources, e.g. relational databasesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument storeDocument store
Relational DBMS
Search engineRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#131  Overall
#24  Document stores
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websiteaws.amazon.com/­documentdbwww.databricks.comsphinxsearch.comsplicemachine.com
Technical documentationaws.amazon.com/­documentdb/­resourcesdocs.databricks.comsphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperDatabricksSphinx Technologies Inc.Splice Machine
Initial release2019201320012014
Current release3.5.1, February 20233.1, March 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java
Server operating systemshostedhostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeschema-freeFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or dateyesnoyes
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.noyes
Secondary indexesyesyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLnowith Databricks SQLSQL-like query language (SphinxQL)yes
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBC
ODBC
RESTful HTTP API
Proprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
Python
R
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functions and aggregatesnoyes infoJava
Triggersnonoyes
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasyesnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)noYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlAccess rights for users and rolesnoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
Amazon DocumentDBDatabricksSphinxSplice Machine
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Amazon DocumentDBDatabricksSphinxSplice Machine
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