I trigger an airflow DAG and pass REST parameters. Upon a REST parameter list, I want to repeat some of the tasks in this DAG. After some tries I got stuck and I am not sure if this is possible.
Here one try:
def determine_rest_params(**kwargs):
values_comma_sep = kwargs["dag_run"].conf["myparam"]
values= []
if values_comma_sep :
values= values_comma_sep .split(",")
return values
def create_task_for_param(p, **kwargs)
# create an operator instance
with airflow.DAG("get_prediction2", default_args=default_args, schedule_interval=None) as dag:
start = DummyOperator(
task_id='start',
dag=dag
)
params = determine_rest_params()
for cur_p in params:
cur_task = create_task_for_param(cur_p)
start >> cur_task
I only see the start task and no other operator. Is it possible in general?
Regards
Oli
You can try this: (Not sure if this will work)
Change the for loop to:
for i in range(0, len(params):
params[i] = create_task_for_param(params[i])
if i == 0:
start.set_downstream(params[i])
else:
params[i-1].set_downstream(parmas[i])
I am also not sure if you are getting the params right way. Just print it and see if you getting params. if yes then the above for loop should work. if not you can try getting params in a start task.
Related
I'm testing python-bigquery-storage to insert multiple items into a table using the _default stream.
I used the example shown in the official docs as a basis, and modified it to use the default stream.
Here is a minimal example that's similar to what I'm trying to do:
customer_record.proto
syntax = "proto2";
message CustomerRecord {
optional string customer_name = 1;
optional int64 row_num = 2;
}
append_rows_default.py
from itertools import islice
from google.cloud import bigquery_storage_v1
from google.cloud.bigquery_storage_v1 import types
from google.cloud.bigquery_storage_v1 import writer
from google.protobuf import descriptor_pb2
import customer_record_pb2
import logging
logging.basicConfig(level=logging.DEBUG)
CHUNK_SIZE = 2 # Maximum number of rows to use in each AppendRowsRequest.
def chunks(l, n):
"""Yield successive `n`-sized chunks from `l`."""
_it = iter(l)
while True:
chunk = [*islice(_it, 0, n)]
if chunk:
yield chunk
else:
break
def create_stream_manager(project_id, dataset_id, table_id, write_client):
# Use the default stream
# The stream name is:
# projects/{project}/datasets/{dataset}/tables/{table}/_default
parent = write_client.table_path(project_id, dataset_id, table_id)
stream_name = f'{parent}/_default'
# Create a template with fields needed for the first request.
request_template = types.AppendRowsRequest()
# The initial request must contain the stream name.
request_template.write_stream = stream_name
# So that BigQuery knows how to parse the serialized_rows, generate a
# protocol buffer representation of our message descriptor.
proto_schema = types.ProtoSchema()
proto_descriptor = descriptor_pb2.DescriptorProto()
customer_record_pb2.CustomerRecord.DESCRIPTOR.CopyToProto(proto_descriptor)
proto_schema.proto_descriptor = proto_descriptor
proto_data = types.AppendRowsRequest.ProtoData()
proto_data.writer_schema = proto_schema
request_template.proto_rows = proto_data
# Create an AppendRowsStream using the request template created above.
append_rows_stream = writer.AppendRowsStream(write_client, request_template)
return append_rows_stream
def send_rows_to_bq(project_id, dataset_id, table_id, write_client, rows):
append_rows_stream = create_stream_manager(project_id, dataset_id, table_id, write_client)
response_futures = []
row_count = 0
# Send the rows in chunks, to limit memory usage.
for chunk in chunks(rows, CHUNK_SIZE):
proto_rows = types.ProtoRows()
for row in chunk:
row_count += 1
proto_rows.serialized_rows.append(row.SerializeToString())
# Create an append row request containing the rows
request = types.AppendRowsRequest()
proto_data = types.AppendRowsRequest.ProtoData()
proto_data.rows = proto_rows
request.proto_rows = proto_data
future = append_rows_stream.send(request)
response_futures.append(future)
# Wait for all the append row requests to finish.
for f in response_futures:
f.result()
