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<@U0667DNC02Y>: Which possibilities i have to sear...
# ask-ai
j
@Scout: Which possibilities i have to search for the asset with the longest duration from a job run?
s
To find the asset with the longest duration from a job run, you can use the DagsterInstance API to retrieve the run step stats and then identify the asset materialization events. Here's a step-by-step approach based on the documentation: 1. Obtain the 
DagsterInstance
object, which is necessary to interact with the Dagster system. You can get an instance from any context variable or by using 
DagsterInstance.get()
. 2. Use the 
get_run_step_stats
method of the 
DagsterInstance
to retrieve the step statistics for a given run ID. This method will provide you with information about each step's execution, including start and end times. 3. Iterate over the step stats and look for asset materialization events. Each step may have one or more associated asset materialization events. 4. Calculate the duration of each step that materialized an asset by subtracting the start time from the end time. 5. Compare the durations to find the asset with the longest duration. Here is an example function that you could use, based on the documentation:
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python from dagster import DagsterInstance, AssetKey  def get_asset_with_longest_duration(run_id: str, instance: DagsterInstance) -> AssetKey:     run_step_stats = instance.get_run_step_stats(run_id)     longest_duration = 0     asset_with_longest_duration = None      for step_stats in run_step_stats:         for event in step_stats.materialization_events:             duration = step_stats.end_time - step_stats.start_time             if duration > longest_duration:                 longest_duration = duration                 asset_with_longest_duration = event.asset_materialization.asset_key      return asset_with_longest_duration
Please note that this code is a conceptual example based on the documentation's description of the relevant APIs. You would need to adapt it to your specific context and ensure that you have the necessary permissions and setup to access the 
DagsterInstance
and run statistics.
j
@Scout: Where i define the dagster instance and how?
s
There was an error processing your request. Please try again.
j
@Scout: Where i define the dagster instance and how?
s
The 
DagsterInstance
is a core abstraction in Dagster that manages access to storage and other resources. It is typically defined and configured via a 
dagster.yaml
file located in the 
DAGSTER_HOME
directory. Here's how you can define and configure a Dagster instance: 1. Set the 
DAGSTER_HOME
Environment Variable: This environment variable should point to a directory on your filesystem. Dagster will look for the 
dagster.yaml
configuration file in this directory. 2. Create a 
dagster.yaml
Configuration File: In the 
DAGSTER_HOME
directory, create a 
dagster.yaml
file that specifies the configuration for your instance. This can include settings for run storage, event storage, compute logs, and more. Here is an example 
dagster.yaml
file that configures a Dagster instance to use a local filesystem for storage:
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yaml run_storage:   module: dagster.core.storage.runs   class: SqliteRunStorage   config:     base_dir: /path/to/dagster/runs  event_log_storage:   module: dagster.core.storage.event_log   class: SqliteEventLogStorage   config:     base_dir: /path/to/dagster/event_logs  compute_logs:   module: dagster.core.storage.local_compute_log_manager   class: LocalComputeLogManager   config:     base_dir: /path/to/dagster/compute_logs  schedule_storage:   module: dagster.core.storage.schedules   class: SqliteScheduleStorage   config:     base_dir: /path/to/dagster/schedules
3. Access the DagsterInstance in Your Code: Once you have configured your 
dagster.yaml
file and set the 
DAGSTER_HOME
environment variable, you can access the Dagster instance in your code using the 
DagsterInstance.get()
method. Here's an example of how to access the Dagster instance in Python:
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python from dagster import DagsterInstance  # Access the current Dagster instance instance = DagsterInstance.get()
This method will load the instance based on the values in the 
dagster.yaml
file located in the 
DAGSTER_HOME
directory. 4. Use the DagsterInstance in Operations: When you're within the context of a Dagster job (such as within an asset, op, sensor, or schedule), you can access the instance via the context argument:
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python from dagster import asset  @asset def my_asset(context):     instance = context.instance     # You can now use the instance to interact with Dagster's system
