Database services take care of scalability and high availability of the database. Database services make the underlying software-stack transparent to the user.
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There are two primary methods to run a database in a cloud:
- Virtual machine image
- Cloud platforms allow users to purchase virtual-machine instances for a limited time, and one can run a database on such virtual machines. Users can either upload their own machine image with a database installed on it, or use ready-made machine images that already include an optimized installation of a database. For example, Oracle provides a ready-made machine image with an installation of Oracle Database 11g Enterprise Edition on Amazon EC2[third-party source needed] and on Microsoft Azure.[third-party source needed]
- Database-as-a-service (DBaaS)
- With a database as a service model, application owners do not have to install and maintain the database themselves. Instead, the database service provider takes responsibility for installing and maintaining the database, and application owners are charged according to their usage of the service. For example, Amazon Web Services provides three database as a service offerings as part of its cloud portfolio: SimpleDB, a NoSQL key-value store; Amazon RDS, a relational database service that includes support for MySQL, Oracle, and more; and DynamoDB. Microsoft offers its Azure SQL Database service[third-party source needed] on its Azure cloud service platform. Cloud computing platform Rackspace offers database as a service for MySQL and MongoDB. Database as a service providers are not limited to cloud computing platforms. For example, the MongoDB as a service provider mLab allows their customers to host their databases on AWS, Azure, or Google Cloud Platform. Database vendors have also launched their own services under this model. Oracle Cloud provides its own database as a service, allowing users to access Oracle Database 11g and 12c as cloud services. MongoDB recently launched its own hosted MongoDB as a service, MongoDB Atlas.
Architecture and common characteristics
- Most database services offer web-based consoles, which the end user can use to provision and configure database instances. For example, the Amazon Web Services web-console enables users to launch database instances, create snapshots (similar to backups) of databases, and monitor database statistics.[third-party source needed]
- Database services consist of a database-manager component, which controls the underlying database instances using a service API. The service API is exposed to the end user, and permits users to perform maintenance and scaling operations on their database instances. For example, the Amazon Relational Database Service's service API enables creating a database instance, modifying the resources available to a database instance, deleting a database instance, creating a snapshot (similar to a backup) of a database, and restoring a database from a snapshot.[third-party source needed]
- Underlying software-stack stack typically includes the operating system, the database and third-party software used to manage the database. The service provider (e.g. MongoLab or ObjectRocket) is responsible for installing, patching and updating the underlying software stack and ensuring the overall health and performance of the database.
- Scalability features differ between vendors – some offer auto-scaling, others enable the user to scale up using an API, but do not scale automatically. There is typically a commitment for a certain level of high availability (e.g. 99.9% or 99.99%).
The design and development of typical systems utilize data management and relational databases as their key building blocks. Advanced queries expressed in SQL work well with the strict relationships that are imposed on information by relational databases. However, relational database technology was not initially designed or developed for use over distributed systems. This issue has been addressed with the addition of clustering enhancements to the relational databases, although some basic tasks require complex and expensive protocols, such as with data synchronization.
Modern relational databases have shown poor performance on data-intensive systems, therefore, the idea of NoSQL has been utilized within database management systems for cloud based systems. Within NoSQL implemented storage, there are no requirements for fixed table schemas, and the use of join operations is avoided. "The NoSQL databases have proven to provide efficient horizontal scalability, good performance, and ease of assembly into cloud applications."
It is also important to differentiate between cloud databases which are relational as opposed to non-relational or NoSQL:
- SQL databases
- Such as PostgreSQL, EDB Postgres Advanced Server, NuoDB, Oracle Database, Microsoft SQL Server, MariaDB and MySQL, are one type of database which can run in the cloud, either in a virtual machine or as a service, depending on the vendor. While SQL databases are easily vertically scalable, horizontal scalability poses a challenge, that cloud database services based on SQL have started to address.[need quotation to verify]
- NoSQL databases
- Such as Apache Cassandra, CouchDB and MongoDB, are another type of database which can run in the cloud. NoSQL databases are built to service heavy read/write loads and can scale up and down easily, and therefore they are more natively suited to running in the cloud.: However, most contemporary applications are built around an SQL data model, so working with NoSQL databases often requires a complete rewrite of application code.
- Some SQL databases have developed NoSQL capabilities including JSON, binary JSON (e.g. BSON or similar variants), and key-value store data types. These multi-model databases include PostgreSQL and EDB Postgres Advanced Server.
- A multi-model database with relational and non-relational capabilities provides a standard SQL interface to users and applications and thus facilitates the usage of such databases for contemporary applications built around an SQL data model. Native multi-model databases like ArangoDB support multiple data models with one core and a unified query language to access all data models.
The following table lists notable database vendors with a cloud database offering, classified by their deployment model – machine image vs. database as a service – and data model, SQL vs. NoSQL.
|Virtual Machine Deployment||Database as a Service|
|SQL Data Model||
|NoSQL Data Model||
- Amazon Machine Images - Oracle Database 11g Release 2 (126.96.36.199) Enterprise Edition - 64 Bit, Amazon Web Services, Retrieved 2011-11-9.
- List of Oracle VM Images (MSDN), Retrieved 2014-9-22
- Safonov, Vladimir O. (2016-01-29). Trustworthy Cloud Computing. John Wiley & Sons. ISBN 9781119113515.
- "MongoDB Atlas - Hosted MongoDB as a Service". Retrieved 2016-09-01.
- "AWS Management Console, Amazon RDS Features", Amazon Web Services, Retrieved 2011-11-10.
