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Ken Nguyen

04/14/2022, 11:28 PM
Are there any example flows involving pulling from different branches of the same repo using mapping?
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Kevin Kho

04/14/2022, 11:29 PM
No…..that…sounds odd though lol. What are you trying to do?
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Ken Nguyen

04/14/2022, 11:29 PM
For some information, I’m using ECSRun + pygit2 to pull repos. I’m having some trouble executing the above since I would be cloning a folder of the same name: 
ValueError: 'dbt' exists and is not an empty directory
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Kevin Kho

04/14/2022, 11:32 PM
Ah I see. Then in that case you just have each mapped task create one directory before cloning right?
I dunno if you’ll still run into issues
Try 
return_all=True
maybe for 
DbtShellTask
? But I think what you have should be enough. What is your executor for this Flow? That affects if the tasks run on separate environments, but LocalExecutor and LocalDask will share the same filesystem
DaskExecutor does not share the same filesystem. I think you might write into the workers disk space, but you’ll have issues when two concurrent tasks are on the same worker.
pygit2
clone_repository takes in a local path. I think you can just use that to rename the destination folder?
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Ken Nguyen

04/14/2022, 11:38 PM
I like the idea of cloning into a different destination folder, lemme check it out
I think that works!!!
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Kevin Kho

04/15/2022, 12:14 AM
Nice!
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Ken Nguyen

04/15/2022, 12:15 AM
The reason I say I think is bc my downstream task is a run_dbt task, but I don’t see any logs from it. So I’m not sure if everything is running correctly
I had to wrap my DbtShellTask in another task, do you think this affects logging? 
@task(log_stdout=True)
def run_dbt(schema_name, branch_name, command):
    DbtShellTask(
        return_all=True,
        profile_name="drsquatch_dev",
        environment="prod",
        overwrite_profiles=True,
        log_stdout=True,
        helper_script="cd {}/dbt".format(branch_name),
        log_stderr=True,
        stream_output=True,
        dbt_kwargs={
            "type": "snowflake",
            "account": secret['SNOWFLAKE_ACCOUNT'],
            "user": secret['SNOWFLAKE_USERNAME'],
            "password": secret['SNOWFLAKE_PASSWORD'],
            "role": "PREFECT_DATA_QUAL_TRCK_ROLE",
            "database": "ANALYTICS_DEV",
            "warehouse": secret['SNOWFLAKE_WAREHOUSE'],
            "schema": schema_name,
            "threads": 12,
            "client_session_keep_alive": False,
        },
    )
##============
I have 
log_stdout=True, log_stderr=True, stream_output=True
, do you see any reason why I wouldn’t be seeing logs? I’m unsure if it’s a mapped run thing or a DbtShellTask wrapped in another task thing
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Kevin Kho

04/15/2022, 12:20 AM
Oh btw I tried the Pine Tar. It’s so funny it’s so black haha. And then I tried the Bay Rum candle too. The soaps are really good
I don’t think that affects logging. You should still see some unless you are on DaskExecutor
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Ken Nguyen

04/15/2022, 12:22 AM
Pine Tar haircare stuff scared me the first few times I used it!!! I feel like I’m in a grunge emo rock band 🤣🤣
Hm, I only see Prefect logs, not the logs from DbtShellTask itself 🤔
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Kevin Kho

04/15/2022, 12:29 AM
Ohh cuz it’s not a task anymore when you call .run(). It’s just the Python under the hood (normal function inside a task)
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Ken Nguyen

04/15/2022, 12:38 AM
Oooh, so what is the work around to get the logs? I’m thinking: 1. Instead of wrapping DbtShellTask in another task, I somehow pass parameters into DbtShellTask (couldn’t find a way to do this initially) 2. Some how bring logs from the DbtShellTask out to the wrapped task
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Kevin Kho

04/15/2022, 12:39 AM
Well I think the wrapper task would be the one you’re interested in right? Yes you can likely subclass DbtShellTask and pass your parameters.
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Ken Nguyen

04/15/2022, 12:42 AM
Could you expand more on what that means?
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Kevin Kho

