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How can I run shell commands on the compute where ...
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n
How can I run shell commands on the compute where the run is starting. I want to run playwright and the error I get is
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playwright._impl._errors.Error: Executable doesn't exist at /root/.cache/ms-playwright/chromium-1097/chrome-linux/chrome
I am using dagster-cloud
s
To run shell commands within a Dagster job, especially when using Dagster Cloud, you can utilize the 
dagster_shell
library, which provides utilities for executing shell commands and scripts. The error you're encountering with Playwright suggests that the executable for Chromium is not found at the expected path, which could be due to various reasons such as incorrect installation, missing dependencies, or file system permissions. Here are some steps you can take to run shell commands and potentially resolve the Playwright issue: 1. Install 
dagster_shell
: Ensure that you have 
dagster_shell
installed in your environment. This library provides the 
create_shell_script_op
and 
create_shell_command_op
functions that can be used to run shell commands and scripts. 2. Create a Shell Op: Use 
create_shell_script_op
or 
create_shell_command_op
to create an op that runs your shell command. For example, to run a shell script, you can do the following:
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python
   from dagster_shell import create_shell_script_op
   from dagster import job

   @job
   def my_job():
       my_shell_op = create_shell_script_op("path_to_your_script.sh", name="my_shell_op")
       my_shell_op()
Replace 
"path_to_your_script.sh"
with the path to your shell script that installs or runs Playwright. 3. Debugging the Playwright Issue: To address the specific Playwright error, you may need to ensure that Playwright is installed correctly and that the Chromium executable is present at the expected location. You might need to run installation commands or set up the environment correctly for Playwright to work. This could involve running 
playwright install
or similar commands as part of your shell script. 4. Permissions and Paths: Since the error message indicates a path under 
/root/.cache
, there might be permission issues or incorrect paths being used. Make sure that the user running the Dagster job has the necessary permissions and that the paths are correctly set for the environment where the job is running. 5. Dagster Cloud Considerations: When running on Dagster Cloud, you need to ensure that the compute environment where your job runs has all the necessary dependencies installed, including Playwright and its browser binaries. You may need to customize your deployment environment to include these dependencies. If you continue to face issues, you can also check the logs for more detailed error messages, which might provide additional clues about what's going wrong. Remember to review the documentation for 
dagster_shell
and Playwright for any platform-specific considerations or additional configuration that might be required. Keep in mind that the above code example is based on the documentation for 
dagster_shell
. If you need to
dynamically construct shell commands during job execution, you should use 
shell_op
instead. Additionally, if you're running into issues with the 
dagster_shell
library on Windows or other platforms, you may need to consider alternative methods or check for known issues and workarounds.
n
@Scout How do i define a job with 2 assets linked to each other and an op. The op should run between the assets. Give me an example
s
In Dagster, you can define a job that includes both assets and ops by using a graph to orchestrate the execution order. You can create a graph where an op is dependent on one asset and another asset is dependent on that op. This way, you can ensure that the op runs between the two assets. Here's an example of how you might define such a job:
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python from dagster import job, op, graph, asset  # Define your first asset @asset def first_asset():     # Your logic to produce the first asset     return "data_from_first_asset"  # Define an op that takes the output of the first asset @op def intermediary_op(input_data):     # Your logic that uses the first asset and prepares data for the second asset     return f"processed_{input_data}"  # Define your second asset, which depends on the op @asset def second_asset(input_data):     # Your logic to produce the second asset, using the output of the intermediary op     return f"data_from_second_asset_based_on_{input_data}"  # Define a graph that orchestrates the execution order @graph def my_graph():     first = first_asset()     processed = intermediary_op(first)     second_asset(processed)  # Create a job from the graph my_job = my_graph.to_job()  # Now you can execute `my_job` to run both assets with the op in between
In this example, 
first_asset
is an asset that produces some data. 
intermediary_op
is an op that takes the output of 
first_asset
and processes it in some way. 
second_asset
is another asset that depends on the output of 
intermediary_op
. The 
my_graph
graph defines the execution order, ensuring that 
intermediary_op
runs after 
first_asset
and before 
second_asset
. Finally, 
my_job
is a job that is created from the graph, which you can execute to run the entire workflow. When you execute 
my_job
, Dagster will ensure that the assets and op are executed in the correct order based on their dependencies.
n
@Scout Can i set env var inside an op?
s
In Dagster, you cannot directly set environment variables inside an op in a way that affects the external environment or other ops, because each op runs in its own process and any changes to environment variables would be local to that process. However, you can read environment variables and pass their values as inputs to ops or use them within the logic of an op. If you need to use an environment variable within an op, you can access it using Python's standard 
os.environ
