# Monitoring (`apify/monitoring`) Actor This actor monitors your actors' statuses, validates their datasets' data, and displays useful information in an interactive dashboard. And if something happens, you'll get notified via email or Slack. - **URL**: https://apify.com/apify/monitoring.md - **Developed by:** [Apify](https://apify.com/apify) (Apify) - **Categories:** Automation - **Stats:** 186 total users, 1 monthly users, 0.0% runs succeeded, 18 bookmarks - **User rating**: 4.33 out of 5 stars ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Monitoring Suite Manually tracking the results of your actors can be complicated and tiresome. Did they succeed? Have they produced the correct amount of results? Were those results valid? How does the amount of results evolve over time? Luckily, you don't need to visit Apify dashboard every day to answer those questions. Monitoring suite is an easy to use tool that allows you to set up automated monitoring of your actors in no time. It can be used for simple tasks and complex projects spanning multiple actors and datasets as well. ### Is this free? The monitoring suite can be used free of charge, but under the hood, it is a system of actors that consume compute units. We've tried our best to make it as efficient as possible. ##### Consumption For average projects, it will consume single digit amounts of CUs per month. - Medium projects (single monitoring configuration with 5-20 daily running monitored tasks or actors) should consume around 10 CUs per month. - Bigger projects (more monitoring configurations with more than 20 daily running tasks or actors) can monthly exceed 50 CUs+ consumption. ### Quick start 1. Click **Try for free** above to create a new task. 2. You should see the **What you want to monitor** input section open. 3. Give your monitoring suite a name under **Monitoring suite name**. 4. Under **Type of target** select if you want to monitor an actor or a task. 5. Fill the names of your actor or task under **Target name patterns**. 6. Select **Notify me whenever actor/task does not succeed**. 7. Click **Run** to enable your monitoring. You can now sit back and relax knowing that whenever a run of your actor / task does not succeed, a notification will be sent to your email immediately. ### How do I turn this off or make changes? To turn off the selected monitoring suite, simply run the task with mode `Delete configuration`. It will clean up all its resources and it will turn off all monitoring activities. You will no longer receive the notifications. If you want to make any changes in existing configuration, just choose the `Update configuration` mode option, make your changes and run the monitoring suite task again. ### How it works The monitoring suite is a complex system. Based on your configuration, it will create [schedules](https://docs.apify.com/schedules) and [webhooks](https://docs.apify.com/webhooks) under your account and use those to trigger actor runs. Some will make sure that your data are correct, others will send you notifications. All monitoring related resources will be prefixed with `monitoring-`. If you start seeing a lot of those, it's expected. You can always turn off the monitoring by following the [steps above](#how-do-i-turn-this-off-or-make-changes). ### Targets The [quick start section](#quick-start) gets you up and running in no time, but the monitoring suite is far more powerful. Let's look at targets first. A target is something that you want to monitor. Currently, it can be: - actor - task - dataset Use the **Type of target** input field to make your selection. If you need to monitor multiple target types, you can create multiple monitoring suites. #### Target name patterns In most scenarios, you can just type in the name of the target you want to monitor into this input field and be done with it. But keep in mind that it's actually a regular expression that matches all targets of the selected **type** under your account. This is extremely useful to quickly select multiple targets to monitor. It is possible to add more than one pattern which can be useful to easily select more targets or in particular if the target type dataset is selected. It means that all the matched datasets are going to be automatically group by these patterns and visualization of their data will be much easier and well-arranged. Imagine you have 3 actors, `amazon-scraper`, `google-scraper` and `results-uploader`. By setting the pattern to `scraper`, you can quickly monitor both your scrapers. If you want to monitor just one of them, provide the full name. If you have actors `my-actor` and `my-actor-2` and you want to monitor only `my-actor`, use regular expression syntax `^my-actor$` to select only the first one. When you have named datasets as `eshop-items-week-1`, `eshop-items-week-2` and `eshop-orders-week-1`, `eshop-orders-week-2` and you want to group your weekly data by data category, just insert two patterns as `eshop-orders` and `eshop-items`. #### Target IDs If for whatever reason the **Target name pattern** option does not