- "Amazon Relational Database Service, Features, Detailed Description", Amazon Web Services, Retrieved 2011-11-10.
- A. Anjomshoaa and A. Tjoa, "How the cloud computing paradigm could shape the future of enterprise information processing", Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services - iiWAS'11, pp. 7-10, 2011.
- S. Cass, "Designing for the Cloud", MIT Technology Review, 2009. [Online]. Available: https://www.technologyreview.com/s/414090/designing-for-the-cloud/. Retrieved 2016-10-04.
- "NoSQL", Wikipedia, 2016. Retrieved 2016-10-04.
- Dave Rosenberg, Are databases in the cloud really all that different?, CNET, Retrieved 2011-11-6
- Agrawal, Rakesh; et al. (2008). "The Claremont report on database research" (PDF). SIGMOD Record. ACM. 37 (3): 9–19. doi:10.1145/1462571.1462573. ISSN 0163-5808.
- Ken North, "SQL, NoSQL or SomeSQL?", Dr. Dobb's, Retrieved 2011-11-9.
- Deploy your database applications and projects on the cloud, IBM.com, Retrieved 2011-9-1
- Chris Kanaracus, "Ingres rolls out cloud database offerings", Infoworld.com, Retrieved 2011-8-28.
- "Amazon Web Services Announces Two New Database Services – AWS Database Migration Service and Amazon RDS for MariaDB, Amazon Press Releases, retrieved 2015-11-17
- "MariaDB Enterprise Cluster + MariaDB MaxScale, Microsoft Azure, retrieved 2015-11-17
- "Running MySQL on Amazon EC2 with EBS (Elastic Block Store), Amazon Web Services, retrieved 2011-11-20
- Swoyer, Stephen. "NuoDB: A Database for the Cloud." TDWI. Nov. 13, 2012. Retrieved Nov. 26, 2012
- "Oracle Database in the Cloud", Oracle.com, Retrieved 2011-11-9.
- Chris Kanaracus, "EnterpriseDB Adding New Cloud Option for PostgreSQL Database", PCWorld, retrieved 2011-8-28
- "AWS | SAP HANA". Amazon Web Services, Inc. Retrieved 2016-07-07.
- "SAP Solutions | Microsoft Azure". azure.microsoft.com. Retrieved 2016-07-07.
- "SAP HANA Enterprise Cloud". hana.sap.com. Retrieved 2016-07-07.
- EnterpriseDB#cite note-10
- "Cloud SQL - MySQL Relational Database Service". Retrieved 2016-11-28.
- "Announcing Heroku PostgreSQL Database Add-on", Heroku Blog, Retrieved 2011-11-9.
- Pethuru, Raj (2014-03-31). Handbook of Research on Cloud Infrastructures for Big Data Analytics. IGI Global. ISBN 9781466658653.
- Noel Yuhanna, SQL Azure Raises The Bar On Cloud Databases, Forrester, Retrieved 2011-11-9.
- Klint Finley, "7 Cloud-Based Database Services", ReadWriteWeb, Retrieved 2011-11-9.
- "Setting up Cassandra in the Cloud", Cassandra Wiki, Retrieved 2011-11-10.
- "Google Cloud Platform Blog: Click to Deploy Apache Cassandra on Google Compute Engine". Retrieved 2016-11-28.
- "Clusterpoint Database Virtual Box VM Installation Guide Archived 2015-03-10 at Archive.is", Clusterpoint, Retrieved 2015-03-08.
- "Amazon Machine Images, CouchDB 0.10.x 32 bit Ubuntu", Amazon Web Services, Retrieved 2011-11-10.
- "CouchDB Cloud Hosting on Google Cloud Platform". Retrieved 2016-11-28.
- "Amazon Machine Image, Hadoop AMI", Amazon Web Services, Retrieved 2011-11-10.
- "Cloud Dataproc - Managed Spark & Managed Hadoop Service". Retrieved 2016-11-28.
- "Hadoop at Rackspace", Rackspace Big Data Platforms, Retrieved 2014-02-24.
- "MarkLogic Developer 8 (HVM) on AWS Marketplace". aws.amazon.com. Retrieved 2016-03-31.
- marklogic.com. "Flexible Deployment" (PDF). Retrieved 2016-11-28.
- "MongoDB on Amazon EC2, MongoDB.org, Retrieved 2011-11-10.
- "Deploying MongoDB on Google Compute Engine". Retrieved 2016-11-28.
- "MongoDB on Azure, MongoDB.org, Retrieved 2011-11-10.
- "Easily Scale MongoDB at Rackspace", Managed MongoDB ObjectRocket by Rackspace, Retrieved 2014-02-24.
- "Neo4J in the Cloud Archived 2011-09-25 at the Wayback Machine.", Neo4J Wiki, Retrieved 2011-11-10.
- "Announcing Neo4J on Windows Azure", Neo4J Blog, Retrieved 2011-11-10.
- Andrew Brust, "Cloudant Makes NoSQL as a Service Bigger", ZDNet, Retrieved 2012-5-22.
- "Bigtable - Scalable NoSQL Database Service". Retrieved 2016-11-28.
- "Datastore - NoSQL Schemaless Database". Retrieved 2016-11-28.
- "MongoDB Atlas - Hosted MongoDB as a Service". Retrieved 2016-08-30.
- "NoSQL Database Cloud Service | Oracle Cloud". cloud.oracle.com. Retrieved 2017-11-29.