A bit confused what is an asset job vs ops job explain in si dagster #ask-ai

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A bit confused, what is an asset job vs ops job. e...

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Brett

03/01/2024, 2:48 AM

A bit confused, what is an asset job vs ops job. explain in simple terms @Scout

Scout

03/01/2024, 2:48 AM

In simple terms, an asset job and an ops job in Dagster represent two different approaches to defining and executing data workflows. Asset Job: An asset job is centered around the concept of software-defined assets. A software-defined asset in Dagster couples an asset (like a table, file, or machine learning model) to the function and upstream assets that are used to produce its contents. When you define an asset job, you are focusing on the data assets themselves—what they should be, how they are computed, and how they relate to each other. Asset jobs are declarative, meaning you define what the end state of your data should be, and Dagster figures out how to get there. This approach is recommended for most data pipelines in Dagster, as it aligns with the framework's emphasis on assets as the main abstraction. Ops Job: An ops job, on the other hand, is built around ops and graphs. Ops are the core units of computation in Dagster and contain the logic of an orchestration graph. A graph is a set of interconnected ops (or sub-graphs) that define the flow of data and execution order. When you define an ops job, you are focusing on the individual computational steps (ops) and how they are connected to form a workflow. This approach is more imperative, specifying exactly how each step should be executed. In summary, an asset job is about defining and managing the state of data assets, while an ops job is about defining and managing the execution of computational steps. The documentation suggests that for most data pipelines, starting with software-defined assets is the recommended approach, but ops and graphs can be used in situations where you want to run code without producing data assets, or when you are anchored in task-based workflows.

Brett

03/01/2024, 2:58 AM

give me an example of an ops job and include the project structure @Scout

Scout

03/01/2024, 2:58 AM

Certainly! Below is an example of an ops job in Dagster, along with a suggested project structure. Project Structure:

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my_dagster_project/ │ ├── dagster.yaml  # Dagster instance configuration │ ├── workspace.yaml  # Workspace configuration │ └── my_pipeline/     ├── __init__.py     ├── jobs.py  # Where jobs are defined     └── ops.py  # Where ops are defined

In this structure,

my_pipeline

is a directory that contains your Dagster job and ops definitions. The

jobs.py

file is where you define your job, and the

ops.py

file is where you define your ops. Example of an Ops Job (
jobs.py
):

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python from dagster import job from my_pipeline.ops import fetch_data, process_data, store_data  @job def my_data_processing_job():     raw_data = fetch_data()     processed_data = process_data(raw_data)     store_data(processed_data)

Example Ops Definitions (
ops.py
):

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python from dagster import op  @op def fetch_data():     # Logic to fetch data from a source     return {"data": "raw data"}  @op def process_data(input_data):     # Logic to process the fetched data     processed = f"processed {input_data['data']}"     return {"data": processed}  @op def store_data(processed_data):     # Logic to store the processed data     print(f"Storing data: {processed_data['data']}")

In this example,

my_data_processing_job

is an ops job that consists of three ops:

fetch_data

process_data

, and

store_data

. Each op represents a step in the data processing workflow. The

@job

decorator is used to define the job, and within the job definition, the ops are called in a sequence that respects their data dependencies. To run this job, you would execute it through the Dagster CLI, the Dagster UI, or programmatically using the Python API. The project structure and job definition help maintain organization and clarity as your Dagster project grows.

Brett

03/01/2024, 3:01 AM

in your example what goes into dagster.yaml and workspace.yaml @Scout

Scout

03/01/2024, 3:01 AM

The

dagster.yaml

file is used to configure your Dagster instance, including settings for storage, run launchers, sensors, and schedules. It's typically located in the

DAGSTER_HOME

directory, which is an environment variable that points to a directory on your filesystem where Dagster stores instance-level configuration and history. An example

dagster.yaml

might look like this:

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yaml # dagster.yaml run_storage:   module: dagster_postgres.run_storage   class: PostgresRunStorage   config:     postgres_db:       username: ...       password: ...       hostname: ...       db_name: ...  event_log_storage:   module: dagster_postgres.event_log   class: PostgresEventLogStorage   config:     postgres_db:       username: ...       password: ...       hostname: ...       db_name: ...  schedule_storage:   module: dagster_postgres.schedule_storage   class: PostgresScheduleStorage   config:     postgres_db:       username: ...       password: ...       hostname: ...       db_name: ...  scheduler:   module: dagster.core.scheduler   class: DagsterDaemonScheduler

The

workspace.yaml

file defines code locations for local development or deploying to your infrastructure. It tells Dagster where to find the code that defines your pipelines, jobs, and other components. An example

workspace.yaml

for the project structure provided earlier might look like this:

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yaml # workspace.yaml load_from:   - python_file:       relative_path: my_pipeline/jobs.py

In this

workspace.yaml

file, we specify that we want to load our job definitions from the

jobs.py

file located in the

my_pipeline

directory relative to the location of the

workspace.yaml

file. When you run Dagster tools like

dagit

(the Dagster UI) or the Dagster CLI, they will use this file to locate and load your job definitions. Please note that the actual content of these files will depend on your specific setup, including what database you're using for storage, what kind of run launcher you have, and other instance-level settings. The examples above are just templates to give you an idea of what these files might contain.

