Deployment V0.1.1

To reap all the advantages of the riskassessment application, an organization needs to generate and store results frequently derived from the app, including but not limited to:

  1. Uploading new packages, & storing their metric info and riskmetric score
  2. Publishing reviewer comments
  3. Submitting package decisions
  4. Storing organization-wide metric weights and score-based thresholds for decision automation
  5. Credential management for authorized users
  6. Etc

To accomplish this, the app automatically creates and populates several storage solutions locally, in the app’s root directory:

  • A SQLite database named credentials.sqlite (by default) for credential management
  • A loggit.json, recording rudimentary logs of actions performed in the app
  • A SQLite database named database.sqlite (by default) for storing all pkg info, metric info, and comments/ decisions
  • A auto_decisions.json file, that initiates and keeps track of score-based thresholds for decision automation

Note that this article won’t cover the credentials database since that’s that’s been covered (at length) in the “Administrative Tools and Options” guide. As you might expect, some deployment environments come with limitations that are not amicable to storage method defined above! Spoiler alert - shinyapps.io is one of them. This article will provide more details about these crucial storage objects and share a run down of the following deployment options with helpful tips:

  • shinyapps.io

  • RStudio Connect

  • Shiny Server

  • ShinyProxy

Note! In the future, we’ll consider adding utilities that would allow users to connect to an external, or remote storage method (like a database).


The database.sqlite file

When the riskassessment::run_app() function is ran, the code base will check if the database.sqlite file exists, and if it doesn’t, several tables will be initiated to create this database just before the app launches. Note that the user has the authority to name the database file to whatever they like using the assessment_db_name argument in run_app(). In addition, you can include a file path here preceding the file name if you wish to move the location of the database away from the root directory. For example, the following code would create a database called “test_database.sqlite” in the dev/ folder:

run_app(assessment_db_name = './dev/test_database.sqlite')

At the time this vignette was authored, the database will initialize the following tables with all NULL values:

  • comments: stores user comments, including user names associated with each comment entry, comment type, and timestamp
  • community_usage_metrics: stores CRAN package downloads per month
  • metric: stores metric info, including the riskmetric name, label, short description, type, and weight
  • package_metrics: stores all package-specific rismetric metrics
  • package: Contains all package information, like name, version, maintainer, etc. It also contains a the riskmetric score, the weighted score, decision, and timestamp.

There is one exception to this: the metric table is populated with data via the initialize_metric_table.sql file. If the user deploying the app wants to enter certain weights prior to launching the application, this would be a good place to do it. For example, if your org wants to place a heavier weight on “has_vignettes”, then you can increase the weight value up accordingly. Of course, you can still do this in the application, but that would require a manual change once the app has launched. Similarly, if you want to ignore a certain metric because it has little value to our organization, you could give it a value of zero here.

Note: if any changes/ updates occur in these tables with a new release of riskassessment, you may need to delete the database file before re-running the app. Any such changes should be announced as ‘breaking changes’ in the app. If so, remember to save a copy of your current database file in another directory for backup purposes.


The loggit.json file

As mentioned previously, the loggit.json file is initiated to track general actions performed in the application, especially those that would impact a change in the database.sqlite file. At the time this vignette was authored, here’s a summary of actions logged:

  • App start up
  • User log-ins
  • Enabling/ Disabling or changing automated decision rules
  • Decisions initiated by users or the decision automation rules, including
  • When pkgs are added or removed from the database, or when there are issues with this process, like when there is an absence of data to that support certain metrics
  • Re-weighting pkg metrics
  • Any database issues or conflicts that may arise

Each loggit “transaction” recorded contains a lot of info, but most notably, it will contain a timestamp, record type (“info”, “warn”, or “error”), and a message. For database transactions, the message will usually include the query that was executed. All log entries are always printed out in the R console for your convenience. For example:

riskassessment::run_app()
> [1] "Log file set to loggit.json"
> Listening on http://127.0.0.1:4097
> {"timestamp": "2023-03-20T11:46:23-0400", "log_lvl": "INFO", "log_msg": "User admin signed on as admin"}
> {"timestamp": "2023-03-20T11:46:33-0400", "log_lvl": "INFO", "log_msg": "The following decision rules were implemented by admin (admin): Medium = (0.33__COMMA__ 0.66]; Low = (0__COMMA__ 0.33]; High = (0.66__COMMA__ 1]."}

Feel free to use this information as you see fit!


The auto_decisions.json file

When riskassessment::run_app() is executed, a search is performed to see if there exists an auto_decisions.json file in the root directory. Without such file, an empty one will be automatically created. This file is used to keep track of any decision automation rules. As you add & edit the rules in the app, the json file will update accordingly, keeping track of each categories risk score range. For example, the following json file coordinates with ranges displayed in the app:

{"Medium":[0.33,0.66],"Low":[0,0.33],"High":[0.66,1]}


Deployment options

shinyapps.io

Beware: shinyapps.io does not offer persistent storage of riskassessment’s SQLite database or logs. Thus, it is not likely a viable deployment option for your organization or group. For example, our demo application (hosted on shinyapps.io) contains a pre-prepared database of packages that can’t be permanently altered. It CAN be altered within a session, but any changes will not persist outside the session.


Posit Connect & Shiny Server


On a server, if you want to save files on disk, you’ll need to set write permissions accordingly on the folder you want to save the database, logs, and auto-decision json file.

On Posit Connect, you need to use an absolute path to specify the directory where to save the files. You can find more information here: Persistent Storage on Posit Connect.


Beyond that, it’s important to note that we strongly encourage those deploying the app to take advantage of the renv.lock file used to maintain package version dependencies. For more information on how to use renv.lock and our general dev philosophy as it pertains to package management, please read the “Using renv” article. Highly related, we encourage the use of git-backed deployment when possible. So, immediately after you update/sync your package dependencies using renv, you may want to run rsconnect::writeManifest() before deploying from a dedicated branch. For more information on git-backed deployment & manifest files, please read the Posit article on git-backed content.


ShinyProxy

With ShinyProxy, you can use a Docker volume to write files outside of the application container. In application.yml, you use can something like this in the specs describing the application:

container-volumes: [ “/var/log/shinylogs:/root/logs” ]

/var/log/shinylogs is a directory on the server where you deploy your applications with ShinyProxy. /root/logs is a directory inside your Docker image.