In vanilla Python, your DAGs will all reference the same object, so when your DAG file is parsed and 200 DAGs are created, there will still only be 1 60MB dict object created (I say vanilla because there are obviously ways to create copies of the object).
HOWEVER, you should assume that each Airflow TASK is being run in a different process, and each process is going to load your DAG file when it runs. If resource use is a concern, I suggest you look at out-of-core or persistent storage for the object so you don't need to load the whole thing every time. On Wed, Mar 22, 2017 at 11:20 AM Boris Tyukin <[email protected]> wrote: > hi Jeremiah, thanks for the explanation! > > i am very new to Python so was surprised that it works and my external > dictionary object was still accessible to all dags generated. I think it > makes sense but I would like to confirm one thing and I do not know how to > test it myself. > > do you think that large dictionary object will still be loaded to memory > only once even if I generate 200 dags that will be accessing it? so > basically they will just use a reference to it or they would create a copy > of the same 60Mb structure. > > I hope my question makes sense :) > > On Wed, Mar 22, 2017 at 10:54 AM, Jeremiah Lowin <[email protected]> > wrote: > > > At the risk of oversimplifying things, your DAG definition file is loaded > > *every* time a DAG (or any task in that DAG) is run. Think of it as a > > literal Python import of your dag-defining module: any variables are > loaded > > along with the DAGs, which are then executed. That's why your dict is > > always available. This will work with Celery since it follows the same > > approach, parsing your DAG file to run each task. > > > > (By the way, this is why it's critical that all parts of your Airflow > > infrastructure have access to the same DAGS_FOLDER) > > > > Now it is true that the DagBag loads DAG objects but think of it as more > of > > an "index" so that the scheduler/webserver know what DAGs are available. > > When it's time to actually run one of those DAGs, the executor loads it > > from the underlying source file. > > > > Jeremiah > > > > On Wed, Mar 22, 2017 at 8:45 AM Boris Tyukin <[email protected]> > > wrote: > > > > > Hi, > > > > > > I have a weird question but it bugs my mind. I have some like below to > > > generate dags dynamically, using Max's example code from FAQ. > > > > > > It works fine but I have one large dict (let's call it my_outer_dict) > > that > > > takes over 60Mb in memory and I need to access it from all generated > > dags. > > > Needless to say, i do not want to recreate that dict for every dag as I > > > want to load it to memory only once. > > > > > > To my surprise, if i define that dag outside of my dag definition > code, I > > > can still access it. > > > > > > Can someone explain why and where is it stored? I thought only dag > > > definitions are loaded to dagbag and not the variables outside it. > > > > > > Is it even a good practice and will it work still if I switch to celery > > > executor? > > > > > > > > > def get_dag(i): > > > dag_id = 'foo_{}'.format(i) > > > dag = DAG(dag_id) > > > .... > > > print my_outer_dict > > > > > > my_outer_dict = {} > > > for i in range(10): > > > dag = get_dag(i) > > > globals()[dag.dag_id] = dag > > > > > >
