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DBMS > Google Cloud Bigtable vs. Sadas Engine vs. SpaceTime vs. Tarantool

System Properties Comparison Google Cloud Bigtable vs. Sadas Engine vs. SpaceTime vs. Tarantool

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonSadas Engine  Xexclude from comparisonSpaceTime  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environmentsSpaceTime is a spatio-temporal DBMS with a focus on performance.In-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelKey-value store
Wide column store
Relational DBMSSpatial DBMSDocument store
Key-value store
Relational DBMS
Secondary database modelsRelational DBMSSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score0.00
Rank#383  Overall
#158  Relational DBMS
Score0.00
Rank#383  Overall
#7  Spatial DBMS
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websitecloud.google.com/­bigtablewww.sadasengine.comwww.mireo.com/­spacetimewww.tarantool.io
Technical documentationcloud.google.com/­bigtable/­docswww.sadasengine.com/­en/­sadas-engine-download-free-trial-and-documentation/­#documentationwww.tarantool.io/­en/­doc
DeveloperGoogleSADAS s.r.l.MireoVK
Initial release2015200620202008
Current release8.02.10.0, May 2022
License infoCommercial or Open Sourcecommercialcommercial infofree trial version availablecommercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++C and C++
Server operating systemshostedAIX
Linux
Windows
LinuxBSD
Linux
macOS
Data schemeschema-freeyesyesFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or datenoyesyesstring, 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 indexesnoyesnoyes
SQL infoSupport of SQLnoyesA subset of ANSI SQL is implementedFull-featured ANSI SQL support
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Proprietary protocol
RESTful HTTP APIOpen binary protocol
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
C
C#
C++
Groovy
Java
PHP
Python
C#
C++
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 nodesShardinghorizontal partitioningFixed-grid hypercubesSharding, 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 zonesnoneReal-time block device replication (DRBD)Asynchronous 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 methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate ConsistencyImmediate 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 integritynoyesnoyes
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 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.noyes infomanaged by 'Learn by Usage'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 rights for users, groups and roles according to SQL-standardyesAccess 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
Google Cloud BigtableSadas EngineSpaceTimeTarantool
DB-Engines blog posts

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

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