04/15/2022, 12:43 AM
Yeah one second let me type an example
🙌 1
Does something like this make sense to you? 
class MyDbtShellTask(DbtShellTask):

    @defaults_from_attrs("command", "env", "helper_script", "dbt_kwargs")
    def run(
        self,
        command: str = None,
        env: dict = None,
        helper_script: str = None,
        dbt_kwargs: dict = None,
        path_to_clone: str = None
    ) -> str:
        pygit2.clone_repostiry(..,path_to_clone,..)

        super().run(command, env, helper_script, dbt_kwargs)
We get the DbtShellTask, make our own copy. The init will be inherited. You overwrite the run, and then use the 
path_to_clone
for example before feeding the rest of the kwargs to 
DbtShellTask.run()
by using the 
super()
And then now you have a modified task that gets prefect logs
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Ken Nguyen

04/15/2022, 12:52 AM
Ahhh I see! So now when I run dbt, I would use MyDbtShellTask instead of DbtShellTask once I’ve made my own copy?
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Kevin Kho

04/15/2022, 12:53 AM
Yeah and overrode the run method to do the additional things you want (and you can add logging there)
In fact, DbtShellTask just inherits ShellTask and does the same thing by calling the 
super().run()
at the end
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Ken Nguyen

04/15/2022, 1:17 AM
Okay reading through that I’m starting to understand. It’s my first time doing it though so I’ll mess around and probably come back with some questions in a little bit!
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Kevin Kho

04/15/2022, 1:18 AM
Sure! I should be around
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Ken Nguyen

04/15/2022, 2:13 AM
Could you explain/point me to resources that explain these bits in your example (I also see it in the DbtShellTask code as well): 
@defaults_from_attrs
) -> str:
I also don’t quite understand how MyDbtShellTask is able take in new inputs edit: found this to read about the decorator https://docs.prefect.io/api/latest/utilities/tasks.html
Like in my current set up: 
@task(log_stdout=True)
def run_dbt(schema_name, branch_name, command):
    DbtShellTask(
        return_all=True,
        profile_name="drsquatch_dev",
        environment="prod",
        overwrite_profiles=True,
        log_stdout=True,
        helper_script="cd {}/dbt".format(branch_name),
        log_stderr=True,
        stream_output=True,
        dbt_kwargs={
            "type": "snowflake",
            "account": secret['SNOWFLAKE_ACCOUNT'],
            "user": secret['SNOWFLAKE_USERNAME'],
            "password": secret['SNOWFLAKE_PASSWORD'],
            "role": "PREFECT_DATA_QUAL_TRCK_ROLE",
            "database": "ANALYTICS_DEV",
            "warehouse": secret['SNOWFLAKE_WAREHOUSE'],
            "schema": schema_name,
            "threads": 12,
            "client_session_keep_alive": False,
        },
    )
It’s able to take in branch_name, and format that into the helper_script parameter. And then it’s able to take in schema_name, and add the value to the dbt_kwargs dictionary . I can’t quite understand how to emulate the same thing when subclassing DbtShellTask
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Kevin Kho

04/15/2022, 2:32 AM
default from attrs takes the init parameters and passes them to the run method as the defaults if you didn’t specify them. it’s what let you do: 
pg = PostgresExecute(db=...,pw=...)
with Flow(...) as flow:
    pg()
and then pg() will use the init kwargs
when you override the run method, you can add the decorator as well to your new run so it can inherit the init stuff. does that make sense?
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Ken Nguyen

04/15/2022, 2:48 AM
Oo I see, then what about new parameters you want to add? Where can you add logic to process those new parameters?
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Kevin Kho

04/15/2022, 2:53 AM
If you want them in the init, you need to overwrite the init method as well, but for you maybe just adding them to the 
run
as additional inputs will suffice. then you dont need them in 
default_from_attrs
because there is no default from the init
Then you add the logic to process before you call 
super().run()
. The 
DbtShellTask
has a ton of logic and then it calls the 
ShellTask.run()
at the end
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Ken Nguyen