suit you, targets can also be specified by providing their IDs, as found in your Apify dashboard. ### Checkers Checkers are the bread and butter of monitoring. They collect different kinds of information about your targets and depending on your needs, you can use only one or all of them in your monitoring suite. You'll receive the information from checkers using the [notifications](#notifications). > When actors or tasks are used as targets, checkers that operate on datasets will automatically use their default datasets. #### Check frequency Each checker has a check / refresh frequency input, where you can specify how often you want the checker to run. Critical issues with your data are best reported immediately. The frequency input understands [natural cron language](https://github.com/darkeyedevelopers/natural-cron.js). There are two basic options. Updating after each monitored run finishes or on a pre-set schedule. Type the **Per run**, **Each run** or **Every run** to run the checker immediately after your actor / task run finishes. To schedule updates, use plain English sentences such as `every day at 13:30` or `every Monday at noon` or `at 8pm every 1st day of the month`. > Note that dataset targets can't be checked per run. For statistical checkers, such as the dashboard, we suggest scheduling regular (daily) updates. #### Run status checker Is the simplest checker we have. It doesn't even have its own section and you turn it on by selecting "**Notify me whenever actor/task does not succeed**". As the name suggests, it works only for actors and tasks and it will check for runs that either `FAILED`, `TIMED-OUT` or `ABORTED`, so you'll never miss a problem again. #### Dashboard with statistics With a dashboard, you can immediately see how your targets performed in easy to read charts and with up to 60 days history. This is especially useful to track trends in your data. Are you getting a consistent number of results or is it declining? Was there an unexpected drop? How often do my actors fail? The dashboard shows you all this information at a glance. ##### Dashboard data grouping You can set `grouping` your target's data by `name patterns` if you do not want to use default grouping by selected targets. All matched targets will then be displayed as one data line in the dashboard charts. For example if you use the same group of scraping actors for different countries as `actor-1-cz`, `actor-2-cz` and `actor-1-us`, `actor-2-us` your patterns can be `cz`, `us` and all your your dashboard will display 2 data lines - one for each state. #### Schema checker If you want to export your data to CSV or simply to keep high quality of data, it's worth making sure that all the items in your dataset match a certain schema. The schema checker uses the [`ow`](https://sindresorhus.com/ow/index.html) syntax for its good balance of simplicity and versatility. Besides validating the schema of individual items, the schema checker can also make sure that your datasets have a minimum or maximum number of items. > The checker measures the number of "**clean**" items in the datasets. ##### Validation options The validation options specify your constraints. They are always an array of objects. This is to enable use of different schemas for different targets. ###### Properties - `filter`:`string`: select targets by matching name to a pattern - `targetIds`:`string[]`: select specific targets by ID - `minItemCount`:`number`: minimum amount of items in dataset - `maxItemCount`:`number`: maximum amount of items in dataset - `schema`:`object`: validation schema for individual items ###### Examples The following example will check whether your dataset includes at least 30 items and that all the items have a property `foo` of type `String` and a property `bar` of type `Number`. ```javascript [ { minItemCount: 30, schema: { foo: t.string, bar: t.number, }, }, ] ```` The next example uses an override to use different validations for one target. The target must be one of the monitoring suite targets. This is to enable more granular validation of previously selected targets, not to add more targets to the mix. ```javascript [ { minItemCount: 30, schema: { foo: t.string, bar: t.number } }, { targetIds: ["A1b2C3d4"], minItemCount: 50, schema: { foo: t.string, bar: t.number } } ] ``` Using a `filter` to provide different validations for different targets. This could be used if you selected `scraper` as your **Target name pattern** in the first section of the input and now you need to split your scrapers into different validation groups. ```javascript [ { filter: "scraper-hockey-", minItemCount: 20, schema: { game: t.string, goals: t.string } }, { filter: "scraper-tennis-", minItemCount: 5, schema: { game: t.string, sets: t.array } } ] ``` #### Duplicates checker When scraping large amounts of data, duplicates will inevitably occur. This checker will let you know when that happens, and will point out the problematic items in your datasets. When used with actors / tasks, it will check their default datasets. Make sure to set the **Unique