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DBMS > Amazon DocumentDB vs. Spark SQL vs. Sphinx vs. Tarantool

System Properties Comparison Amazon DocumentDB vs. Spark SQL vs. Sphinx vs. Tarantool

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NameAmazon DocumentDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonSphinx  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceSpark SQL is a component on top of 'Spark Core' for structured data processingOpen source search engine for searching in data from different sources, e.g. relational databasesIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelDocument storeRelational DBMSSearch engineDocument store
Key-value store
Relational DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#132  Overall
#24  Document stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websiteaws.amazon.com/­documentdbspark.apache.org/­sqlsphinxsearch.comwww.tarantool.io
Technical documentationaws.amazon.com/­documentdb/­resourcesspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsphinxsearch.com/­docswww.tarantool.io/­en/­doc
DeveloperApache Software FoundationSphinx Technologies Inc.VK
Initial release2019201420012008
Current release3.5.0 ( 2.13), September 20233.5.1, February 20232.10.0, May 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence availableOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC++C and C++
Server operating systemshostedLinux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
BSD
Linux
macOS
Data schemeschema-freeyesyesFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyesyesnostring, double, decimal, uuid, integer, blob, boolean, datetime
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 indexesyesnoyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsSQL-like query language (SphinxQL)Full-featured ANSI SQL support
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBC
ODBC
Proprietary protocolOpen binary protocol
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
Java
Python
R
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresnononoLua, C and SQL stored procedures
Triggersnononoyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesnoneyes, utilizing Spark CoreSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasnonenoneAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)no
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyCasual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsnonoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, cooperative multitasking
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlAccess rights for users and rolesnonoAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources
Amazon DocumentDBSpark SQLSphinxTarantool
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