04/15/2022, 3:00 AM
Got it, lets say I wanted to pass schema_name, so that it can be used in dbt_kwargs, where would I add code to say schema_name goes into dbt_kwarg?
I think I'm struggling to understand the syntax and where things should go. The DbtShellTask code is an example that I can look at, but there are lots of things going on so I can't isolate each element and understand it as easily
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Kevin Kho

04/15/2022, 3:05 AM
Yeah so let’s say we’re purely concerned with modifying 
schema_name
with some logic and we want to inject that in 
dbt_kwargs
and that motivates our new task 
class MyDbtShellTask(DbtShellTask):

    def run(command=None, env=None, helper_script=None, dbt_kwargs=None, schema_name_suffix=str):
        dbt_kwargs['schema_name'] = dbt_kwargs['schema_name'] + schema_name_suffix
        super().run(command, env, helper_script, dbt_kwargs)
So dbt_kwargs is a dict and you can update/pull out stuff before you call the super
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Ken Nguyen

04/15/2022, 3:09 AM
Ooo I seeeee
I can put previously defined functions in there as well (e.g. pull_repo function)
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Kevin Kho

04/15/2022, 3:12 AM
Yes!
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Ken Nguyen

04/15/2022, 5:25 PM
Quick update on this, I got everything working, but the 2 dbt jobs don’t seem to be running in parallel. One was run after the other with no overlap, I think it might be due to the way we set up dbt. But do you have any reason to think it could be due to other reasons (maybe DbtShellTask itself or ECSRun functionality)?
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Kevin Kho

04/15/2022, 5:31 PM
parallelization only happens when you use LocalDaskExecutor. are you doing that?
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Ken Nguyen

04/15/2022, 5:50 PM
oh I didn’t know that! I am not using the LocalDaskExecuter, lemme check that out
Looking more into it, thats involving a bit more set up than I anticipated at first to have it run on prefect cloud. Instead of utilising parallel computing to run tasks at the same time, I think it might be simpler to just have 2 separate flows being kicked off at the same time. I’m thinking perhaps having 2 create_flow_runs that passes parameters from parent flow to 2 children flow, thoughts?
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Kevin Kho

04/15/2022, 11:08 PM
Why is it more involved? Just add 
flow.executor = LocalDaskExecutor()
and it should run in parallel
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Ken Nguyen

04/16/2022, 12:26 AM
Oh I’m silly, I completely misunderstood the LocalDaskExecutor
If I have my tasks mapped and my parameters set up like so: 
with Flow("data-quality-tracking-parallel-dbt-run-flow", run_config=RUN_CONFIG, storage=STORAGE, executor=LocalDaskExecutor()) as flow:
    branch_name = Parameter('branch_name', default=["branch1", "branch2"], required=False)
    schema_name = Parameter('schema_name', default=["schema1", "schema2"], required=False)
Do parallel runs take elements from each parameters at the same index? In other words, will flow runs always have 
branch1
be ran with 
schema1
, or is it random and I might get 
branch1
be ran with 
schema2
as well?
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Kevin Kho

04/16/2022, 10:20 PM
If using mapping, order of the list is preserved
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Ken Nguyen

04/16/2022, 10:58 PM
That’s good to know, then I think my issues is due to DbtShellTask not handling mapped runs too well. Both parallel run seems to take on the dbt_kwargs of one of them. For example, I have a MyDbtShellTask that takes in mapped schema_name argument and passes it into dbt_kwargs, but both run seem to have the same dbt_kwargs. Check out the below screenshots of filtered logs of 2 separate flows. Ideally, one line should say 
table model *heap_change*.subscription_renewals
and the other should say 
table model *heap_stage*.subscription_renewals
. But as you can see, each task in both flows seem to just pick 1, and the picking seems random
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Kevin Kho

04/16/2022, 11:00 PM
Can i see the full code? Just remove sensitive stuff
Full Flow code*
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Ken Nguyen

