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DBMS > Google Cloud Datastore vs. Lovefield vs. Microsoft Azure Data Explorer vs. Tarantool

System Properties Comparison Google Cloud Datastore vs. Lovefield vs. Microsoft Azure Data Explorer vs. Tarantool

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonLovefield  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformEmbeddable relational database for web apps written in pure JavaScriptFully managed big data interactive analytics platformIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelDocument storeRelational DBMSRelational DBMS infocolumn orientedDocument store
Key-value store
Relational DBMS
Secondary database modelsDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
Spatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score0.29
Rank#293  Overall
#133  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websitecloud.google.com/­datastoregoogle.github.io/­lovefieldazure.microsoft.com/­services/­data-explorerwww.tarantool.io
Technical documentationcloud.google.com/­datastore/­docsgithub.com/­google/­lovefield/­blob/­master/­docs/­spec_index.mddocs.microsoft.com/­en-us/­azure/­data-explorerwww.tarantool.io/­en/­doc
DeveloperGoogleGoogleMicrosoftVK
Initial release2008201420192008
Current release2.1.12, February 2017cloud service with continuous releases2.10.0, May 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud serviceyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaScriptC and C++
Server operating systemshostedserver-less, requires a JavaScript environment (browser, Node.js) infotested with Chrome, Firefox, IE, SafarihostedBSD
Linux
macOS
Data schemeschema-freeyesFixed schema with schema-less datatypes (dynamic)Flexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyes, details hereyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesstring, 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.nonoyesno
Secondary indexesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like query language (GQL)SQL-like query language infovia JavaScript builder patternKusto Query Language (KQL), SQL subsetFull-featured ANSI SQL support
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Open binary protocol
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
JavaScript.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresusing Google App EnginenoYes, possible languages: KQL, Python, RLua, C and SQL stored procedures
TriggersCallbacks using the Google Apps EngineUsing read-only observersyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding infoImplicit feature of the cloud serviceSharding, 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 nodesMulti-source replication using Paxosnoneyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.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 methodsyes infousing Google Cloud DataflownoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
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.Eventual Consistency
Immediate Consistency
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 integrityyes infovia ReferenceProperties or Ancestor pathsyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDnoACID, 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 persistentyesyes, by using IndexedDB or the cloud service Firebase Realtime Databaseyesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infousing MemoryDBnoyes, 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)noAzure Active Directory AuthenticationAccess 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 DatastoreLovefieldMicrosoft Azure Data ExplorerTarantool
DB-Engines blog posts

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

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