Remember to replace 
/path/to/dagster/
with the actual paths where you want Dagster to store its data. The configuration can be adjusted to use different storage options, such as PostgreSQL, depending on your requirements.
j
@Scout: get_run_step_stats give me an empty list. what could be wrong?
s
If the 
get_run_step_stats
method is returning an empty list, there could be several reasons for this behavior. Here are some potential issues to consider: 1. Run ID Does Not Exist: Ensure that the run ID you are querying actually exists in the system. If the run ID is incorrect or does not correspond to any executed run, you will get an empty list. 2. Run Has Not Started or Completed Any Steps: If the run has not started any steps or if it has not completed any steps, there will be no step stats to return. This could happen if the run was created but not launched, or if it failed before any steps were executed. 3. Event Log Storage Issues: The step stats are derived from the event logs. If there is an issue with the event log storage (e.g., it's not properly configured, or there's a permissions issue), then the step stats may not be available. 4. Run Was Executed with 
execute_in_process
: If the run was executed using the 
execute_in_process
method without specifying an instance that writes to persistent storage, the run records will not be available in the event log. To ensure that run records are persisted, you should execute the run with an instance that is configured to write to persistent storage. 5. Filtering or Pagination Parameters: If you are using any filtering or pagination parameters with 
get_run_step_stats
, ensure that they are not too restrictive, causing no results to be returned. 6. Instance Misconfiguration: If the 
DagsterInstance
is not properly configured, it might not be able to access the underlying storage to retrieve the run step stats. 7. Database Connectivity Issues: If you are using a database for run and event log storage, ensure that the database is running and that there are no connectivity issues. To debug this issue, you can: - Verify that the run ID is correct and corresponds to a run that has executed steps. - Check the configuration of your 
DagsterInstance
to ensure it is correct and that it points to the right storage. - Look at the logs for any errors related to storage or database connectivity. - If you are using 
execute_in_process
, make sure to pass a persistent instance if you want to write run records to storage. - Check the event log storage directly to see if there are entries corresponding to the run ID you are querying. If you continue to encounter issues, you may want to check the system logs for any errors or reach out to the Dagster community for support, providing details about your setup and the steps you've taken to troubleshoot the problem.
j
@Scout: How can i access the Execution Duration for a step with dagsterInstance
s
To access the execution duration for a step using the 
DagsterInstance
, you can use the 
get_run_step_stats
method, which provides statistics about each step within a run, including the execution duration. Here's a step-by-step guide on how to do this: 1. Ensure you have a 
DagsterInstance
: You need to have access to a 
DagsterInstance
object to interact with the Dagster system. You can obtain it using 
DagsterInstance.get()
if you're running your code in the same environment as your Dagster deployment. 2. Use the 
get_run_step_stats
method: Call this method with the specific run ID to get statistics about the steps within that run. 3. Calculate the duration: The 
RunStepStats
object returned by 
get_run_step_stats
includes 
start_time
and 
end_time
attributes for each step. You can calculate the duration by subtracting 
start_time
from 
end_time
. Here's an example function that calculates the execution duration for each step in a run:
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python from dagster import DagsterInstance  def get_step_durations(run_id: str) -> dict:     instance = DagsterInstance.get()  # Get the current Dagster instance     step_stats = instance.get_run_step_stats(run_id)  # Get stats for the given run ID          step_durations = {}     for stats in step_stats:         if stats.start_time and stats.end_time:             # Calculate the duration in seconds             duration = (stats.end_time - stats.start_time).total_seconds()             step_durations[stats.step_key] = duration          return step_durations  # Example usage: run_id = "your_run_id_here" durations = get_step_durations(run_id) for step_key, duration in durations.items():     print(f"Step {step_key} took {duration} seconds to execute.")