04/16/2022, 11:05 PM
##=========================================##
##================ CONFIGS ================##
##=========================================##
import prefect
from prefect import task, Flow, Parameter, unmapped
from prefect.utilities.tasks import defaults_from_attrs
from prefect.executors import LocalDaskExecutor
from prefect.storage import GitHub
from prefect.run_configs import ECSRun
from prefect.client import Secret
import pygit2

STORAGE = GitHub(repo="dr-squatch/prefect",
                 path="/flows/data_quality_tracking/data_quality_tracking_parallel_dbt_run_flow.py",
                 access_token_secret="GITHUB_ACCESS_TOKEN")

RUN_CONFIG = ECSRun(labels=['dev'],
                    task_role_arn=Secret("ECS_TASK_ROLE_ARN").get(),
                    execution_role_arn=Secret("ECS_EXECUTION_ROLE_ARN").get(),
                    image="kendrsq/prefect-dbt:latest")

logger = prefect.context.get("logger")
##=========================================##
##=========================================##

##=========================================##
##=========== PULL_DBT_REPO TASK ==========##
##=========================================##
@task(name="Clone DBT")
def pull_dbt_repo(branch_name, schema_name):
    git_token = Secret("GITHUB_ACCESS_TOKEN").get()
    dbt_repo_name = "analytics"
    dbt_repo_https = (
        f"https://{git_token}:x-oauth-basic@github.com/dr-squatch/{dbt_repo_name}"
    )

    dbt_repo = pygit2.clone_repository(dbt_repo_https, branch_name)
    branch = dbt_repo.branches['origin/' + branch_name]
    # <http://logger.info|logger.info>("Pulling the {} branch from analytics repo".format(branch_name))
    <http://logger.info|logger.info>("[PULL_REPO] Running dbt task from the {} branch, into the ANALYTICS_DEV.{} schema".format(branch_name, schema_name))
    ref = dbt_repo.lookup_reference(branch.name)
    dbt_repo.checkout(ref)
    <http://logger.info|logger.info>("Cloning of the {} branch complete".format(branch_name))
##=========================================##
##=========================================##

##=========================================##
##=========== DBTSHELLTASK SETUP ==========##
##=========================================##
from prefect.tasks.dbt.dbt import DbtShellTask

secret = Secret("PREFECT_SNOWFLAKE_DETAILS").get()

class MyDbtShellTask(DbtShellTask):

    @defaults_from_attrs("command", "env", "helper_script", "dbt_kwargs")
    def run(
        self,
        command: str = None,
        env: dict = None,
        helper_script: str = None,
        dbt_kwargs: dict = None,
        branch_name: str = None,
        schema_name: str = None,
        # Above 2 arguments are additional arguments to be added on top of regular DbtShellTask arguments
    ) -> str:

        helper_script = "cd {}/dbt".format(branch_name)
        dbt_kwargs['schema'] = schema_name
        <http://logger.info|logger.info>("[DBT] Running dbt task from the {} branch, into the ANALYTICS_DEV.{} schema".format(branch_name, schema_name))
        
        super().run(command=command, env=env, helper_script=helper_script, dbt_kwargs=dbt_kwargs)


run_dbt = MyDbtShellTask(
    return_all=True,
    profile_name="drsquatch_dev",
    environment="prod",
    overwrite_profiles=True,
    log_stdout=True,
    log_stderr=True,
    stream_output=True,
    dbt_kwargs={
        "type": "snowflake",
        "account": secret['SNOWFLAKE_ACCOUNT'],
        "user": secret['SNOWFLAKE_USERNAME'],
        "password": secret['SNOWFLAKE_PASSWORD'],
        "role": "PREFECT_DATA_QUAL_TRCK_ROLE",
        "database": "ANALYTICS_DEV",
        "warehouse": secret['SNOWFLAKE_WAREHOUSE'],
        "threads": 12,
        "client_session_keep_alive": False,
    },
)
##=========================================##
##=========================================##

##=========================================##
##============= OUTPUT_PRINT ==============##
##=========================================##
@task
def output_print(output):
    <http://logger.info|logger.info>(output)
##=========================================##
##=========================================##

