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DBMS > Badger vs. RavenDB vs. Spark SQL vs. Tarantool

System Properties Comparison Badger vs. RavenDB vs. Spark SQL vs. Tarantool

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonRavenDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Open Source Operational and Transactional Enterprise NoSQL Document DatabaseSpark SQL is a component on top of 'Spark Core' for structured data processingIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelKey-value storeDocument storeRelational DBMSDocument store
Key-value store
Relational DBMS
Secondary database modelsGraph DBMS
Spatial DBMS
Time Series DBMS
Spatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.22
Rank#320  Overall
#47  Key-value stores
Score2.84
Rank#101  Overall
#18  Document stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Websitegithub.com/­dgraph-io/­badgerravendb.netspark.apache.org/­sqlwww.tarantool.io
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerravendb.net/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.tarantool.io/­en/­doc
DeveloperDGraph LabsHibernating RhinosApache Software FoundationVK
Initial release2017201020142008
Current release5.4, July 20223.5.0 ( 2.13), September 20232.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoAGPL version 3, commercial license availableOpen Source infoApache 2.0Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC#ScalaC and C++
Server operating systemsBSD
Linux
OS X
Solaris
Windows
Linux
macOS
Raspberry Pi
Windows
Linux
OS X
Windows
BSD
Linux
macOS
Data schemeschema-freeschema-freeyesFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or datenonoyesstring, 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 indexesnoyesnoyes
SQL infoSupport of SQLnoSQL-like query language (RQL)SQL-like DML and DDL statementsFull-featured ANSI SQL support
APIs and other access methods.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API
JDBC
ODBC
Open binary protocol
Supported programming languagesGo.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresnoyesnoLua, C and SQL stored procedures
Triggersnoyesnoyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesnoneShardingyes, utilizing Spark CoreSharding, 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 nodesnoneMulti-source replicationnoneAsynchronous 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 methodsnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneDefault ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.Casual 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 integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID, Cluster-wide transaction availablenoACID, 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 persistentyesyesyesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlnoAuthorization levels configured per client per databasenoAccess 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
BadgerRavenDBSpark SQLTarantool
DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021,  Vladimir Perepelytsya, Tarantool (sponsor) 

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