Add the k6 load test script to the docs

docs/developer.md

@@ -50,6 +50,24 @@ make migrations-rebuild
 This target will rebuild the `database/migrate_generated.go` file to include execution of all
 the packages in `database/migrations`.
 
+## Filling the database
+
+It's often necessary to test out your changes on a loaded database, and we definitely want to do this
+before making a release. A production-level target for database shape is:
+* 40k runs across 300 experiments
+* 2k unique metrics per run, each with 200 values
+
+To get some percentage of this into your local database instance, you can use the included K6
+load test script, which can be run in the dev container.
+
+1. Start the FML tracking server
+2. Start a terminal in the dev container. 
+3. `cd docs/example`
+4. `k6 run k6_load.js`
+
+Tweak the numbers in `k6_load.js` for number of runs, metrics, etc -- the default amounts are 
+pretty small.
+
 ## Working with the UIs
 
 FastTrackML incorporates the existing Aim and MLFlow web UIs, albeit

docs/example/k6_load.js

@@ -0,0 +1,118 @@
+import http from 'k6/http';
+
+export default function () {
+  const namespace = 'default'
+  const numberOfExperiments = 2
+  const runsPerExperiment = 10
+  const paramsPerRun = 100
+  const metricsPerRun = 1000
+  const stepsPerMetric = 10
+
+  for (let i = 0; i < numberOfExperiments; i++) {
+    const experimentId = createExperiment(namespace)
+    for (let j = 0; j < runsPerExperiment; j++) {
+      createRun(namespace, experimentId, paramsPerRun, metricsPerRun, stepsPerMetric)
+    }
+  }
+}
+
+function createExperiment(namespace) {  
+  const base_url = `http://localhost:5000/ns/${namespace}/api/2.0/mlflow/`;
+
+  const exp_response = http.post(
+    base_url + 'experiments/create',
+    JSON.stringify({
+      "name": `experiment-${Date.now()}`,
+    }),
+    {
+      headers: {
+        'Content-Type': 'application/json'
+      },
+    }
+  );
+  return exp_response.json().experiment_id;
+}
+
+
+function createRun(namespace, experimentId, numParams, numMetrics, numSteps) {  
+  const base_url = `http://localhost:5000/ns/${namespace}/api/2.0/mlflow/`;
+
+  const run_response = http.post(
+    base_url + 'runs/create',
+    JSON.stringify({
+      experiment_id: experimentId,
+      start_time: Date.now(),
+      tags: [
+        {
+          key: "mlflow.user",
+          value: "k6"
+        }
+      ]
+    }),
+    {
+      headers: {
+        'Content-Type': 'application/json'
+      },
+    }
+  );
+  const run_id = run_response.json().run.info.run_id;
+
+  let params = []
+  for (let id = 1; id <= numParams; id++) {
+    params.push({
+      key: `param${id}`,
+      value: `${id * Math.random()}`,
+    })
+  }
+  http.post(
+    base_url + 'runs/log-batch',
+    JSON.stringify({
+      run_id: run_id,
+      params: params
+    }),
+    {
+      headers: {
+        'Content-Type': 'application/json'
+      },
+    }
+  );
+
+  let metrics = [];
+  for (let step = 1; step <= numSteps; step++) {
+    for (let id = 1; id <= numMetrics; id++) {
+      metrics.push({
+        key: `metric${id}`,
+        value: id * step * Math.random(),
+        timestamp: Date.now(),
+        step: step,
+      })
+    }
+  }
+
+  http.post(
+    base_url + 'runs/log-batch',
+    JSON.stringify({
+      run_id: run_id,
+      metrics: metrics
+    }),
+    {
+      headers: {
+        'Content-Type': 'application/json'
+      },
+    }
+  );
+
+  http.post(
+    base_url + 'runs/update',
+    JSON.stringify({
+      run_id: run_id,
+      end_time: Date.now(),
+      status: 'FINISHED'
+    }),
+    {
+      headers: {
+        'Content-Type': 'application/json'
+      },
+    }
+  );
+}