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DBMS > Google Cloud Bigtable vs. NSDb vs. Tarantool vs. Yanza

System Properties Comparison Google Cloud Bigtable vs. NSDb vs. Tarantool vs. Yanza

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonNSDb  Xexclude from comparisonTarantool  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsTime Series DBMS for IoT Applications
Primary database modelKey-value store
Wide column store
Time Series DBMSDocument store
Key-value store
Relational DBMS
Time Series DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Websitecloud.google.com/­bigtablensdb.iowww.tarantool.ioyanza.com
Technical documentationcloud.google.com/­bigtable/­docsnsdb.io/­Architecturewww.tarantool.io/­en/­doc
DeveloperGoogleVKYanza
Initial release2015201720082015
Current release2.10.0, May 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprisecommercial infofree version available
Cloud-based only infoOnly available as a cloud serviceyesnonono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ScalaC and C++
Server operating systemshostedLinux
macOS
BSD
Linux
macOS
Windows
Data schemeschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columnsschema-free
Typing infopredefined data types such as float or datenoyes: int, bigint, decimal, stringstring, double, decimal, uuid, integer, blob, boolean, datetimeno
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.nononono
Secondary indexesnoall fields are automatically indexedyesno
SQL infoSupport of SQLnoSQL-like query languageFull-featured ANSI SQL supportno
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
gRPC
HTTP REST
WebSocket
Open binary protocolHTTP API
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
Java
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
any language that supports HTTP calls
Server-side scripts infoStored proceduresnonoLua, C and SQL stored proceduresno
Triggersnoyes, before/after data modification events, on replication events, client session eventsyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.none
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)
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual 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
Immediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsnoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsno
Concurrency infoSupport for concurrent manipulation of datayesyesyes, cooperative multitaskingyes
Durability infoSupport for making data persistentyesUsing Apache Luceneyes, write ahead loggingyes
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, 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
no

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More resources
Google Cloud BigtableNSDbTarantoolYanza
DB-Engines blog posts

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

show all

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