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DBMS > Google Cloud Bigtable vs. Ingres vs. SwayDB vs. Tarantool

System Properties Comparison Google Cloud Bigtable vs. Ingres vs. SwayDB vs. Tarantool

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonIngres  Xexclude from comparisonSwayDB  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.Well established RDBMSAn embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storageIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelKey-value store
Wide column store
Relational DBMSKey-value storeDocument store
Key-value store
Relational DBMS
Secondary database modelsSpatial 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
Score4.11
Rank#81  Overall
#44  Relational DBMS
Score0.00
Rank#382  Overall
#59  Key-value stores
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websitecloud.google.com/­bigtablewww.actian.com/­databases/­ingresswaydb.simer.auwww.tarantool.io
Technical documentationcloud.google.com/­bigtable/­docsdocs.actian.com/­ingreswww.tarantool.io/­en/­doc
DeveloperGoogleActian CorporationSimer PlahaVK
Initial release20151974 infooriginally developed at University Berkely in early 1970s20182008
Current release11.2, May 20222.10.0, May 2022
License infoCommercial or Open SourcecommercialcommercialOpen Source infoGNU Affero GPL V3.0Open 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

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCScalaC and C++
Server operating systemshostedAIX
HP Open VMS
HP-UX
Linux
Solaris
Windows
BSD
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.nono infobut tools for importing/exporting data from/to XML-files availablenono
Secondary indexesnoyesnoyes
SQL infoSupport of SQLnoyesnoFull-featured ANSI SQL support
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)
Open binary protocol
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
Java
Kotlin
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresnoyesnoLua, C and SQL stored procedures
Triggersnoyesnoyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning infoIngres Star to access multiple databases simultaneouslynoneSharding, 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 zonesIngres ReplicatornoneAsynchronous 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 operationsACIDAtomic execution of operationsACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyes infoMVCCyesyes, 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.nonoyesyes, 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)fine grained access rights according to SQL-standardnoAccess 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 BigtableIngresSwayDBTarantool
DB-Engines blog posts

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

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