recommenders VS Apache ZooKeeper

recommenders – Best Practices on Recommendation Systems

I would like to do a benchmarking on the Microsoft models SVD, SAR and NMF (available here: https://github.com/microsoft/recommenders) but with this input data I get a precision and recall close to zero. Any ideas how I can improve this? For SVD and NMF (surprise library) the model wants a rating input that is normally distributed, which it not the case for my binary data where the transactions all have a rating of 1.

Then I tried to find some more advanced models and I found this really good list and in there I found the Microsoft one. So it's' where we are now, which a bunch of different models and not a documentation/tutorials out there.

Apache ZooKeeper — a distributed coordination, synchronization, and configuration service (written in Java);

To use Kafka, we also need to deploy a service that keeps configuration informations such as Zookeeper.

Failure Detection and Recovery It’s not enough to have backup systems. It’s also crucial to detect failures quickly. Modern systems employ monitoring tools and rely on distributed coordination systems such as Zookeeper or etcd to identify faults in real-time: once detected, recovery mechanisms are triggered to restore the service.

zookeeper: is (was?) used for secrets management. it was also used as a basic health check, but has been since been replaced.

You can install Kafka from https://kafka.apache.org/quickstart. Because Druid and Kafka both use Apache Zookeeper, I opted to use the Zookeeper deployment that comes with Druid, so didn’t start it with Kafka. Once running, I created two topics for me to post the data into, and for Druid to ingest from:

Please note that we use Zookeeper as the Governance Center.

Next, review deployment complexity such as DB-less versus database-backed deployments. For example, Kong does require running Cassandra or Postgres. Apigee requires Cassandra, Zookeeper, and Postgres to run, while other solutions like Express Gateway and Tyk only require Redis. Apache APISIX uses etcd as its data store, it stores and manages routing-related and plugin-related configurations in etcd in the Data Plane.

ZooKeeper, a system for coordinating distributed nodes, similar to Google's Chubby

[Unit] Description=Apache Zookeeper server Documentation=http://zookeeper.apache.org Requires=network.target remote-fs.target After=network.target remote-fs.target [Service] Type=simple ExecStart=/usr/local/kafka/bin/zookeeper-server-start.sh /usr/local/kafka/config/zookeeper.properties ExecStop=/usr/local/kafka/bin/zookeeper-server-stop.sh Restart=on-abnormal [Install] WantedBy=multi-user.target

ElasticJob achieves distributed coordination through ZooKeeper. In practical scenarios, users may start multiple jobs in the same project simultaneously, all of which use the same Apache Curator client. There are certain risks due to the nature of ZooKeeper and the callback method of Curator in a single event thread.