Brett

03/01/2024, 3:08 AM

@Scout give a full example of dagster.yaml with all possible configurable options

Scout

03/01/2024, 3:08 AM

As of my knowledge cutoff date in early 2023, providing a full example of

dagster.yaml

with all possible configurable options would be quite extensive and potentially overwhelming, as there are many options that can be configured based on the specific needs of your Dagster instance. These options include configurations for run storage, event log storage, compute log storage, schedule storage, run launchers, executors, and more. However, I can guide you to the Dagster documentation, which provides a comprehensive reference for the

dagster.yaml

file. The documentation includes detailed descriptions of all the configurable components and their respective options. It's important to tailor the

dagster.yaml

file to your deployment's specific requirements, such as the choice of databases, object stores, and other infrastructure components. Here's a simplified example of what a

dagster.yaml

might include, with placeholders for you to fill in based on your infrastructure:

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yaml # dagster.yaml run_storage:   module: dagster_postgres.run_storage   class: PostgresRunStorage   config:     postgres_db:       username: "your_username"       password: "your_password"       hostname: "your_host"       db_name: "your_db_name"  event_log_storage:   module: dagster_postgres.event_log   class: PostgresEventLogStorage   config:     postgres_db:       username: "your_username"       password: "your_password"       hostname: "your_host"       db_name: "your_db_name"  compute_log_manager:   module: dagster.core.storage.local_compute_log_manager   class: LocalComputeLogManager   config:     base_dir: "/path/to/compute/logs"  schedule_storage:   module: dagster_postgres.schedule_storage   class: PostgresScheduleStorage   config:     postgres_db:       username: "your_username"       password: "your_password"       hostname: "your_host"       db_name: "your_db_name"  scheduler:   module: dagster.core.scheduler   class: DagsterDaemonScheduler  run_launcher:   module: dagster.core.launcher   class: DefaultRunLauncher  telemetry:   enabled: false  execution:   multiprocess:     config:       max_concurrent: 4  resources:   io_manager:     config:       s3_bucket: "your_bucket_name"       s3_prefix: "your_prefix"

This example includes configurations for PostgreSQL-backed run storage, event log storage, and schedule storage, a local compute log manager, the default run launcher, and a multiprocess executor with a maximum of four concurrent processes. It also includes a placeholder for an S3-backed IO manager. Please refer to the Dagster documentation for a full list of configurable options and detailed explanations of each component. The exact configuration will depend on the components you use and the infrastructure you deploy to.

Brett

03/01/2024, 3:13 AM

Im not able to get a op job to run.

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from dagster import job, op


@op
def return_five():
    return 5


@op
def add_one(arg):
    return arg + 1


@job
def do_stuff():
    add_one(return_five())

What else do i have to do for it to show up in the dagster ui @Scout

Scout

03/01/2024, 3:13 AM

To get your op job to show up and run in the Dagster UI, you need to ensure a few things are set up correctly: 1. Workspace Configuration: You need a

workspace.yaml

file that tells Dagster where to find your job definitions. This file should be in the Dagster home directory or in the root of your project. Here's an example

workspace.yaml

that points to a Python file:

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yaml     load_from:       - python_file:           relative_path: "path_to_your_python_file.py"           location_name: "my_location"

Replace

"path_to_your_python_file.py"

with the actual path to your Python file containing the job definition. 2. Dagster Instance Configuration: Ensure you have a

dagster.yaml

file that configures your Dagster instance. This file typically resides in the

DAGSTER_HOME

directory. If you haven't set

DAGSTER_HOME

, it defaults to

$HOME/.dagster

. 3. Start Dagit (Dagster UI): Run Dagit, which is the web interface for Dagster. You can start it by running the following command in your terminal:

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shell     dagit -f path_to_your_python_file.py

Or if you have a `workspace.yaml`:

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shell     dagit

Make sure you are in the directory that contains your

workspace.yaml

when you run this command. 4. Access Dagit: Once Dagit is running, it will provide you with a local URL, usually

<http://localhost:3000>

. Open this URL in your web browser to access the Dagster UI. 5. View and Launch Job: In the Dagster UI, navigate to the "Jobs" section. You should see your

do_stuff

job listed there. You can click on it and then click "Launch Run" to execute the job. If you've done all of the above and the job still doesn't show up or run, check for the following common issues: - Make sure there are no syntax errors or exceptions in your Python file. - Ensure that the Python environment where Dagit is running has Dagster and any other necessary dependencies installed. - Check the terminal where you started Dagit for any error messages. - Ensure that the

workspace.yaml

file is correctly formatted and points to the right location of your job definition file. If you continue to face issues, you may want to check the Dagster documentation or community resources for troubleshooting tips or consider reaching out to the Dagster community for support.

