18 April 2014

The Ruby Reflector

Topic

Consistency

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By Todd Hoff of High Scalability 12 months ago.
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In NoSQL: Past, Present, Future Eric Brewer has a particularly fine section on explaining the often hard to understand ideas of BASE (Basically Available, Soft State, Eventually Consistent), ACID ( Atomicity, Consistency, Isolation, Durability), CAP ( Consistency Availability, Partition Tolerance), in terms of a pernicious long standing myth about the sanctity of consistency in banking.

Myth : Money is important, so banks must use transactions to keep money safe and consistent, right?

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By Bryan McLellan of Chef Blog over 1 year ago.
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[ CHEF-3183 ] - Consistency and expected behavior of resource notifications

[ CHEF-3201 ] - knife client create - already exists exit code

[ CHEF-3210 ] - wrong regexp in provider/service/freebsd.rb

[ CHEF-3235 ] - [regression] file(...).owner and file(...).mode returns nil instead of expected integer value

[ CHEF-3237 ] - Expanding '~/ Library/ LaunchAgents' fails resolving HOME when …

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By Todd Hoff of High Scalability over 2 years ago.
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V8 JavaScript instead of client-side libraries; Keep your code small and light.

Nice thread in NoSQL Databases on HBase and Consistency in CAP . The short summary of the article is that CAP isn't "C, A, or P, choose two," but rather "When P happens, choose A or C." To read more of what the Internet has to say on scalability, please read more below...

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On codahale.com over 3 years ago.
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If a system chooses to provide Consistency over Availability in the presence of partitions (again, read: failures), it will preserve the guarantees of its atomic reads and writes by refusing to respond to some requests. It may decide to shut down entirely (like the clients of a single-node data store), refuse writes (like Two-Phase Commit), or only respond to reads and writes for pieces of data whose "master" node is inside the partition component (like Membase).

This is perfectly …

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By Mike Perham of 4 years ago.
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…distributed systems is Brewer's CAP theorem : distributed systems can have Consistency, Availability and Partition-tolerance properties but can only guarantee two. In the case of Cassandra, they guarantee AP and loosen consistency to what is known as eventual consistency . Consider a write and a read that are very close together in time. Let's say you have a key "A" with a value of "123″ in your cluster. Now you update "A" to be "456″. …

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Consistency - CAP theorem - can get any two of Consistency, Availability, Partition tolerance - not all three. (Also see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1915 )

Most systems need partition tolerance and availability ahead of consistency.

Customer wants to place an order - you will accept the order, not return the money saying the system is unavailable - availability is important

Inventory would be checked asynchronously

Order details …

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By Srinath Perera of High Scalability over 2 years ago.
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…article takes four parameters about an application/ usecase ( Scale, Consistency, Type of Data, and Queries needed), then take some 40+ cases that arises from different value combination of those parameters and make one or more concrete recommendations on right storage solution for that case.

What follows are the four parameters and potential values they can take and the recommendations for structured, semi-structured, and unstructured data:

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By Todd Hoff of High Scalability over 3 years ago.
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The title of this post is a quote from Ilya Grigorik's post Weak Consistency and CAP Implications . Besides the article being excellent, I thought this idea had something to add to the great NoSQL versus RDBMS debate, where Mike Stonebraker makes the argument that network partitions are rare so designing eventually consistent systems for such rare occurrence is not worth losing ACID semantics over. Even if network partitions are rare, latency between datacenters is not rare, so the game is still on.

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