# Shutdown background threads and close the streaming connection.
append_rows_stream.close()
return row_count
def create_row(row_num: int, name: str):
row = customer_record_pb2.CustomerRecord()
row.row_num = row_num
row.customer_name = name
return row
def main():
write_client = bigquery_storage_v1.BigQueryWriteClient()
rows = [ create_row(i, f"Test{i}") for i in range(0,20) ]
send_rows_to_bq("PROJECT_NAME", "DATASET_NAME", "TABLE_NAME", write_client, rows)
if __name__ == '__main__':
main()
Note:
In the above, CHUNK_SIZE is 2 just for this minimal example, but, in a real situation, I used a chunk size of 5000.
In real usage, I have several separate streams of data that need to be processed in parallel, so I make several calls to send_rows_to_bq, one for each stream of data, using a thread pool (one thread per stream of data). (I'm assuming here that AppendRowsStream is not meant to be shared by multiple threads, but I might be wrong).
It mostly works, but I often get a mix of intermittent errors in the call to append_rows_stream's send method:
google.cloud.bigquery_storage_v1.exceptions.StreamClosedError: This manager has been closed and can not be used.
google.api_core.exceptions.Unknown: None There was a problem opening the stream. Try turning on DEBUG level logs to see the error.
I think I just need to retry on these errors, but I'm not sure how to best implement a retry strategy here. My impression is that I need to use the following strategy to retry errors when calling send:
If the error is a StreamClosedError, the append_rows_stream stream manager can't be used anymore, and so I need to call close on it and then call my create_stream_manager again to create a new one, then try to call send on the new stream manager.
Otherwise, on any google.api_core.exceptions.ServerError error, retry the call to send on the same stream manager.
Am I approaching this correctly?
Thank you.
The best solution to this problem is to update to the newer lib release.
This problem happens or was happening in the older versions because once the connection write API reaches 10MB, it hangs.
If the update to the newer lib does not work you can try these options:
Limit the connection to < 10MB.
Disconnect and connect again to the API.
I have a very slow performing scraper. I know the bottle neck is not the pipeline (i.e. bi_pipeline) because other scrapers that don't use XMLFeedSpider are very fast. Here is my code:
class MySpider(XMLFeedSpider):
custom_settings = {
'ITEM_PIPELINES': {
'my.pipelines.bi_pipeline': 400
}
}
start_urls=["http://localhost/my.xml"]
iterator = 'iternodes' # This is actually unnecessary, since it's the default value
itertag = 'DEALER'
def parse_node(self, response, node):
my_item = Dealer()
my_item['title'] = node.xpath('TITLE/text()').get()
# send to pipeline to get stored in database
yield my_item
# get the sales for each dealer
yield Request("https://some.domain.com/od/dealers.json?id=" + node.xpath('ID/text()').get(), callback=self.each_sale)
I don't know why but this is very slow. Like 35 items per minute. Where should I look to optimize?
Solved. There was an update script being called on a Trigger in the database. It was a clean up script and the target I was running it on needed a lot of cleaning.
To execute the function periodically, the following command is used.
self.core.schedule(periodic(t), periodic_function)
I would like to disable the above function when certain conditions are met. Anyone knows how?
#GYOON
I would do what the code here does: https://github.com/VOLTTRON/volttron/blob/master/services/core/VolttronCentralPlatform/vcplatform/agent.py#L320
Basically what is going on is in the agent's onstart/onconfig method a function that is to be executed is called in a spawn later greenlet
class MyAgent(Agent):
def __init__(self, **kwargs):
self._periodic_event = None
self.enabled = True
#Core.receiver('onstart')
def onstart(self, sender, **kwargs):
self.core.spawn_later(1, self._my_periodic_event)
def _my_periodic_event(self):
if self._periodic_event is not None:
self._periodic_event.cancel()
# do stuff here within this event loop
if self.enabled:
# note this is an internal volttron function see the referenced link for
# import
now = get_aware_utc_now()
next_update_time = now + datetime.timedelta(seconds=20)
self._periodic_event = self.core.schedule(next_update_time, self._my_periodic_event)
The good thing about this is it allows you to have complete control over the scheduling process. The enable, disable, when to start etc. You can change the number of seconds if you need to with member variables.