dictionary. Here's an example:
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python import os from dagster import op  @op def my_op(context):     # Access an environment variable     my_env_var = os.environ.get('MY_ENV_VAR', 'default_value')          # Use the environment variable in the op's logic     <http://context.log.info|context.log.info>(f"The value of MY_ENV_VAR is: {my_env_var}")          # Rest of your op's logic...
In this example, 
my_op
retrieves the value of the environment variable 
MY_ENV_VAR
. If 
MY_ENV_VAR
is not set, it defaults to 
'default_value'
. The value is then used within the op's logic, and you can perform operations based on this value. If you need to set environment variables that will be used by a subprocess spawned by an op, you can set them just before spawning the subprocess within the op's code. Here's an example of how you might do that:
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python import os import subprocess from dagster import op  @op def my_op_with_subprocess(context):     # Set an environment variable for the subprocess     os.environ['MY_ENV_VAR_FOR_SUBPROCESS'] = 'value_for_subprocess'          # Spawn a subprocess that will use the environment variable     result = subprocess.run(['my_subprocess_script.sh'], capture_output=True, text=True, env=os.environ)          # Log the result of the subprocess     <http://context.log.info|context.log.info>(f"Subprocess output: {result.stdout}")          # Rest of your op's logic...
In this example, 
my_op_with_subprocess
sets an environment variable that is then passed to a subprocess. The subprocess will have access to 
MY_ENV_VAR_FOR_SUBPROCESS
because it inherits the environment from the parent process (the op). Remember that changes to 
os.environ
are local to the op and will not affect the environment of other ops or the broader system environment. If you need to set environment variables that persist beyond the scope of an op or affect other parts of your system, you would typically do this outside of Dagster, such as in your deployment environment or CI/CD pipeline.
n
How do I access the current file path of execution when op is running in dagster cloud. I want to create a directory called data in a static locatoinin the folder where the code is running and shell script can access it if I set up an env variable.
@Scout How do I access the current file path of execution when op is running in dagster cloud. I want to create a directory called data in a static locatoinin the folder where the code is running and shell script can access it if I set up an env variable.
s
When running an op in Dagster Cloud, the current file path of execution can be obtained using Python's built-in 
__file__
attribute, which gives you the path of the current Python file. However, when you want to create a directory in a static location that a shell script can access, you need to consider the execution environment provided by Dagster Cloud. Here's a general approach to achieve this: 1. Determine the Base Directory: Use 
__file__
to find the directory of the current Python file and then calculate the base directory from there. 2. Create the Data Directory: Use Python's 
os
module to create the 
data
directory if it doesn't already exist. 3. Set an Environment Variable: Set an environment variable to point to this 
data
directory so that your shell script can access it. Here's an example of how you might write an op to do this:
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python import os from dagster import op  @op def my_op(context):     # Get the directory of the current file     current_file_dir = os.path.dirname(os.path.abspath(__file__))          # Define the path for the 'data' directory     data_dir = os.path.join(current_file_dir, 'data')          # Create the 'data' directory if it doesn't exist     if not os.path.exists(data_dir):         os.makedirs(data_dir)          # Set an environment variable to point to the 'data' directory     os.environ['MY_DATA_DIR'] = data_dir          # Now you can use 'MY_DATA_DIR' in your shell script     # Your shell script should access this directory using the environment variable     # For example: `export DATA_DIR=${MY_DATA_DIR}` in the shell script      # Rest of your op's logic...
However, when running in Dagster Cloud, you need to be aware of the execution environment. The filesystem where your code runs might be ephemeral, meaning that any files or directories you create can be lost after the op finishes executing. This is especially true in serverless execution environments. If you need to ensure that data persists across runs or is accessible to other systems, you should use external storage solutions like cloud storage buckets (e.g., AWS S3, Google Cloud Storage) or a managed database. Additionally, if you're running a shell script from within an op and you want to pass environment variables to it, you can do so using the 
subprocess
module as shown in the previous example. Make sure that the shell script is designed to look for the 
MY_DATA_DIR
environment variable and use it accordingly. Keep in mind that the exact paths and permissions may vary depending on the specifics of your Dagster Cloud setup, so you should test this approach and adjust it based on the behavior you observe in your environment.
n
@Scout write me a custom io manager using postgres to do delset. Also figure out what are the parameters you will need for tis IO manager to work correctly. Show me code of the IOmanage and a small simple asset that returns a dataframe which is stored in postgres by the IO manager
s
Creating a custom I/O manager in Dagster for PostgreSQL that performs a delete-and-set (delset) operation involves writing a class that inherits from 
IOManager
. This I/O manager will need to handle the logic for connecting to the PostgreSQL database, deleting existing data, and writing new data. Here's an example of how you might implement such an I/O manager:
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python
import pandas as pd
import psycopg2
from dagster import IOManager, io_manager, OutputContext, AssetMaterialization, MetadataValue