Make sure to replace 
"your_run_id_here"
with the actual ID of the run you're interested in. Please note that this code assumes that the 
DagsterInstance
is properly configured and that the run with the given 
run_id
has been executed and has step stats recorded. If you encounter an empty list or other issues, please refer to the previous guidance on troubleshooting potential problems with 
get_run_step_stats
.
j
@Scout: can i extract the Execution Duration from the event
s
Yes, you can extract the execution duration from the event logs in Dagster. Each event related to step execution, such as 
STEP_START
and 
STEP_SUCCESS
, includes a timestamp. By comparing the timestamps of the start and success (or failure) events for a given step, you can calculate the execution duration. Here's a general approach to extracting execution duration from events: 1. Access the Event Log: Use the 
DagsterInstance
to fetch the event log entries for a specific run. 2. Filter Step Start and End Events: Look for 
STEP_START
and 
STEP_SUCCESS
(or 
STEP_FAILURE
) events within the event log. 3. Calculate Durations: For each step, calculate the duration by subtracting the timestamp of the 
STEP_START
event from the timestamp of the 
STEP_SUCCESS
or 
STEP_FAILURE
event. The following is a conceptual example based on the documentation's description of the relevant APIs. You would need to adapt it to your specific context:
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python from dagster import DagsterInstance, DagsterEventType  def get_step_execution_duration(run_id: str) -> dict:     instance = DagsterInstance.get()     events = instance.all_logs(run_id)          step_start_times = {}     step_durations = {}          for event in events:         if event.dagster_event:             if event.dagster_event.event_type == DagsterEventType.STEP_START:                 step_key = event.dagster_event.step_key                 step_start_times[step_key] = event.timestamp             elif event.dagster_event.event_type in (DagsterEventType.STEP_SUCCESS, DagsterEventType.STEP_FAILURE):                 step_key = event.dagster_event.step_key                 if step_key in step_start_times:                     start_time = step_start_times[step_key]                     end_time = event.timestamp                     step_durations[step_key] = end_time - start_time          return step_durations  # Example usage: run_id = "your_run_id_here" durations = get_step_execution_duration(run_id) for step_key, duration in durations.items():     print(f"Step {step_key} took {duration} seconds to execute.")
Please replace 
"your_run_id_here"
with the actual ID of the run you're interested in. This code assumes that the 
DagsterInstance
is properly configured and that the run with the given 
run_id
has been executed and has events recorded. Keep in mind that this is a simplified example, and in a real-world scenario, you may need to handle additional complexities such as time zones or missing events. If you encounter any issues, you may need to troubleshoot based on the specific context and configuration of your Dagster instance.
j
@Scout: How can do this on asset level?
s
In Dagster, assets are typically materialized by steps within a job. To determine the execution duration of an asset materialization, you would still look at the step events that correspond to the asset's materialization. However, since assets can be materialized by different steps across different runs, you would need to track which step corresponds to the asset materialization you're interested in. Here's how you can approach this on the asset level: 1. Identify the Step that Materializes the Asset: Determine which step in your job corresponds to the materialization of the asset. This is typically the step where an 
AssetMaterialization
event is logged. 2. Fetch the Event Log for Runs that Materialize the Asset: Use the 
DagsterInstance
to fetch the event log entries for runs that include the materialization of the asset. 3. Filter Asset Materialization Events: Look for 
ASSET_MATERIALIZATION
events within the event log to identify when the asset was materialized. 4. Find Step Start and End Times: For each 
ASSET_MATERIALIZATION
event, find the corresponding 
STEP_START
and 
STEP_SUCCESS
(or 
STEP_FAILURE
) events to determine the start and end times of the step that materialized the asset. 5. Calculate Durations: Calculate the duration by subtracting the timestamp of the 
STEP_START
event from the timestamp of the 
STEP_SUCCESS
or 
STEP_FAILURE
event. Here's a conceptual example of how you might implement this:
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python
from dagster import DagsterInstance, DagsterEventType

