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DBMS > Prometheus vs. Quasardb vs. Spark SQL vs. Tarantool

System Properties Comparison Prometheus vs. Quasardb vs. Spark SQL vs. Tarantool

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Editorial information provided by DB-Engines
NamePrometheus  Xexclude from comparisonQuasardb  Xexclude from comparisonSpark SQL  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionOpen-source Time Series DBMS and monitoring systemDistributed, high-performance timeseries 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 modelTime Series DBMSTime Series DBMSRelational DBMSDocument 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
Score7.69
Rank#50  Overall
#3  Time Series DBMS
Score0.21
Rank#322  Overall
#29  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Websiteprometheus.ioquasar.aispark.apache.org/­sqlwww.tarantool.io
Technical documentationprometheus.io/­docsdoc.quasar.ai/­masterspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.tarantool.io/­en/­doc
DeveloperquasardbApache Software FoundationVK
Initial release2015200920142008
Current release3.14.1, January 20243.5.0 ( 2.13), September 20232.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0commercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licensesOpen 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 systemsLinux
Windows
BSD
Linux
OS X
Windows
Linux
OS X
Windows
BSD
Linux
macOS
Data schemeyesschema-freeyesFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateNumeric data onlyyes infointeger and binaryyesstring, 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.no infoImport of XML data possiblenonono
Secondary indexesnoyes infowith tagsnoyes
SQL infoSupport of SQLnoSQL-like query languageSQL-like DML and DDL statementsFull-featured ANSI SQL support
APIs and other access methodsRESTful HTTP/JSON APIHTTP APIJDBC
ODBC
Open binary protocol
Supported programming languages.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Java
Python
R
Scala
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 nodesShardingSharding infoconsistent hashingyes, 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 nodesyes infoby FederationSource-replica replication with selectable replication factornoneAsynchronous 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 methodsnowith Hadoop integration
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate 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 integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID, 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 persistentyesyes infoby using LevelDByesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoTransient modenoyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlnoCryptographically strong user authentication and audit trailnoAccess 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
PrometheusQuasardbSpark 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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