Brett

03/01/2024, 3:31 AM

from the jobs overview menu in the dagster ui, how do you isolate a run only a specific task in a job @Scout

Scout

03/01/2024, 3:31 AM

In Dagster, if you want to execute only a specific op (task) within a job, you can use op selection syntax to isolate and run that particular op. This is useful when you want to test or debug individual components of a larger job. Here's how you can do it from the Dagster UI: 1. Navigate to the Jobs Overview: Open the Dagster UI and go to the "Jobs" section. 2. Select the Job: Find the job you want to execute and click on it to view its details. 3. Go to the Launchpad: Click on the "Launchpad" tab within the job's detail view. 4. Configure Op Selection: In the Launchpad, you'll find a configuration editor. Here, you can specify which ops you want to run by using the op selection syntax. The syntax allows you to select ops by name and also supports wildcard characters for selecting multiple ops. For example, if you have an op named

process_data

and you only want to run this op, you would use the following op selection syntax in the configuration editor:

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yaml    ops:      process_data: {}

If you want to run

process_data

and all of its downstream dependencies, you would use:

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yaml    ops:      process_data: "*"

5. Launch the Run: After specifying the op selection in the configuration, click the "Launch Run" button to execute only the selected op(s). Keep in mind that the op selection feature is designed to run the selected op along with its necessary dependencies unless you explicitly use the wildcard syntax to include downstream ops. The exact syntax for op selection may vary depending on the version of Dagster you are using, so it's a good idea to refer to the latest Dagster documentation for the most accurate and up-to-date information.

Brett

03/01/2024, 3:43 AM

this seems to launch all tasks. How can I run only the second task in this job from the launchpad?

Copy code

from dagster import job, op


@op
def return_five():
    return 5


@op
def add_one(arg):
    return arg + 1


@job
def do_stuff():
    add_one(return_five())

@Scout

Scout

03/01/2024, 3:43 AM

To run only the second op (

add_one

) in your job from the Dagster UI's Launchpad, you would need to use the op selection feature. However, since

add_one

depends on the output of

return_five

, you cannot run

add_one

in isolation without first running

return_five

to provide the necessary input. In Dagster, ops (tasks) are designed to run as part of a directed acyclic graph (DAG), where the output of one op is passed as input to another. This means that if an op has dependencies, those dependencies must be executed to provide the context and inputs required for the op to run. If you still want to test or run the

add_one

op separately, you would need to provide a way for it to receive the input it expects without having to execute

return_five

. One approach to achieve this is to refactor your job to allow for more flexible execution paths or to provide hardcoded inputs for testing purposes. However, as of my knowledge cutoff date in early 2023, Dagster does not support executing a single op in the middle of a job's execution graph without running its upstream dependencies. The op selection syntax allows you to select a subset of the graph to run, but it will always include the necessary upstream ops to satisfy the dependencies of the selected ops. For testing individual ops in isolation, you can use the

execute_in_process

method on the job definition, which allows you to specify inputs for each op directly in your test code. This method is typically used for unit testing and debugging purposes. Here's an example of how you might test the

add_one

op in isolation:

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python result = do_stuff.execute_in_process(     inputs={"return_five": 5} ) assert result.success assert result.output_for_node("add_one") == 6

In this example, we're providing a hardcoded input for the

return_five

op when calling

execute_in_process

, which allows us to effectively test the

add_one

op in isolation.

Brett

03/01/2024, 3:53 AM

Then why does the UI let me isolate add_one when you go into the rerun tab @Scout

Scout

03/01/2024, 3:53 AM

I apologize for any confusion. In the Dagster UI, the ability to re-execute a specific op within a job is indeed possible through the re-execution feature. This feature allows you to re-run a job from a specific op, effectively isolating it, assuming that the previous runs have successfully completed and their outputs are available for use as inputs to the op you want to isolate. Here's how you can use the re-execution feature to run only the

add_one

op from the Dagster UI: 1. Complete Initial Run: First, you need to have a successful run of the entire job so that all outputs are stored and available for downstream ops. 2. Navigate to the Runs Page: Go to the "Runs" section in the Dagster UI and find the run you want to re-execute. 3. Select the Run for Re-execution: Click on the run to view its details. 4. Choose Re-execution Option: In the run's detail view, you should see options for re-execution. Click on the "Re-execute" button, which may present you with different re-execution strategies. 5. Select Specific Op: Choose the option that allows you to re-execute from a specific op or step. You can then select the

add_one

op for re-execution. 6. Launch Re-execution: After selecting the op, launch the re-execution. Dagster will use the outputs from the previous run as inputs to the

add_one

op, effectively isolating and running only that op. This feature is particularly useful when you have a long-running job and one of the later ops fails; you can fix the issue and re-execute from the point of failure without having to run the entire job again. Please note that the exact UI elements and steps may vary depending on the version of Dagster you are using. Always refer to the latest Dagster documentation or UI tooltips for the most accurate guidance.

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