def get_asset_materialization_duration(run_id: str, asset_key: str) -> float:
    instance = DagsterInstance.get()
    events = instance.all_logs(run_id)
    
    step_start_time = None
    materialization_duration = None
    
    for event in events:
        if event.dagster_event:
            if event.dagster_event.event_type == DagsterEventType.STEP_START:
                step_start_time = event.timestamp
            elif event.dagster_event.event_type == DagsterEventType.ASSET_MATERIALIZATION:
                if event.dagster_event.asset_key == asset_key:
                    # Assuming the materialization happens at the end of the step
                    end_time = event.timestamp
                    if step_start_time:
                        materialization_duration = end_time - step_start_time
                        break  # Assuming one materialization per run for simplicity
    
    return materialization_duration

# Example usage:
run_id = "your_run_id_here"
asset_key = "your_asset_key_here"
duration = get_asset_materialization_duration(run_id, asset_key)
if duration is not None:
    print(f"Asset {asset_key} took {duration} seconds to materialize.")
Please replace 
"your_run_id_here"
and 
"your_asset_key_here"
with the a
ctual run ID and asset key you're interested in. This code assumes that the 
DagsterInstance
is properly configured and that the run with the given 
run_id
has been executed and has events recorded. This example is simplified and assumes that each asset is materialized once per run. In practice, you may need to handle cases where an asset is materialized multiple times within a run or across different runs. You would also need to ensure that the 
asset_key
matches the key used in the 
AssetMaterialization
event. If you encounter any issues, you may need to troubleshoot based on the specific context and configuration of your Dagster instance.
j
@Scout: here is the problem that we dont get the real asset execution . We get the step exectution and so the asset with the longest run is always the last one from a change from on op to another op. what can i do?
s
The issue you're encountering is a known limitation in Dagster's current asset observability features. As of my knowledge cutoff in early 2023, there isn't a built-in way to directly measure the execution time of an asset independent of the steps that produce it, especially when an asset is produced by multiple steps or when multiple assets are produced by a single step. From the discussions in the Dagster community, it's clear that users are looking for better ways to understand the performance characteristics of assets, but the system doesn't yet provide a straightforward method for isolating asset execution time from step execution time. Given this limitation, here are a few potential workarounds you might consider: 1. Custom Logging: Implement custom logging within your asset definitions to record the start and end times of the asset computation. You can then use this information to calculate the execution duration of the asset. 2. Asset Sensors: Use asset sensors to trigger a job when an asset is materialized and log the time when the job starts and finishes. This won't give you the execution time of the asset within a larger job, but it can be useful if you have jobs dedicated to materializing specific assets. 3. Instrumentation within Assets: If possible, add custom instrumentation within the asset computation logic itself to measure and record the time taken for the computation. You could emit this timing information as metadata in an 
AssetMaterialization
event. 4. Analysis of Downstream Dependencies: If an asset is the input to other assets, you might be able to infer its computation time by analyzing the start times of downstream assets that depend on it. 5. Manual Analysis: If the asset computation is encapsulated within a single op or a known set of ops, you could manually analyze the logs to estimate the asset computation time. 6. Feature Request: If this is a critical need for your use case, consider raising a feature request in the Dagster GitHub repository or contributing to the discussion on existing issues related to asset execution time observability. It's important to note that these workarounds are not ideal and may not provide a perfect solution to your problem. They are suggestions for how you might be able to approximate the execution time of an asset until Dagster provides a more robust solution for asset-level performance metrics.
j
@Scout: Can i acess the event asset metadata information like execution duration
s
As of my last update, Dagster does not directly record execution duration as part of the asset metadata in the 
AssetMaterialization
event. Asset metadata typically includes custom information that you define and attach to an 
AssetMaterialization
or 
AssetObservation
event, such as row count, file size, or descriptive statistics. However, you can manually calculate the execution duration and include it as part of the metadata when you log an 
AssetMaterialization
event. Here's an example of how you might do this within an op that materializes an asset:
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python
from dagster import op, AssetMaterialization, Output, OpExecutionContext
import time

@op
def my_asset_op(context: OpExecutionContext):
    start_time = time.time()
    
    # Your asset computation logic here
    # ...
    
    end_time = time.time()
    duration = end_time - start_time
    
    # Log the asset materialization with the execution duration as metadata
    context.log_event(
        AssetMaterialization(
            asset_key="my_asset",
            metadata={"execution_duration": duration},
        )
    )
    
    # Return the result of your computation
    return Output(...)
In this example, the execution duration is calculated by measuring the time before and after the asset computation logic. The duration is then included in the metadata of the 
AssetMaterialization
event. To access this metadata later, you would query the event log for 
AssetMaterialization
events and extract the metadata from those events. Here's an example of how you might do that:
Copy code
python
from dagster import DagsterInstance, DagsterEventType, AssetKey

def get_asset_execution_duration(run_id: str, asset_key: AssetKey):
    instance = DagsterInstance.get()
    events = instance.all_logs(run_id)
    
    for event in events:
        if event.is_dagster_event and event.dagster_event.event_type == DagsterEventType.ASSET_MATERIALIZATION:
            if event.dagster_event.asset_key == asset_key:
                metadata = event.dagster_event.event_specific_data.materialization.metadata_entries
                # Find the execution duration in the metadata entries
                for entry in metadata:
                    if entry.label == "execution_duration":
                        return entry.entry_data.value
    
