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DBMS > FeatureBase vs. Google Cloud Bigtable vs. Tarantool vs. Yaacomo

System Properties Comparison FeatureBase vs. Google Cloud Bigtable vs. Tarantool vs. Yaacomo

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Editorial information provided by DB-Engines
NameFeatureBase  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonTarantool  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionReal-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.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 applicationsOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelRelational DBMSKey-value store
Wide column store
Document store
Key-value store
Relational DBMS
Relational DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.31
Rank#292  Overall
#135  Relational DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Websitewww.featurebase.comcloud.google.com/­bigtablewww.tarantool.ioyaacomo.com
Technical documentationdocs.featurebase.comcloud.google.com/­bigtable/­docswww.tarantool.io/­en/­doc
DeveloperMolecula and Pilosa Open Source ContributorsGoogleVKQ2WEB GmbH
Initial release2017201520082009
Current release2022, May 20222.10.0, May 2022
License infoCommercial or Open SourcecommercialcommercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprisecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC and C++
Server operating systemsLinux
macOS
hostedBSD
Linux
macOS
Android
Linux
Windows
Data schemeyesschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columnsyes
Typing infopredefined data types such as float or dateyesnostring, double, decimal, uuid, integer, blob, boolean, datetimeyes
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 indexesnonoyesyes
SQL infoSupport of SQLSQL queriesnoFull-featured ANSI SQL supportyes
APIs and other access methodsgRPC
JDBC
Kafka Connector
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Open binary protocolJDBC
ODBC
Supported programming languagesJava
Python
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
Triggersnonoyes, before/after data modification events, on replication events, client session eventsyes
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.horizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesInternal 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)
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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
Immediate Consistency
Foreign keys infoReferential integrityyesnoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesAtomic single-row operationsACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, cooperative multitaskingyes
Durability infoSupport for making data persistentyes, using Linux fsyncyesyes, write ahead loggingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes, full featured in-memory storage engine with persistenceyes
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
fine grained access rights according to SQL-standard

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More resources
FeatureBaseGoogle Cloud BigtableTarantoolYaacomo
DB-Engines blog posts

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

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