Versions Compared

Old Version 10

changes.mady.by.user Angela Zoss (Old)

Saved on

compared with

New Version Current

changes.mady.by.user Angela Zoss (Old)

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.


- Summary of

...

meeting was presented to Reporting SIG and has now been posted on the wiki: LDP1 Hosting - Experiences and Strategies from Users/DevOps Providers

...

The following experiences were shared by people with varying amounts of experience hosting LDP1, either as a third-party hosting provider or within a library organization. These suggestions are not intended to supersede specific recommendations by the developers of the LDP1 software.

General tips

...

.

...

Periodic extraction of data 

  • LDP1, ldpmarc and folio-analytics data extraction jobs need to be run on a schedule and in sequence. Possible scheduling services include cron, Jenkins, Kubernets cronjob.
  • For order of extraction jobs, might try LDP1, then ldpmarc, then folio-analytics derived tables.
  • Once a day tends to be pretty good.
  • While it updates the data, you wouldn't want to run large queries against the reporting database because the data update uses a lot of system resources.
  • Daily incremental update for ldpmarc is quite fast (e.g., 10 minutes for University of Chicago).
  • For performance, run with network proximity between the FOLIO and LDP databases 

Backups / disaster recovery:

  • There are different options for a PostgreSQL backup solution.
  • Some are running on Amazon AWS, RDS Postgres services for hosting, including disaster recovery (7 days of snapshots).
  • Others have local snapshots. 7 days is likely enough, because LDP keeps history of the records internally.

Cloning

  • Q: When you clone FOLIO production environment, do you clone the LDP1 data over or do you build the LDP1 data back from the FOLIO data that you have cloned?
    • Texas A&M: We don't clone the data over, but we re-build it from scratch. Or, if I upgrade, I just upgrade the LDP. In that way it preserves the history.
    • Index Data: If we refresh the staging environment for a tenant, we will re-build LDP from scratch.

Timing for upgrades / testing

  • One model for LDP1 and ldpmarc: run two instances (staging and production). LDP1 and ldpmarc releases are both fairly independent of FOLIO releases, new versions come out on their own cadence. When there is a new version of LDP1 or ldpmarc, implement that in staging. If it's more than a point upgrade, might invite users to test. Follow that with upgrade of production.
  • An upgrade in LDP1 is really simple. You invoke the LDP server with an upgrade-database command. Then LDP knows what verison it is talking to.      
  • For ldpmarc, no upgrade process is necessary. If needed, the new version will automatically perform a full update (rather than incremental update).
  • One model for FOLIO Analytics: FOLIO Analytics is released with specific FOLIO flower releases, so upgrade those together. Include LDP1 in standard FOLIO testing. Apart from that, invite user to test only when folio-analytics tables have changed.
  • Another model: have a special Jenkins job which creates a thing called the LDP engine. This engine includes all 3 components. Use a Docker image for all the setups for the Flower Release. When a new version of LDP comes, build a new Docker image with the latest versions. Do a smoke test. If everything works fine, create recommendations for the upcoming Flower Release. Use these until the next upcoming flower release.

Logging

  • Some use container log aggregation (CloudWatch, or Elastic), so the logs don't go away when the container goes away.
  • Some use FluentD/Rancher for logs, which then get pushed to Splunk. Might need to log to standard error/out instead of to a file.
  • Probably a good idea to send an alert for when the jobs fail, but could also check all manually each morning.

Security

  • This group did not feel there are security concerns to hosting LDP1.
  • Set up your network securely. Set up access to the reporting database securely. Secure (or pseudonomize) personally identifiable information. Apart from that, there are no security concerns !
  • Single-Sign On to database access is not supported. You do have to use regular postgresql security. But that is more an integration problem than a security problem.
  • Additional strategies: Make sure the LDP IP address is only accessible from certain subnets. Make sure Kubernetes network is namespace isolated.
  • Texas A&M: Set up different user accounts with read-only access, and use those accounts for connections via tools like Cloud Beaver. Then embed those tools in a VM that uses standard university permissions systems (e.g., SSO via Shibboleth). The different read-only user accounts can be granted permissions to just certain types of data, and different staff can be granted permission to use just the VM that matches the permissions they should have.
  • SSL is used for LDP database access unless otherwise configured..

Concerns

  • Data transfer time gets longer as data in FOLIO increases.
  • You will need more resources in LDP1 as your production database grows.
  • Some experience silent failures and do not feel there is good and detailed logging. (But there is good support by Nassib!)
  • Documentation in LDP1 repository is mostly oriented to developers. It is not easy to find information if you are not familiar with the system.
  • For ldpmarc, the incremental update is quick, but the full update can be pretty long.  (This is less of an issue since ldpmarc 1.6, which includes significant performance improvements.)
  • There is no automatic recovery. When a process fails, it needs to be re-run manually.

Related Resources

...

This project will be considered complete now.