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DBMS > BoltDB vs. GeoSpock vs. Google Cloud Bigtable vs. Tarantool

System Properties Comparison BoltDB vs. GeoSpock vs. Google Cloud Bigtable vs. Tarantool

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Editorial information provided by DB-Engines
NameBoltDB  Xexclude from comparisonGeoSpock  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonTarantool  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionAn embedded key-value store for Go.Spatial and temporal data processing engine for extreme data scaleGoogle'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 modelKey-value storeRelational DBMSKey-value store
Wide column store
Document store
Key-value store
Relational DBMS
Secondary database modelsTime Series DBMSSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.80
Rank#215  Overall
#31  Key-value stores
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
Websitegithub.com/­boltdb/­boltgeospock.comcloud.google.com/­bigtablewww.tarantool.io
Technical documentationcloud.google.com/­bigtable/­docswww.tarantool.io/­en/­doc
DeveloperGeoSpockGoogleVK
Initial release201320152008
Current release2.0, September 20192.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoMIT LicensecommercialcommercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoJava, JavascriptC and C++
Server operating systemsBSD
Linux
OS X
Solaris
Windows
hostedhostedBSD
Linux
macOS
Data schemeschema-freeyesschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or datenoyesnostring, 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.nononono
Secondary indexesnotemporal, categoricalnoyes
SQL infoSupport of SQLnoANSI SQL for query only (using Presto)noFull-featured ANSI SQL support
APIs and other access methodsJDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Open binary protocol
Supported programming languagesGoC#
C++
Go
Java
JavaScript (Node.js)
Python
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 nodesnoneAutomatic shardingShardingSharding, 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 nodesnoneInternal 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 methodsnonoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate 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 integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnoAtomic 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 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.nononoyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlnoAccess rights for users can be defined per tableAccess 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
BoltDBGeoSpockGoogle 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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