##=========================================##
##================= FLOW ==================##
##=========================================##
with Flow("data-quality-tracking-parallel-dbt-run-flow", run_config=RUN_CONFIG, storage=STORAGE, executor=LocalDaskExecutor()) as flow:
    branch_name = Parameter('branch_name', default=["parallel_testing_branch", "parallel_testing_branch2"], required=False)
    schema_name = Parameter('schema_name', default=["heap_stage", "heap_change"], required=False)
    dbt_command = Parameter('dbt_command', default="dbt run -m subscriptions")

    # output_print.map("The inputted branch is {} and the inputted schema is {}".format(branch_name, schema_name))

    pull_repo = pull_dbt_repo.map(
        schema_name=schema_name,
        branch_name=branch_name
        )

    deps = run_dbt.map(
        schema_name=schema_name,
        branch_name=branch_name,
        command=unmapped("dbt deps"),
        task_args={"name": "DBT: Dependencies"},
        upstream_tasks=[pull_repo],
    )

    run_model = run_dbt.map(
        schema_name=schema_name,
        branch_name=branch_name,
        command=unmapped(dbt_command),
        task_args={"name": "DBT: Run model"},
        upstream_tasks=[deps],
    )
##=========================================##
##=========================================##

flow.register(project_name="data_quality_tracking")
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Kevin Kho

04/16/2022, 11:08 PM
Can you try 
upstream_tasks=pull_repo
with no bracket?
k

Ken Nguyen

04/16/2022, 11:10 PM
Should I do 
upstream_tasks=deps
without brackets as well?
k

Kevin Kho

04/16/2022, 11:11 PM
yes but i was just asking for one to test cuz i’m not sure if that’s the issue
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Ken Nguyen

04/16/2022, 11:12 PM
Getting this error: 
for t in upstream_tasks or []:
  File "/Users/kennguyen/Documents/prefect/master_data_qual_env/lib/python3.9/site-packages/prefect/core/task.py", line 998, in __iter__
    raise TypeError(
TypeError: Task is not iterable. If your task returns multiple results, pass `nout` to the task decorator/constructor, or provide a `Tuple` return-type annotation to your task.
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Kevin Kho

04/16/2022, 11:13 PM
Ok let me read a bit more
🙌 1
Wait though, you are saying they take one set of 
dbt_kwargs
, but i am not seeing a second passed in?
Ah never mind I see the intent one sec
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Ken Nguyen

04/16/2022, 11:15 PM
Ya, under the 
class MyDbtShellTask(DbtShellTask):
, I got a 
dbt_kwargs['schema'] = schema_name
in there
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Kevin Kho

04/16/2022, 11:19 PM
Unsure on first glance. Can you just log the dbt_kwargs to see if the schema name change in taking effect? I suspect it is
Yeah it has to if one is working…cuz there is nowhere else to get it
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Ken Nguyen

04/16/2022, 11:21 PM
Yep I see that it prints out 2 different schema for dbt_kwargs, but the execution of the dbt itself only shows 1 schema.
Like dbt task seems to use whichever dbt_kwargs was printed last
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Kevin Kho

04/16/2022, 11:22 PM
Can you try leaving out the task name first?
deps = run_dbt.map(
        schema_name=schema_name,
        branch_name=branch_name,
        command=unmapped("dbt deps"),
        upstream_tasks=pull_repo,
    )
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Ken Nguyen

04/16/2022, 11:24 PM
lemme try it out
Still seems DbtShellTask is still executing with one dbt_kwargs only
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Kevin Kho

04/16/2022, 11:29 PM
This is very puzzling. I can’t pin it down immediately. We know it’s making it in right? It’s just that DbtShellTask is still using the old one?
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Ken Nguyen

04/16/2022, 11:29 PM
I don’t it’s using the old one, it’s the opposite actually
it;s using the latest one
Yep I have logger.info() in there that proves that it’s making it in
Thank you for your help, I gotta be AFK now, hope we can pick this back up later??
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Kevin Kho

04/16/2022, 11:30 PM
Yeah I need to hop off as well but I should be more free later
🙌 1
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