Compute Per Actor
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mnmkng/compute-per-actor
This actor goes through your run history and calculates compute unit usage statistics per actor. This is useful to get an idea which of your actors would benefit the most from optimizations.
Dockerfile
1# This is a template for a Dockerfile used to run acts in Actor system.
2# The base image name below is set during the act build, based on user settings.
3# IMPORTANT: The base image must set a correct working directory, such as /usr/src/app or /home/user
4FROM apify/actor-node-basic:v0.21.10
5
6# Second, copy just package.json and package-lock.json since it should be
7# the only file that affects "npm install" in the next step, to speed up the build
8COPY package*.json ./
9
10# Install NPM packages, skip optional and development dependencies to
11# keep the image small. Avoid logging too much and print the dependency
12# tree for debugging
13RUN npm --quiet set progress=false \
14 && npm install --only=prod --no-optional \
15 && echo "Installed NPM packages:" \
16 && (npm list --all || true) \
17 && echo "Node.js version:" \
18 && node --version \
19 && echo "NPM version:" \
20 && npm --version
21
22# Copy source code to container
23# Do this in the last step, to have fast build if only the source code changed
24COPY . ./
25
26# NOTE: The CMD is already defined by the base image.
27# Uncomment this for local node inspector debugging:
28# CMD [ "node", "--inspect=0.0.0.0:9229", "main.js" ]package.json
1{
2 "name": "apify-project",
3 "version": "0.0.1",
4 "description": "",
5 "author": "It's not you it's me",
6 "license": "ISC",
7 "dependencies": {
8 "apify": "0.21.10"
9 },
10 "scripts": {
11 "start": "node main.js"
12 }
13}main.js
1const Apify = require('apify');
2
3const { client, utils: { log, sleep } } = Apify;
4
5Apify.main(async () => {
6 log.info('Getting actors.');
7 const actors = await getActors();
8 log.info('Getting actor runs.');
9 const actorsWithRuns = await getActorsWithRuns(actors);
10 log.info('Getting stats for individual runs.');
11 const pool = createRunPool(actorsWithRuns);
12 await pool.run();
13 await Apify.setValue('ACTORS', actorsWithRuns);
14 log.info('Calculating CU usage.');
15 const results = calculateComputeUnitUsage(actorsWithRuns);
16 log.info('Saving output.');
17 await Apify.setValue('OUTPUT', { actors: results });
18});
19
20async function getActors() {
21 const { items } = await client.acts.listActs();
22 return items;
23}
24
25async function getActorsWithRuns(actors) {
26 const actorsWithRunsPromises = actors.map(async (actor, actorIdx) => {
27 await sleep(actorIdx * 33);
28 log.info(`Getting runs for actor: ${actor.id}`);
29 const { items } = await client.acts.listRuns({ actId: actor.id })
30 return { ...actor, runs: items };
31 }, {});
32 return Promise.all(actorsWithRunsPromises);
33}
34
35async function getRunStats(actorId, run) {
36 return client.acts.getRun({ actId: actorId, runId: run.id })
37}
38
39function createRunPool(actorsWithRuns) {
40 const allTasks = actorsWithRuns.reduce((tasks, actor, actorIndex) => {
41 const runs = actor.runs.map((run, runIndex) => ({
42 actorId: actor.id,
43 actorIndex,
44 run,
45 runIndex,
46 }));
47 return tasks.concat(runs);
48 }, []);
49 setInterval(() => {
50 log.info(`There are ${allTasks.length} remaining runs to process.`);
51 }, 10000)
52 return new Apify.AutoscaledPool({
53 minConcurrency: 5,
54 runTaskFunction: async () => {
55 const task = allTasks.shift();
56 const runWithStats = await getRunStats(task.actorId, task.run);
57 actorsWithRuns[task.actorIndex].runs[task.runIndex] = runWithStats;
58 },
59 isTaskReadyFunction: () => !!allTasks.length,
60 isFinishedFunction: () => !allTasks.length
61 })
62}
63
64function calculateComputeUnitUsage(actors) {
65 return actors.map((actor) => {
66 const computeUnitUsage = Array.isArray(actor.runs)
67 ? actor.runs.reduce((sum, run) => sum + (run.stats.computeUnits || 0), 0)
68 : null;
69 return {
70 id: actor.id,
71 name: actor.name,
72 computeUnitUsage,
73 }
74 })
75}Developer
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