Again sorry for the late response on this, but hopefully this helps!
Currently, I am working with a EV3 lego robot that is controlled by several neurons. Now I want to modify the code (running on
python3) in such a way that one can change certain parameter values on the run via the shell (Ubuntu) in order to manipulate the robot's dynamics at any time (and for multiple times). Here is a schema of what I have achieved so far based on a short example code:
from multiprocessing import Process
from multiprocessing import SimpleQueue
import ev3dev.ev3 as ev3
class Neuron:
(definitions of class variables and update functions)
def check_input(queue):
while (True):
try:
new_para = str(input("Type 'parameter=value': "))
float(new_para[2:0]) # checking for float in input
var = new_para[0:2]
if (var == "k="): # change parameter k
queue.put(new_para)
elif (var == "g="): # change parameter g
queue.put(new_para)
else:
print("Error". Type 'k=...' or 'g=...')
queue.put(0) # put anything in queue
except (ValueError, EOFError):
print("New value is not a number. Try again!")
(some neuron-specific initializations)
queue = SimpleQueue()
check = Process(target=check_input, args=(queue,))
check.start()
while (True):
if (not queue.empty()):
cmd = queue.get()
var = cmd[0]
val = float(cmd[2:])
if (var == "k"):
Neuron.K = val
elif (var == "g"):
Neuron.g = val
(updating procedure for neurons, writing data to file)
Since I am new to multiprocessing there are certainly some mistakes concerning taking care of locking, efficiency and so on but the robot moves and input fields occur in the shell. However, the current problem is that it's actually impossible to make an input:
> python3 controller_multiprocess.py
> Type 'parameter=value': New value is not a number. Try again!
> Type 'parameter=value': New value is not a number. Try again!
> Type 'parameter=value': New value is not a number. Try again!
> ... (and so on)
I know that this behaviour is caused by putting the exception of EOFError due to the fact that this error occurs when the exception is removed (and the process crashes). Hence, the program just rushes through the try-loop here and assumes that no input (-> empty string) was made over and over again. Why does this happen? - when not called as a threaded procedure the program patiently waits for an input as expected. And how can one fix or bypass this issue so that changing parameters gets possible as wanted?
Thanks in advance!
Part of the implementation of inlineCallbacks is this:
if isinstance(result, Deferred):
# a deferred was yielded, get the result.
def gotResult(r):
if waiting[0]:
waiting[0] = False
waiting[1] = r
else:
_inlineCallbacks(r, g, deferred)
result.addBoth(gotResult)
if waiting[0]:
# Haven't called back yet, set flag so that we get reinvoked
# and return from the loop
waiting[0] = False
return deferred
result = waiting[1]
# Reset waiting to initial values for next loop. gotResult uses
# waiting, but this isn't a problem because gotResult is only
# executed once, and if it hasn't been executed yet, the return
# branch above would have been taken.
waiting[0] = True
waiting[1] = None
As it is shown, if in am inlineCallbacks-decorated function I make a call like this:
#inlineCallbacks
def myfunction(a, b):
c = callsomething(a)
yield twisted.internet.defer.succeed(None)
print callsomething2(b, c)
This yield will get back to the function immediately (this means: it won't be re-scheduled but immediately continue from the yield). This contrasts with Tornado's tornado.gen.moment (which isn't more than an already-resolved Future with a result of None), which makes the yielder re-schedule itself, regardless the future being already resolved or not.
How can I run a behavior like the one Tornado does when yielding a dummy future like moment?
The equivalent might be something like a yielding a Deferred that doesn't fire until "soon". reactor.callLater(0, ...) is generally accepted to create a timed event that doesn't run now but will run pretty soon. You can easily get a Deferred that fires based on this using twisted.internet.task.deferLater(reactor, 0, lambda: None).
You may want to look at alternate scheduling tools instead, though (in both Twisted and Tornado). This kind of re-scheduling trick generally only works in small, simple applications. Its effectiveness diminishes the more tasks concurrently employ it.
Consider whether something like twisted.internet.task.cooperate might provide a better solution instead.