keys**. The checker does not assert uniqueness of whole dataset items, but only of values under the selected keys. For example, if you are collecting the inventory of an online store, and the items in the store are uniquely identified by a SKU (stock keeping unit), you would save the SKU to your dataset under the key `sku` and then set `sku` as the Unique key. ### Notifications Notifications are the final step of the monitoring operation. They will deliver the information gathered by your checkers in a simple human readable format. By default, notifications will only be sent if something goes wrong. If you want to be notified for successful checks, select the given options under the individual checker sections. ##### Notification grouping When there are more actor/task runs that would finish all at once or close each other grouping all the notification to one notification report instead of sending each of one separately could be nice. All the notifications that should come within 5 minutes range will be group if you set this option. #### Email Email notifications are the default. You don't need to do anything and they will be automatically set up with your account email and a subject line that will tell you your monitoring suite's status at a glance. You can disable email notifications, use a different email or a different subject line in the appropriate input fields. #### Slack For larger projects and for teams, using Slack notifications is often better than email. Monitoring suite will send reports to a channel of your choice, detailing the status of your suite. To set it up, you need a channel ID and a token. ##### Channel This is easy. To send notifications to the `notifications` channel in your Slack workspace, use `#notifications`. ##### Token To access the channel, you need a token. There are various tokens that you can use and multiple approaches, depending on your Slack workspace. Visit [the Slack authentication docs](https://api.slack.com/authentication) and perhaps ask the owner of your workspace for help with getting the right token. ### What's next? - automated grouping of related datasets in dashboard - dataset content visualisation / checks - key-value store content checker # Actor input Schema ## `mode` (type: `string`): Select mode of the monitoring. Configuration run modes are Create, update and delete. Monitoring configuration will be created and all connected tools will be turned ON when 'Create configuration' is chosen. Choose 'Update configuration' mode when you already created the configuration and you just want to change some details of existing monitoring configuration. Option 'Delete configuration' turns the monitoring off and removes all the created tools and storages connected to the monitoring configuration. ## `projectName` (type: `string`): Name of your monitoring suite. It will be used in notifications and to identify related targets in the Apify dashboard. ## `targetType` (type: `string`): Only one type of target can be monitored by a single monitoring suite. If you want to watch more types, create more monitoring suites. ## `targetPatternList` (type: `array`): Regular expressions that will be matched against selected actors / tasks or datasets under your Apify account. All matching targets will then be monitored by this monitoring suite. This is typically also the fastest way to select a single target. Just type its full name. Datasets are going to be automatically group by these patterns when dashboard statistics is counted for dataset target type, this can be overriden by setting the `Group targets by name patterns` option in the `Statistics dashboard` section. ## `targetList` (type: `array`): If for whatever reason the Target name pattern option does not suit you, targets can also be specified by providing their IDs, as found in your Apify dashboard. ## `failuresCheck` (type: `boolean`): It's the simplest checker we have. As the name suggests, it works only for actors and tasks and it will check for runs that either FAILED, TIMED-OUT or ABORTED, so you'll never miss a problem again. ## `groupNotifications` (type: `boolean`): It groups notifications to one notification report when more notifications should come within 5 minutes range instead of sending each of them separately. This can be useful when there are more actor/task runs that would finish all at once or close each other. ## `dashboardStatistics` (type: `boolean`): Collects statistics and produces a link to a dashboard with visualisations. ## `dashboardStatisticsFrequency` (type: `string`): Choose how often should the dashboard statistics update. There are two basic options. Updating after each monitored run finishes or on a pre-set schedule. To update with every run, type: per run, each run or every run. To schedule updates, use plain English sentences such as every day at 13:30 or every Monday at noon or at 8pm every 1st day of the month. For more examples see: natural cron. ## `dashboardGroupByList` (type: `array`): Regular expressions or name patterns that will be used to group your selected targets