class PostgresDelsetIOManager(IOManager):
    def __init__(self, hostname, username, password, database, table_name):
        self.hostname = hostname
        self.username = username
        self.password = password
        self.database = database
        self.table_name = table_name

    def connect(self):
        return psycopg2.connect(
            host=self.hostname,
            user=self.username,
            password=self.password,
            dbname=self.database
        )

    def handle_output(self, context: OutputContext, obj: pd.DataFrame):
        conn = self.connect()
        cursor = conn.cursor()

        # Delete existing data
        cursor.execute(f"DELETE FROM {self.table_name}")

        # Write new data
        obj.to_sql(self.table_name, conn, if_exists='replace', index=False)

        # Commit changes and close connection
        conn.commit()
        cursor.close()
        conn.close()

        # Emit an asset materialization event for observability
        context.log_event(
            AssetMaterialization(
                asset_key=context.asset_key,
                description="Updated data in PostgreSQL table.",
                metadata={
                    "hostname": MetadataValue.url(f"postgresql://{self.hostname}"),
                    "table_name": MetadataValue.text(self.table_name),
                },
            )
        )

    def load_input(self, context):
        # This method would be used to load data from PostgreSQL if needed
        pass

# Define the I/O manager using a decorator
@io_manager
def postgres_delset_io_manager(init_context):
    # Here you would get the parameters from the init_context, such as from config
    hostname = init_context.resource_config["hostname"]
    username = init_context.resource_config["username"]
    password = init_context.resource_config["password"]
    database = init_context.resource_config["database"]
    table_name = init_context.resource_config["table_name"]

