High Availability Considerations for H₂O¶

It’s helpful to think about high availability needs based on different stages in your workflow.

Data exploration (Interactive)

This is an interactive process that is generally carried out either through the R language (H₂O + R package) or the H₂O Web UI. In the current version of (H₂O, the cluster itself does not have HA support. Users needing HA can, however, connect to a spare H₂O cluster by providing a different IP address and Port to connect to.

Modeling (Interactive or batch)

Once again, since the current version of H₂O does not have specific HA support, users that have HA requirements can keep spare clusters available and point to them via an IP address and Port.

Hadoop users in particular may wish to create a disposable cluster for each individual modeling job and discard the cluster upon completion. The ‘hadoop jar’ command makes it very easy to spin up a cluster on demand. This approach limits the loss of a cluster to an individual job, which can be restarted by a user script.

Predicting (Real-time)

For real-time predictions, the H₂O models can be exported into plain Java POJO classes. These plain Java objects just do basic arithmetic operations and don’t allocate any data. Since they run independently from the full H₂O software stack, they don’t add any additional HA or uptime complexity to a production environment.