by it's name. Named datasets are by default grouped by `targetPatternList`, setting this option will override it. All matched targets will then be displayed as one data line in the dashboard charts. For example if you use the same group of scraping actors for different countries as actor-1-cz, actor-2-cz and actor-1-us, actor-2-us your patterns can be cz, us and all your your dashboard will display 2 data lines - one for each state. ## `dashboardStatisticsNotifyOnUpdate` (type: `boolean`): You will be notified every time when the dashboard is updated with new data. ## `validationCheck` (type: `boolean`): Datasets or default datasets will be validated using the provided validation options. ## `validationCheckOptions` (type: `string`): The validation options specify your constraints. They are always an array of objects. This is to enable use of different schemas for different targets. See README for details. ## `validationCheckFrequency` (type: `string`): Choose how often will the schema checks run. There are two basic options. Updating after each monitored run finishes or on a pre-set schedule. To update with every run, type: per run, each run or every run. To schedule updates, use plain English sentences such as every day at 13:30 or every Monday at noon or at 8pm every 1st day of the month. For more examples see: natural cron. ## `validationNotifyOnSuccess` (type: `boolean`): Validation checking report is going to be generated and sent via notification even if your data is correct. ## `duplicationCheck` (type: `boolean`): Datasets or default datasets will be checked for duplicates using the provided unique key. ## `duplicationCheckKeys` (type: `array`): You can define a list of unique keys for the duplication checking. Each unique key represents the dataset field that will be compared for uniqueness. You should use something that's guaranteed to be unique among items in your dataset. Like email for people, SKU for e-shop items, GUIDs and so on. ## `duplicationCheckAllowedDuplicates` (type: `integer`): Represents acceptable number of duplicated items occurring in the dataset. The duplication check will pass and the notification won't be sent If the number of duplicated items is lower than the allowed number of duplicates. ## `duplicationCheckFrequency` (type: `string`): Choose how often will the check for duplicates run. There are two basic options. Updating after each monitored run finishes or on a pre-set schedule. To update with every run, type: per run, each run or every run. To schedule updates, use plain English sentences such as every day at 13:30 or every Monday at noon or at 8pm every 1st day of the month. For more examples see: natural cron. ## `duplicationNotifyOnSuccess` (type: `boolean`): Duplicate checking report is going to be generated and sent via notification even if your data is correct. ## `doNotSendEmail` (type: `boolean`): If selected, email notification will not be sent. ## `emailNotification` (type: `string`): Email address to override your account email address. ## `emailSubject` (type: `string`): Subject to override the generated subject of your notification email. ## `slackNotification` (type: `string`): Insert Slack channel name, such as #notifications ## `slackToken` (type: `string`): Insert application or user OAuth Access Token to the Slack API. Ask your workspace admin for help if needed. ## Actor input object example ```json { "mode": "CREATE", "targetType": "ACTOR", "targetPatternList": [], "targetList": [], "dashboardStatisticsFrequency": "Every day at noon", "dashboardGroupByList": [], "validationCheckOptions": "/* global ow */\n// The `ow` variable represents the ow validation variable. More at https://sindresorhus.com/ow/index.html\n [\n {\n filter: \"us\", // matches all targets with \"us\" pattern\n minItemCount: 5000,\n schema: {\n url: ow.string.url,\n description: ow.string,\n },\n },\n {\n Ids: [\"A1b2C3d4\", \"B1A2d45\"], // matches 2 targets with specific IDs\n schema: {\n itemId: ow.number,\n region: ow.string,\n },\n },\n]", "validationCheckFrequency": "Per run", "duplicationCheckKeys": [], "duplicationCheckAllowedDuplicates": 0, "duplicationCheckFrequency": "Per run" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "dashboardStatisticsFrequency": "Every day at noon", "validationCheckOptions": `/* global ow */ // The `ow` variable represents the ow validation variable. More at https://sindresorhus.com/ow/index.html [ { filter: "us", // matches all targets with "us" pattern minItemCount: 5000, schema: { url: ow.string.url, description: ow.string, }, }, { Ids: ["A1b2C3d4", "B1A2d45"], // matches 2 targets with specific IDs schema: { itemId: ow.number, region: ow.string, }, }, ]`, "validationCheckFrequency": "Per run", "duplicationCheckFrequency": "Per run" }; // Run the Actor and wait for it to finish const run = await client.actor("apify/monitoring").