    return PostgresDelsetIOManager(hostname, username, password, database, table_name)
Now, let's define a simple asset that returns a DataFrame and uses this I/O manager to store the data in PostgreSQL: ```python from dagster import asset import pandas as pd @asset(io_manager_key="postgres_delset_io_man
ager") def my_asset(): # Create a simple DataFrame data = { "id": [1, 2, 3], "name": ["Alice", "Bob", "Charlie"] } df = pd.DataFrame(data) return df
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To use this I/O manager, you would need to configure it in your Dagster repository definition with the necessary parameters:  ```python from dagster import repository, define_asset_job  @repository def my_repository():     return [         my_asset,         define_asset_job("my_asset_job", selection="my_asset"),         postgres_delset_io_manager.configured({             "hostname": "localhost",             "username": "myuser",             "password": "mypassword",             "database": "mydatabase",             "table_name": "mytable"         }),     ]
In this example, the 
postgres_delset_io_manager
is configured with the necessary connection parameters for the PostgreSQL database. The 
my_asset
function creates a simple DataFrame that the I/O manager will then store in the specified PostgreSQL table, replacing any existing data. Please note that this is a simplified example and does not include error handling or other best practices for production use. You should also ensure that your database credentials are securely managed and not hardcoded in your repository definition.
n
@Scout write me a custom io manager using postgres to do delsert. Only delete rows that are matching on some sort of key combination which should be the primary key or unique key combinatoin. Also figure out what are the parameters you will need for tis IO manager to work correctly. Show me code of the IOmanage and a small simple asset that returns a dataframe which is stored in postgres by the IO manager
s
Creating a custom I/O manager in Dagster for PostgreSQL that performs a delete-and-insert (delsert) operation based on a primary or unique key combination requires careful handling of the database operations to ensure that only the matching rows are deleted before inserting new data. Below is an example of how you might implement such an I/O manager. This example assumes that you have a primary or unique key on which you can match rows to delete. For simplicity, let's assume the key is a single column named 
id
.
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python
import pandas as pd
import psycopg2
from dagster import IOManager, io_manager, OutputContext

class PostgresDelsertIOManager(IOManager):
    def __init__(self, hostname, username, password, database, table_name, key_columns):
        self.hostname = hostname
        self.username = username
        self.password = password
        self.database = database
        self.table_name = table_name
        self.key_columns = key_columns

    def connect(self):
        return psycopg2.connect(
            host=self.hostname,
            user=self.username,
            password=self.password,
            dbname=self.database
        )

    def handle_output(self, context: OutputContext, obj: pd.DataFrame):
        conn = self.connect()
        cursor = conn.cursor()

        # Begin a transaction
        conn.autocommit = False

        # Delete rows that match the DataFrame's key columns
        keys = self.key_columns
        for _, row in obj[keys].iterrows():
            conditions = ' AND '.join([f"{key} = %s" for key in keys])
            values = tuple(row[key] for key in keys)
            cursor.execute(f"DELETE FROM {self.table_name} WHERE {conditions}", values)

        # Insert new data
        obj.to_sql(self.table_name, conn, if_exists='append', index=False)

        # Commit the transaction
        conn.commit()

        # Close the cursor and connection
        cursor.close()
        conn.close()

    def load_input(self, context):
        # This method would be used to load data from PostgreSQL if needed
        pass

# Define the I/O manager using a decorator
@io_manager
def postgres_delsert_io_manager(init_context):
    # Here you would get the parameters from the init_context, such as from config
    hostname = init_context.resource_config["hostname"]
    username = init_context.resource_config["username"]
    password = init_context.resource_config["password"]
    database = init_context.resource_config["database"]
    table_name = init_context.resource_config["table_name"]
    key_columns = init_context.resource_config["key_columns"]

