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DBMS > Google Cloud Datastore vs. Tarantool vs. TimesTen

System Properties Comparison Google Cloud Datastore vs. Tarantool vs. TimesTen

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonTarantool  Xexclude from comparisonTimesTen  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsIn-Memory RDBMS compatible to Oracle
Primary database modelDocument storeDocument 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
Score4.87
Rank#78  Overall
#12  Document stores
Score1.88
Rank#146  Overall
#24  Document stores
#25  Key-value stores
#68  Relational DBMS
Score1.59
Rank#157  Overall
#72  Relational DBMS
Websitecloud.google.com/­datastorewww.tarantool.iowww.oracle.com/­database/­technologies/­related/­timesten.html
Technical documentationcloud.google.com/­datastore/­docswww.tarantool.io/­en/­docdocs.oracle.com/­database/­timesten-18.1
DeveloperGoogleVKOracle, TimesTen Performance Software, HP infooriginally founded in HP Labs it was acquired by Oracle in 2005
Initial release200820081998
Current release2.10.0, May 202211 Release 2 (11.2.2.8.0)
License infoCommercial or Open SourcecommercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprisecommercial
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC and C++
Server operating systemshostedBSD
Linux
macOS
AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows
Data schemeschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columnsyes
Typing infopredefined data types such as float or dateyes, details herestring, 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.nonono
Secondary indexesyesyesyes
SQL infoSupport of SQLSQL-like query language (GQL)Full-featured ANSI SQL supportyes
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Open binary protocolJDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
C
C++
Java
PL/SQL
Server-side scripts infoStored proceduresusing Google App EngineLua, C and SQL stored proceduresPL/SQL
TriggersCallbacks using the Google Apps Engineyes, before/after data modification events, on replication events, client session eventsno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding, 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 nodesMulti-source replication using PaxosAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.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 or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyes, cooperative multitaskingyes
Durability infoSupport for making data persistentyesyes, write ahead loggingyes infoby means of logfiles and checkpoints
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 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
Google Cloud DatastoreTarantoolTimesTen
DB-Engines blog posts

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

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