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DBMS > Drizzle vs. Google Cloud Datastore vs. Impala vs. Microsoft SQL Server vs. ToroDB

System Properties Comparison Drizzle vs. Google Cloud Datastore vs. Impala vs. Microsoft SQL Server vs. ToroDB

Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonImpala  Xexclude from comparisonMicrosoft SQL Server  Xexclude from comparisonToroDB  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformAnalytic DBMS for HadoopMicrosofts flagship relational DBMSA MongoDB-compatible JSON document store, built on top of PostgreSQL
Primary database modelRelational DBMSDocument storeRelational DBMSRelational DBMSDocument store
Secondary database modelsDocument storeDocument store
Graph DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score6.45
Rank#67  Overall
#12  Document stores
Score17.56
Rank#40  Overall
#25  Relational DBMS
Score944.96
Rank#3  Overall
#3  Relational DBMS
Score0.03
Rank#342  Overall
#45  Document stores
Websitecloud.google.com/­datastorewww.cloudera.com/­products/­open-source/­apache-hadoop/­impala.htmlwww.microsoft.com/­en-us/­sql-serverwww.torodb.com
Technical documentationcloud.google.com/­datastore/­docsdocs.cloudera.com/­documentation/­enterprise/­latest/­topics/­impala.htmldocs.microsoft.com/­en-US/­sql/­sql-serverwww.torodb.com/­stampede/­docs
DeveloperDrizzle project, originally started by Brian AkerGoogleClouderaMicrosoft8Kdata
Initial release20082008201319892016
Current release7.2.4, September 20124.0.0, July 2021SQL Server 2019, November 2019
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialOpen Source infoApache Version 2commercial inforestricted free version is availableOpen Source infoAGPL-V3
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C++C++Java
Server operating systemsFreeBSD
Linux
OS X
hostedLinuxLinux
Windows
All OS with a Java 7 VM
Data schemeyesschema-freeyesyesschema-free
Typing infopredefined data types such as float or dateyesyes, details hereyesyesyes infostring, integer, double, boolean, date, object_id
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 indexesyesyesyesyes
SQL infoSupport of SQLyes infowith proprietary extensionsSQL-like query language (GQL)SQL-like DML and DDL statementsyes
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)
Supported programming languagesC
C++
Java
PHP
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
All languages supporting JDBC/ODBCC#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic
Server-side scripts infoStored proceduresnousing Google App Engineyes infouser defined functions and integration of map-reduceTransact SQL, .NET languages, R, Python and (with SQL Server 2019) Java
Triggersno infohooks for callbacks inside the server can be used.Callbacks using the Google Apps Enginenoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingtables can be distributed across several files (horizontal partitioning); sharding through federationSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using Paxosselectable replication factoryes, but depending on the SQL-Server EditionSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowyes infoquery execution via MapReduceno
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 ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesyes infovia ReferenceProperties or Ancestor pathsnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and roles infobased on Apache Sentry and Kerberosfine grained access rights according to SQL-standardAccess rights for users and roles

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More resources
DrizzleGoogle Cloud DatastoreImpalaMicrosoft SQL ServerToroDB
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