    return PostgresDelsertIOManager(hostname, username, password, database, table_name, key_columns)
Now, let's define a simple asset that returns a DataFrame and uses this I/O mana
ger to store the data in PostgreSQL:
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python from dagster import asset import pandas as pd  @asset(io_manager_key="postgres_delsert_io_manager") def my_asset():     # Create a simple DataFrame with an 'id' column as the primary key     data = {         "id": [1, 2, 3],         "name": ["Alice", "Bob", "Charlie"]     }     df = pd.DataFrame(data)     return df
To use this I/O manager, you would need to configure it in your Dagster repository definition with the necessary parameters:
Copy code
python from dagster import repository, define_asset_job  @repository def my_repository():     return [         my_asset,         define_asset_job("my_asset_job", selection="my_asset"),         postgres_delsert_io_manager.configured({             "hostname": "localhost",             "username": "myuser",             "password": "mypassword",             "database": "mydatabase",             "table_name": "mytable",             "key_columns": ["id"]  # Primary or unique key columns         }),     ]
In this example, the 
postgres_delsert_io_manager
is configured with the necessary connection parameters for the PostgreSQL database and the key columns used to match rows for deletion. The 
my_asset
function creates a simple DataFrame that the I/O manager will then store in the specified PostgreSQL table, deleting any existing rows that match on the key columns before inserting the new data. Please note that this is a simplified example and does not include error handling or other best practices for production use. You should also ensure that your database credentials are securely managed and not hardcoded in your repository definition. Additionally, the 
to_sql
method from Pandas requires SQLAlchemy, so make sure to have it installed in your environment.
n
@Scout write me a custom io manager using postgres to do delsert. Only delete rows that are matching on some sort of key combination which should be the primary key or unique key combination. The tablename and keys are different for each asset. It should be passed at the time of creation of the asset in code. Also figure out what are the parameters you will need for tis IO manager to work correctly. Show me code of the IOmanage and a small simple asset that returns a dataframe which is stored in postgres by the IO manager
s
Creating a custom I/O manager in Dagster that performs a delete-and-insert (delsert) operation based on a primary or unique key combination for PostgreSQL requires handling the database operations to ensure that only the matching rows are deleted before inserting new data. Since the table name and keys are different for each asset, these should be passed as parameters at the time of asset creation. Below is an example of how you might implement such an I/O manager and an asset that uses it: ```python import pandas as pd import psycopg2 from dagster import IOManager, io_manager, OutputContext, AssetIn, asset class PostgresDelsertIOManager(IOManager): def __init__(self, hostname, username, password, database): self.hostname = hostname self.username = username self.password = password self.database = database def connect(self): return psycopg2.connect( host=self.hostname, user=self.username, password=self.password, dbname=self.database ) def handle_output(self, context: OutputContext, obj: pd.DataFrame): table_name = context.resource_config['table_name'] key_columns = context.resource_config['key_columns'] conn = self.connect() cursor = conn.cursor() # Begin a transaction conn.autocommit = False # Delete rows that match the DataFrame's key columns keys = key_columns for _, row in obj[keys].iterrows(): conditions = ' AND '.join([f"{key} = %s" for key in keys]) values = tuple(row[key] for key in keys) cursor.execute(f"DELETE FROM {table_name} WHERE {conditions}", values) # Insert new data obj.to_sql(table_name, conn, if_exists='append', index=False) # Commit the transaction conn.commit() # Close the cursor and connection cursor.close() conn.close() def load_input(self, context): # This method would be used to load data from PostgreSQL if needed pass # Define the I/O manager using a decorator @io_manager(config_schema={"hostname": str, "username": str, "password": str, "database": str}) def postgres_delsert_io_manager(init_context): return PostgresDelsertIOManager( hostname=init_context.resource_config["hostname"], username=init_context.resource_config["username"], password=init_context.resource_config["password"], database=init_context.resource_config["database"] ) # Define an asset that returns a DataFrame and uses the custom I/O manager @asset(ins={"table_name": AssetIn(), "key_columns": AssetIn()}) def my_asset(context, table_name, key_columns): # Create a simple DataFrame with an 'id' column as the primary k
ey data = { "id": [1, 2, 3], "name": ["Alice", "Bob", "Charlie"] } df = pd.DataFrame(data) # Set the table name and key columns for the I/O manager to use context.op_config = { "table_name": table_name, "key_columns": key_columns } return df # Example usage of the asset with specific table name and key columns my_asset.configured( {"table_name": "my_table", "key_columns": ["id"]}, name="my_asset_with_config" ) ``` In this example, the 
PostgresDelsertIOManager
class is defined to handle the delsert operation. The 
handle_output
method takes the table name and key columns from the 
context.resource_config
, which are set when the asset is configured. The 
my_asset
asset function is defined with two 
AssetIn
parameters, 
table_name
and 
key_columns
, which are used to configure the I/O manager for each specific asset instance. The 
my_asset.configured
method is used to create a configured version of the asset with a specific table name and key columns. Please note that this is a simplified example and does not include error handling or other best practices for production use. You should also ensure that your database credentials are securely managed and not hardcoded in your repository definition. Additionally, the 
to_sql
method from Pandas requires SQLAlchemy, so make sure to have it installed in your environment.
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