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DBMS > DataFS vs. Google Cloud Bigtable vs. Tarantool

System Properties Comparison DataFS vs. Google Cloud Bigtable vs. Tarantool

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Editorial information provided by DB-Engines
NameDataFS  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionAll data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.In-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelObject oriented DBMSKey-value store
Wide column store
Document store
Key-value store
Relational DBMS
Secondary database modelsGraph DBMSSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.06
Rank#354  Overall
#15  Object oriented DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websitenewdatabase.comcloud.google.com/­bigtablewww.tarantool.io
Technical documentationdev.mobiland.com/­Overview.xspcloud.google.com/­bigtable/­docswww.tarantool.io/­en/­doc
DeveloperMobiland AGGoogleVK
Initial release201820152008
Current release1.1.263, October 20222.10.0, May 2022
License infoCommercial or Open SourcecommercialcommercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC and C++
Server operating systemsWindowshostedBSD
Linux
macOS
Data schemeClasses, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)schema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyesnostring, 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 indexesnonoyes
SQL infoSupport of SQLnonoFull-featured ANSI SQL support
APIs and other access methods.NET Client API
Proprietary client DLL
WinRT client
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Open binary protocol
Supported programming languages.Net
C
C#
C++
VB.Net
C#
C++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresnoLua, C and SQL stored procedures
Triggersno, except callback-events from server when changes happenednoyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesProprietary Sharding systemShardingSharding, 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 nodesInternal replication in Colossus, and regional replication between two clusters in different zonesAsynchronous 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 systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)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 integrityyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyes, cooperative multitasking
Durability infoSupport for making data persistentyesyesyes, 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 controlWindows-ProfileAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access 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
DataFSGoogle Cloud BigtableTarantool
DB-Engines blog posts

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

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