One of the great availability and scaling features of Caché is Enterprise Cache Protocol (ECP). With consideration during application development distributed processing using ECP allows a scale out architecture for Caché applications. Application processing can scale to very high rates from a single application server to the processing power of up to 255 application servers with no application changes.

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Article
· Nov 17, 2016 11m read
ECP and Process Management API

The technology of load balancing between several servers with relatively low capacity has been a standard feature of Caché for quite a while. It is based on the distributed cache technology called ECP (Enterprise Cache Protocol). ECP provides a host of possibilities for horizontal scaling of an application, and yet keeping the project budget fairly low. Another apparent advantage of ECP network is the possibility to conceal its architecture in the depths of Caché configuration so that applications developed for the traditional (vertical) architecture can be fairly easily migrated to a horizontal ECP environment. The ease of this process is so mesmerizing, that you start wishing it was always this way. For instance, everybody is used to having a possibility to control Caché processes: the $Job system variable and associated classes/functions work magic in skilful hands. Stop, but now processes can end up being on different Caché servers…

This article is about how to gain as much transparency in controlling processes in ECP environment as in traditional (non ECP) one.

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Article
· Sep 30, 2016 1m read
ECP Magic

I saw someone recently refer to ECP as magic. It certainly seems so, and there is a lot of very clever engineering to make it work. But the following sequence of diagrams is a simple view of how data is retrieved and used across a distributed architecture.

For more more on ECP including capacity planning follow this link: Data Platforms and Performance - Part 7 ECP for performance, scalability and availability

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Question
· Jun 15, 2016
Cluster deployment recipes?

Let assume you have a infinitely scaling algorithm implemented in your application, using replication, ECP, or any other means of horizontal scaling, and let assume you know how to run your system under any volume of requests, the trick is to deploy required number of computing nodes in the cluster. If we are talking about cluster of 2-4 nodes your administrator (or as they call it today "devops engineer") will install anything manually. Probably he will easily handle 5 nodes configuration in the cluster.

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