Re: Disable hbase user history queries

[Pat Ferrel]

First, I’m not sure this is a good idea. You loose the realtime nature of recommendations based on the up-to-the-second recording of user behavior. You get this with live user event input even without re-calculating the model in realtime.

Second, no you can’t disable queries for user history, it is the single most important key to personalized recommendations.

I’d have to know more about your application but the first line of cost cutting for us in custom installations (I work for ActionML the maintainer of the UR Template) is to make the Spark cluster temporary since it is not needed to serve queries and only needs to run during training. We start it up, train. then shut it down.

If you really want to shut the entire system down and don’t want realtime user behavior you can query for all users and put the results in your DB or in-memory cache like a hashmap, then just serve from your db or in-memory cache. This takes you back to the days of the old Mahout Mapreduce recommenders (pre 2014) but maybe it fits your app.

If you are doing E-Commerce think about a user’s shopping behavior. They shop, browse, then buy. Once they buy that old shopping behavior is no longer indicative of realtime intent. If you miss using that behavior you may miss the shopping session altogether. But again, your needs may vary.


On Jun 1, 2017, at 6:19 AM, Martin Fernandez <martingf...@gmail.com> wrote:

Hello guys,

we are trying to deploy Universal Recommender + predictionIO in our infrastructure but we don't want to distribute hbase accross datacenters cause of the latency. So the idea is to build and train the engine offline and then copy the model and ealstic data to PIO replicas. I noticed when I deploy engine, it always tries to connect to HBASE server since it is used to query user history. Is there any way to disable those user history queries and avoid connection to HBASE?

Thanks

Martin

[Martin Fernandez]

Thanks Pat for your reply. I am doing Video on Demand e-commerce in which reatime query would be very helpful but I want to minimize the risks of HDFS synchronization latency between datacenters. Do you have experience running predictionIO + Universal Recommender in multiple DCs that you can share? Did you face any latency issue with the HBASE cluster?  

Thanks in advance

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Saludos / Best Regards,

Martin Gustavo Fernandez

Mobile: +5491132837292

[Pat Ferrel]

I haven’t done this, it can be done. But why? You always give up performance when instances are not on the same physical LAN. Recommenders are generally not considered mission critical where the ultimate HA is required.

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[Martin Fernandez]

Actually I have my infraestructure splitted in multiple datacenters in an Active/active mode. So how can I manage to have a PIO instance running in each DC? Do I have to deploy also HBASE as well? How can I maintain HBASE data? 

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[Pat Ferrel]

I would put PIO in one and query/input from either of your other datacenters. The latency issues with input/query are much much simpler than spreading HBase across datacenters. I mean would you want to put Spark and Elasticsearch across datacenters too? You are doing 2 datacenters  for HA, right? Do you really need HA for a recommender? If so all of the components support this but it will cost a lot in complexity and $$ for instances.

[Pat Ferrel]

Event if you need this I’d think about 2 deployments with in input proxy that sends to both input servers and a load balancer for queries. That risks the split brain problem but if one goes down you can recover since no data is lost and you can export from DS1 and import to DS2 to recover. Recommenders can deal with small amounts of data loss too unless it’s changes to item attributes.

The alternative of making every component service HA to the extent of multiple datacenters would be a lot more work. I’s not hard to make PIO HA for a single datacenter in fact it’s pretty easy. It’s making each service work across multiple datacenters that might end up being complex.

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