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "dashboardStatisticsFrequency": "Every day at noon", "validationCheckOptions": """/* global ow */ // The `ow` variable represents the ow validation variable. More at https://sindresorhus.com/ow/index.html [ { filter: \"us\", // matches all targets with \"us\" pattern minItemCount: 5000, schema: { url: ow.string.url, description: ow.string, }, }, { Ids: [\"A1b2C3d4\", \"B1A2d45\"], // matches 2 targets with specific IDs schema: { itemId: ow.number, region: ow.string, }, }, ]""", "validationCheckFrequency": "Per run", "duplicationCheckFrequency": "Per run", } # Run the Actor and wait for it to finish run = client.actor("apify/monitoring").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "dashboardStatisticsFrequency": "Every day at noon", "validationCheckOptions": "/* global ow */\\n// The `ow` variable represents the ow validation variable. More at https://sindresorhus.com/ow/index.html\\n [\\n {\\n filter: \\"us\\", // matches all targets with \\"us\\" pattern\\n minItemCount: 5000,\\n schema: {\\n url: ow.string.url,\\n description: ow.string,\\n },\\n },\\n {\\n Ids: [\\"A1b2C3d4\\", \\"B1A2d45\\"], // matches 2 targets with specific IDs\\n schema: {\\n itemId: ow.number,\\n region: ow.string,\\n },\\n },\\n]", "validationCheckFrequency": "Per run", "duplicationCheckFrequency": "Per run" }' | apify call apify/monitoring --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=apify/monitoring", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Monitoring", "description": "This actor monitors your actors' statuses, validates their datasets' data, and displays useful information in an interactive dashboard. And if something happens, you'll get notified via email or Slack.", "version": "0.0", "x-build-id": "k6TruHzfACd7tEsIp" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/apify~monitoring/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-apify-monitoring", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/apify~monitoring/runs": { "post": { "operationId": "runs-sync-apify-monitoring", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/apify~monitoring/run-sync": { "post": { "operationId": "run-sync-apify-monitoring", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "projectName", "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "CREATE", "UPDATE", "DELETE" ], "type": "string", "description": "Select mode of the monitoring. Configuration run modes are Create, update and delete. Monitoring configuration will be created and all connected tools will be turned ON when 'Create configuration' is chosen. Choose 'Update configuration' mode when you already created the configuration and you just want to change some details of existing monitoring configuration. Option 'Delete configuration' turns the monitoring off and removes all the created tools and storages connected to the monitoring configuration.", "default": "CREATE" }, "projectName": { "title": "Monitoring suite name", "pattern": "^([a-zA-Z0-9!\\-_.'()]{1,15})$", "maxLength": 15, "type": "string", "description": "Name of your monitoring suite. It will be used in notifications and to identify related targets in the Apify dashboard." }, "targetType": { "title": "Type of target", "enum": [ "ACTOR", "TASK", "NAMED_DATASET" ], "type": "string", "description": "Only one type of target can be monitored by a single monitoring suite. If you want to watch more types, create more monitoring suites.", "default": "ACTOR" }, "targetPatternList": { "title": "Target name patterns", "type": "array", "description": "Regular expressions that will be matched against selected actors / tasks or datasets under your Apify account. All matching targets will then be monitored by this monitoring suite. This is typically also the fastest way to select a single target. Just type its full name. Datasets are going to be automatically group by these patterns when dashboard statistics is counted for dataset target type, this can be overriden by setting the `Group targets by name patterns` option in the `Statistics dashboard` section.", "default": [], "items": { "type": "string" } }, "targetList": { "title": "Target IDs", "type": "array", "description": "If for whatever reason the Target name pattern option does not suit you, targets can also be specified by providing their IDs, as found in your Apify dashboard.", "default": [], "items": { "type": "string" } }, "failuresCheck": { "title": "Notify me whenever actor/task does not succeed", "type": "boolean", "description": "It's the simplest checker we have. As the name suggests, it works only for actors and tasks and it will check for runs that either FAILED, TIMED-OUT or ABORTED, so you'll never miss a problem again." }, "groupNotifications": { "title": "Group notifications", "type": "boolean", "description": "It groups notifications to one notification report when more notifications should come within 5 minutes range instead of sending each of them separately. This can be useful when there are more actor/task runs that would finish all at once or close each other." }, "dashboardStatistics": { "title": "Enable dashboard", "type": "boolean", "description": "Collects statistics and produces a link to a dashboard with visualisations." }, "dashboardStatisticsFrequency": { "title": "Update