DistOS 2015W Session 11 - Revision history

Ashley at 01:36, 13 April 2015

2015-04-13T01:36:57Z

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			* [http://research.google.com/archive/bigtable-osdi06.pdf Chang et al., "BigTable: A Distributed Storage System for Structured Data" (OSDI 2006)]
			* [http://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf DeCandia et al., "Dynamo: Amazon’s Highly Available Key-value Store" (SOSP 2007)]
			* [http://www.cs.cornell.edu/projects/ladis2009/papers/lakshman-ladis2009.pdf Lakshman & Malik, "Cassandra - A Decentralized Structured Storage System" (LADIS 2009)]
			* [https://www.usenix.org/conference/osdi12/technical-sessions/presentation/corbett Corbett et al., "Spanner: Google’s Globally-Distributed Database" (OSDI 2012)]

			Session 11's theme is about Distributed Hash Tables as they are used and implemented by large commercial companies, for what purposes, and with what specializations or design constraints. Two papers focus on Google, while Facebook and Amazon get one each.

	==BigTable==		==BigTable==
	* Google System used for storing data of various Google Products, for instance Google Analytics, Google Finance, Orkut, Personalized Search, Writely, Google Earth and many more		* Google System used for storing data of various Google Products, for instance Google Analytics, Google Finance, Orkut, Personalized Search, Writely, Google Earth and many more

2015-04-06T05:13:01Z

Spanner

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	* Has been built on top of the Google's Big table.		* Has been built on top of the Google's Big table.
	*Provided data consistency and Supports SQL like Interface.		*Provided data consistency and Supports SQL like Interface.
	* Uses ~~True~~ time to guarantee the correctness properties around concurrency control.		* Uses a separate high-reliability time service to guarantee the correctness properties around concurrency control.
	** The timestamps are utilized.		** The timestamps are utilized.
	*It shares data across machines and migrates data automatically across machines		*It shares data across machines and migrates data automatically across machines
	*Data Control Functions in spanner controls latency and performance		*Data Control Functions in spanner controls latency and performance

2015-04-04T11:38:25Z

Spanner

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	** The timestamps are utilized.		** The timestamps are utilized.
	*It shares data across machines and migrates data automatically across machines		*It shares data across machines and migrates data automatically across machines
			*Data Control Functions in spanner controls latency and performance

2015-04-04T11:34:05Z

Spanner

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	* Has been built on top of the Google's Big table.		* Has been built on top of the Google's Big table.
	*Provided data consistency and Supports SQL like Interface.		*Provided data consistency and Supports SQL like Interface.
	*Main focus is managing cross-datacentre replicated data.
	* Uses True time to guarantee the correctness properties around concurrency control.		* Uses True time to guarantee the correctness properties around concurrency control.
	** The timestamps are utilized.		** The timestamps are utilized.
			*It shares data across machines and migrates data automatically across machines

2015-04-04T11:31:20Z

Cassandra

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	**Bootstrapping		**Bootstrapping
	** Scaling the cluster		** Scaling the cluster
			*It can run cheap commodity hardware and handle high throughput
			*Its multiple usable structure makes it very scalable

	=Spanner=		=Spanner=

2015-04-04T11:27:52Z

BigTable

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	*** Input source		*** Input source
	***Output Target		***Output Target
			*The timestamp mechanism in BIG table helps clients to access recent versions of data with simple accessing aspects of using row and column.
			*Parallel computation and cluster management system makes BIG table flexible and highly scalable.

	== Dynamo==		== Dynamo==

2015-03-31T02:42:27Z

Spanner

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	=Spanner=		=Spanner=
	* Google's scalable, multi version, globally distributed database.		* Google's scalable, multi version, globally distributed database.
			* Has been built on top of the Google's Big table.
	*Provided data consistency and Supports SQL like Interface.		*Provided data consistency and Supports SQL like Interface.
	*Main focus is managing cross-datacentre replicated data		*Main focus is managing cross-datacentre replicated data.
			* Uses True time to guarantee the correctness properties around concurrency control.
			** The timestamps are utilized.

2015-03-31T02:34:47Z

Spanner

← Older revision		Revision as of 02:34, 31 March 2015
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	=Spanner=		=Spanner=
	*Provided data consistency and Supports SQL like Interface		* Google's scalable, multi version, globally distributed database.
			*Provided data consistency and Supports SQL like Interface.
			*Main focus is managing cross-datacentre replicated data

2015-03-31T02:31:24Z

Cassandra

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	** Partitioning: Takes place using consistent hashing		** Partitioning: Takes place using consistent hashing
	**Replication: Each item replicated at n hosts where "n" is the replication factor configured per system.		**Replication: Each item replicated at n hosts where "n" is the replication factor configured per system.
	** Membership		** Membership: Cluster membership is based on Scuttle butt which is a highly efficient anti-entropy Gossip based mechanism.The Membership further has sub part such as:
			***Failure Detection
	**Bootstrapping		**Bootstrapping
	** Scaling the cluster		** Scaling the cluster

2015-03-31T02:29:15Z

Cassandra

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	**Get		**Get
	** Delete		** Delete
	*		* System Architecture consists of :
			** Partitioning: Takes place using consistent hashing
			**Replication: Each item replicated at n hosts where "n" is the replication factor configured per system.
			** Membership
			**Bootstrapping
			** Scaling the cluster

	=Spanner=		=Spanner=
	*Provided data consistency and Supports SQL like Interface		*Provided data consistency and Supports SQL like Interface