Performance Data

Performance data in OpenLMIS is meant to be data that helps us answer questions such as:

  • What happens to the server and the operations it provides when there are 10,000 orderables, users, facilities, requisitions, etc?
  • What happens when all that data is being used by many concurrent users?
  • What’s the impact on network performance, especially for those in low resource environments?
  • What sort of deployment topology works best for typical implementations?
  • Does the UI (and possibly other clients) display large sets of data well?

Some basic characteristics of performance data:

  • there is a lot of it
  • it doesn’t have to look nice or make that much sense to domain experts (e.g. a Vaccine could be randomly generated to be ordered through the essential meds Program, and that’s okay). Lorem ipsum and random numbers are just fine here.
  • it must be deployable in a deployment topology that is as close to a production setup as possible. After all it’s for performance testing, and performance testing on a local laptop doesn’t tell us (much) about anything a production server running in the cloud would experience.

Where is performance data located?

Performance data is stored in Git within each Service that defines it, much like demo-data. In fact in most cases Performance Data builds off of demo-data, and so a Service should be able to load performance data or demo-data in very similar ways.

How to load performance data

Like demo-data, performance data is an optional set of data that may be loaded when the Service starts. To do this a Service should load performance data, likely after any demo-data, by looking for the profiles set in the environment variable If this environment variable contains the string performance-data, then the service should load this data before it’s operational for use.

How to create and manage performance data

Performance data is generated with the help of the tool Mockaroo. This tool is used to define schemas which match the Service’s tables and it may generate large CSVs which are then stored in the Service in git. CSVs are used as they easily enable the use of foreign key / UUID lookups when a Mockaroo dataset is used (as this Mockaroo dataset video demonstrates). These CSVs are placed in git for the Service to load the data, however if the Service needs new performance data, the database schema changes or something else causes the performance data to need to be updated, the OpenLMIS Mockaroo account should be used to generate a new set, which will then be stored in the Service.

What types of performance data should be created?

Performance data is relatively expensive and tedious to maintain given the questions we’re trying to answer. While it’s necessary to do so, here are some general guidelines for what to spend time generating, and what not to:


  • Generate performance data that will allows performance tests to reflect country data needs.
  • Try to generate data that’s more right than random. Random is okay, However if the tool has a sufficiently large set of facilities, or products, use it.
  • Respect database constraints, foreign keys, references to IDs in other Services etc
  • Keep in mind that some UUIDs need to be known. They can’t be generated. You’ll need to know a few of these key UUIDs (e.g. Program, User, etc) in order to construct useful performance tests.


  • Overcomplicate the data. 1 billion facilities, a trillion requisitions, 1000 programs just aren’t anywhere near likely and just take longer to load and more time to maintain. 10k facilities, 100k requisitions, 10 programs are much more representative.
  • Similarly, don’t generate data when demo-data already has enough. E.g. demo data already has a few Programs, you’re time is better spent setting up one of those programs to have 10k facility type approved products than you are generating 100 programs.
  • Don’t build performance tests off of generated IDs. While Mockaroo makes it easy to build sets of data with referential integrity, using generated IDs hardcoded in performance tests will result in more brittle tests.