frequency", "type": "string", "description": "Choose how often should the dashboard statistics update. There are two basic options. Updating after each monitored run finishes or on a pre-set schedule. To update with every run, type: per run, each run or every run. To schedule updates, use plain English sentences such as every day at 13:30 or every Monday at noon or at 8pm every 1st day of the month. For more examples see: natural cron.", "default": "Every day at noon" }, "dashboardGroupByList": { "title": "Group targets by name patterns", "type": "array", "description": "Regular expressions or name patterns that will be used to group your selected targets by it's name. Named datasets are by default grouped by `targetPatternList`, setting this option will override it. All matched targets will then be displayed as one data line in the dashboard charts. For example if you use the same group of scraping actors for different countries as actor-1-cz, actor-2-cz and actor-1-us, actor-2-us your patterns can be cz, us and all your your dashboard will display 2 data lines - one for each state.", "default": [], "items": { "type": "string" } }, "dashboardStatisticsNotifyOnUpdate": { "title": "Notify me on dashboard updates", "type": "boolean", "description": "You will be notified every time when the dashboard is updated with new data." }, "validationCheck": { "title": "Enable schema validation", "type": "boolean", "description": "Datasets or default datasets will be validated using the provided validation options." }, "validationCheckOptions": { "title": "Validation options", "type": "string", "description": "The validation options specify your constraints. They are always an array of objects. This is to enable use of different schemas for different targets. See README for details." }, "validationCheckFrequency": { "title": "Validation frequency", "type": "string", "description": "Choose how often will the schema checks run. There are two basic options. Updating after each monitored run finishes or on a pre-set schedule. To update with every run, type: per run, each run or every run. To schedule updates, use plain English sentences such as every day at 13:30 or every Monday at noon or at 8pm every 1st day of the month. For more examples see: natural cron.", "default": "Per run" }, "validationNotifyOnSuccess": { "title": "Send me report even if validation passes", "type": "boolean", "description": "Validation checking report is going to be generated and sent via notification even if your data is correct." }, "duplicationCheck": { "title": "Enable duplicate items check", "type": "boolean", "description": "Datasets or default datasets will be checked for duplicates using the provided unique key." }, "duplicationCheckKeys": { "title": "Unique keys", "type": "array", "description": "You can define a list of unique keys for the duplication checking. Each unique key represents the dataset field that will be compared for uniqueness. You should use something that's guaranteed to be unique among items in your dataset. Like email for people, SKU for e-shop items, GUIDs and so on.", "default": [], "items": { "type": "string" } }, "duplicationCheckAllowedDuplicates": { "title": "Number of allowed duplicates", "type": "integer", "description": "Represents acceptable number of duplicated items occurring in the dataset. The duplication check will pass and the notification won't be sent If the number of duplicated items is lower than the allowed number of duplicates.", "default": 0 }, "duplicationCheckFrequency": { "title": "Check frequency", "type": "string", "description": "Choose how often will the check for duplicates run. There are two basic options. Updating after each monitored run finishes or on a pre-set schedule. To update with every run, type: per run, each run or every run. To schedule updates, use plain English sentences such as every day at 13:30 or every Monday at noon or at 8pm every 1st day of the month. For more examples see: natural cron.", "default": "Per run" }, "duplicationNotifyOnSuccess": { "title": "Send me report even if check passes", "type": "boolean", "description": "Duplicate checking report is going to be generated and sent via notification even if your data is correct." }, "doNotSendEmail": { "title": "Disable email notifications", "type": "boolean", "description": "If selected, email notification will not be sent." }, "emailNotification": { "title": "Custom address", "type": "string", "description": "Email address to override your account email address." }, "emailSubject": { "title": "Custom subject", "type": "string", "description": "Subject to override the generated subject of your notification email." }, "slackNotification": { "title": "Channel", "type": "string", "description": "Insert Slack channel name, such as #notifications" }, "slackToken": { "title": "Token", "type": "string", "description": "Insert application or user OAuth Access Token to the Slack API. Ask your workspace admin for help if needed." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```