    return None

# Example usage:
run_id = "your_run_id_here"
asset_key = AssetKey("my_asset")
duration = get_asset_execution_duration(run_id, asset_key)
if duration is not None:
    print(f"Asset {asset_key} took {duration} seconds to execute.")
This function queries the event log for a specific run ID and looks for 
AssetMaterialization
events for the given asset key. It then extracts the execution duration from the metadata entries attached to the event. Please note
that this approach requires you to manually include the execution duration in the asset metadata when you log the 
AssetMaterialization
event. Dagster does not automatically include execution duration in asset metadata.
j
@Scout: Following error message : AttributeError: 'AssetMaterialization' object has no attribute 'metadata_entries'
s
I apologize for the confusion. The 
AssetMaterialization
event does not have a 
metadata_entries
attribute directly. Instead, you should access the 
metadata_entries
from the 
event_specific_data
of the 
DagsterEvent
object. Here's how you can correctly access the metadata for an asset materialization event:
Copy code
python from dagster import DagsterInstance, DagsterEventType, AssetKey  def get_asset_execution_duration(run_id: str, asset_key: AssetKey):     instance = DagsterInstance.get()     events = instance.all_logs(run_id)          for event in events:         if event.is_dagster_event and event.dagster_event.event_type == DagsterEventType.ASSET_MATERIALIZATION:             if event.dagster_event.asset_key == asset_key:                 # Access the metadata entries from the event_specific_data                 metadata_entries = event.dagster_event.event_specific_data.metadata_entries                 # Find the execution duration in the metadata entries                 for entry in metadata_entries:                     if entry.label == "execution_duration":                         return entry.entry_data.value          return None  # Example usage: run_id = "your_run_id_here" asset_key = AssetKey("my_asset") duration = get_asset_execution_duration(run_id, asset_key) if duration is not None:     print(f"Asset {asset_key} took {duration} seconds to execute.")
In this corrected function, 
metadata_entries
is accessed from 
event.dagster_event.event_specific_data
, which contains the data specific to the 
AssetMaterialization
event, including the metadata. Please ensure that when you log the 
AssetMaterialization
event, you include the execution duration in the metadata as shown in the previous example. This will allow you to retrieve it later using the function above.
j
@Scout: AttributeError: 'StepMaterializationData' object has no attribute 'metadata_entries'
s
I apologize for the confusion earlier. It seems there was a misunderstanding regarding the structure of the 
AssetMaterialization
event and how to access its metadata. The correct way to access metadata for an 
AssetMaterialization
event is through the 
metadata_entries
attribute of the 
Materialization
object within the 
event_specific_data
. Here's how you can access the metadata entries for an 
AssetMaterialization
event:
Copy code
python from dagster import DagsterInstance, DagsterEventType, AssetKey  def get_asset_materialization_metadata(run_id: str, asset_key: AssetKey):     instance = DagsterInstance.get()     events = instance.all_logs(run_id)          for event in events:         if event.is_dagster_event and event.dagster_event.event_type == DagsterEventType.ASSET_MATERIALIZATION:             if event.dagster_event.asset_key == asset_key:                 # Access the metadata entries from the materialization                 materialization = event.dagster_event.event_specific_data.materialization                 metadata_entries = materialization.metadata_entries                 # Extract metadata entries into a dictionary                 metadata_dict = {entry.label: entry.entry_data.value for entry in metadata_entries}                 return metadata_dict          return None  # Example usage: run_id = "your_run_id_here" asset_key = AssetKey("my_asset") metadata = get_asset_materialization_metadata(run_id, asset_key) if metadata is not None:     print(f"Metadata for asset {asset_key}: {metadata}")
In this function, 
metadata_entries
is accessed from the 
materialization
object, which is part of the 
event_specific_data
of the 
DagsterEvent
. The metadata entries are then converted into a dictionary for easier access. Please ensure that when you create the 
AssetMaterialization
event, you include the desired metadata. This function will then be able to retrieve that metadata for you. If you are still encountering issues, please ensure that the 
AssetMaterialization
event is being logged correctly with the metadata you expect to retrieve.
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