[Cogsvcs] Personalizer - moving docs over

Author: diberry

articles/cognitive-services/personalizer/concepts-scalability-performance.md

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+---
+title: Reinforcement Learning - Personalizer
+titleSuffix: Azure Cognitive Services
+description: Personalizer uses information about actions and current context to make better ranking suggestions. The information about these actions and context are attributes or properties that are referred to as features.
+services: cognitive-services
+author: edjez
+manager: nitinme
+ms.service: cognitive-services
+ms.subservice: personalizer
+ms.topic: overview
+ms.date: 05/07/2019
+ms.author: edjez
+---
+# Scalability and Performance
+
+Personalizer is used in many high-performance and high-traffic websites and applications.
+When evaluating Personalizer for use in these situations, there are two main factors to consider:
+
+1. Keeping low latency when making Rank API calls
+1. Making sure training throughput keeps up with event input
+
+## Performance & Latency
+
+Personalization can return a rank very rapidly, with most of the call duration dedicated to communication through the REST API. Azure will auto-scale the ability to respond to requests rapidly.
+
+
+###  Low-latency Scenarios
+
+Some applications require  low latencies when returning a rank. This is necessary to keep the user from 'waiting' a noticeable amount of time; or keep a server experiencing extreme traffic from tying up compute time and network connections.
+
+If your web site is scaled on your infrastructure, you can avoid making http calls by hosting the Personalizer API in your own servers running a Docker container.
+
+This change would be transparent to your application, other than using an endpoint URL referring to the running docker instances as opposed to an online service in the cloud.
+
+### Extreme Low Latency Scenarios
+
+If you require latencies under a millisecond, and have already tested using Personalizer via containers, please contact our support team so we can assess your scenario and provide guidance suited to your needs.
+
+
+## Scalability and Training Throughput
+
+Personalizer works by updating a model that is re-trained based on messages sent asynchronously by Personalizer after Rank and Reward APIs. These messages are sent using an Azure EventHub for the application.
+
+ It is unlikely most applications will reach the maximum joining and training throughput of Personalizer.
+ While reaching this maximum will not slow down the application, it would imply Event Hub queues are getting filled internally faster than they can be cleaned up.
+
+How to estimate your throughput requirements
+
+* Estimate what is the average number of bytes per ranking events (adding the lengths of the context and action JSON documents)
+* Divide 20MB/sec by this number.
+
+For example, if your average payload has 500 features and each is an estimated 20 characters, then each event is approximately 10kb. With these estimates, 20,000,000 / 10,000 = 2,000 events/sec, which is about 173 million events/day. 
+
+If you are reaching these limits please contact our support team for architecture advice.
+
+# More Information