how to structure beginning and ending synchronous calls using trio? - python-trio

My ask is for structured trio pseudo-code (actual trio function-calls, but dummy worker-does-work-here fill-in) so I can understand and try out good flow-control practices for switching between synchronous and asynchronous processes.
I want to do the following...
load a file of json-data into a data-dict
aside: the data-dict looks like { 'key_a': {(info_dict_a)}, 'key_b': {info_dict_b} }
have each of n-workers...
access that data-dict to find the next record-to-process info-dict
prepare some data from the record-being-processed and post the data to a url
process the post-response to update a 'response' key in the record-being-processed info-dict
update the data-dict with the key's info-dict
overwrite the original file of json-data with the updated data-dict
Aside: I know there are other ways I could achieve my overall goal than the clunky repeated rewrite of a json file -- but I'm not asking for that input; I really would like to understand trio well enough to be able to use it for this flow.
So, the processes that I want to be synchronous:
the get next record-to-process info-dict
the updating of the data-dict
the overwriting of the original file of json-data with the updated data-dict
New to trio, I have working code here ...which I believe is getting the next record-to-process synchronously (via using a trio.Semaphore() technique). But I'm pretty sure I'm not saving the file synchronously.
Learning Go a few years ago, I felt I grokked the approaches to interweaving synchronous and asynchronous calls -- but am not there yet with trio. Thanks in advance.

Here is how I would write the (pseudo-)code:
async def process_file(input_file):
# load the file synchronously
with open(input_file) as fd:
data = json.load(fd)
# iterate over your dict asynchronously
async with trio.open_nursery() as nursery:
for key, sub in data.items():
if sub['updated'] is None:
sub['updated'] = 'in_progress'
nursery.start_soon(post_update, {key: sub})
# save your result json synchronously
save_file(data, input_file)
trio guarantees you that once you exit the async with block every task you spawned is complete so you can safely save your file because no more update will occur.
I also removed the grab_next_entry function because it seems to me that this function will iterate over the same keys (incrementally) at each call (giving a O(n!)) complexity while you could simplify it by just iterating over your dict once (dropping the complexity to O(n))
You don't need the Semaphore either, except if you want to limit the number of parallel post_update calls. But trio offers a builtin mechanism for this as well thanks to its CapacityLimiter that you would use like this:
limit = trio.CapacityLimiter(10)
async with trio.open_nursery() as nursery:
async with limit:
for x in z:
nursery.start_soon(func, x)
UPDATE thanks to #njsmith's comment
So, in order to limit the amount of concurrent post_update you'll rewrite it like this:
async def post_update(data, limit):
async with limit:
...
And then you can rewrite the previous loop like that:
limit = trio.CapacityLimiter(10)
# iterate over your dict asynchronously
async with trio.open_nursery() as nursery:
for key, sub in data.items():
if sub['updated'] is None:
sub['updated'] = 'in_progress'
nursery.start_soon(post_update, {key: sub}, limit)
This way, we spawn n tasks for the n entries in your data-dict, but if there are more than 10 tasks running concurrently, then the extra ones will have to wait for the limit to be released (at the end of the async with limit block).

This code uses channels to multiplex requests to and from a pool of workers. I found the additional requirement (in your code comments) that the post-response rate is throttled, so read_entries sleeps after each send.
from random import random
import time, asks, trio
snd_input, rcv_input = trio.open_memory_channel(0)
snd_output, rcv_output = trio.open_memory_channel(0)
async def read_entries():
async with snd_input:
for key_entry in range(10):
print("reading", key_entry)
await snd_input.send(key_entry)
await trio.sleep(1)
async def work(n):
async for key_entry in rcv_input:
print(f"w{n} {time.monotonic()} posting", key_entry)
r = await asks.post(f"https://httpbin.org/delay/{5 * random()}")
await snd_output.send((r.status_code, key_entry))
async def save_entries():
async for entry in rcv_output:
print("saving", entry)
async def main():
async with trio.open_nursery() as nursery:
nursery.start_soon(read_entries)
nursery.start_soon(save_entries)
async with snd_output:
async with trio.open_nursery() as workers:
for n in range(3):
workers.start_soon(work, n)
trio.run(main)

Related

Python BigQuery Storage Write retry strategy when writing to default stream

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.

Scrapy XMLFeedSpider is very slow

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.

Most elegant way to execute CPU-bound operations in asyncio application?

I am trying to develop part of system that has the following requirement:
send health status to a remote server(every X seconds)
receive request for executing/canceling CPU bound job(s)(for example - clone git repo, compile(using conan) it.. etc).
I am using the socketio.AsyncClient to handle these requirements.
class CompileJobHandler(socketio.AsyncClientNamespace):
def __init__(self, namespace_val):
super().__init__(namespace_val)
// some init variables
async def _clone_git_repo(self, git_repo: str):
// clone repo and return its instance
return repo
async def on_availability_check(self, data):
// the health status
await self.emit('availability_check', " all good ")
async def on_cancel_job(self, data):
// cancel the current job
def _reset_job(self):
// reset job logics
def _reset_to_specific_commit(self, repo: git.Repo, commit_hash: str):
// reset to specific commit
def _compile(self, is_debug):
// compile logics - might be CPU intensive
async def on_execute_job(self, data):
// **request to execute the job(compile in our case)**
try:
repo = self._clone_git_repo(job_details.git_repo)
self._reset_to_specific_commit(repo, job_details.commit_hash)
self._compile(job_details.is_debug)
await self.emit('execute_job_response',
self._prepare_response("SUCCESS", "compile successfully"))
except Exception as e:
await self.emit('execute_job_response',
self._prepare_response(e.args[0], e.args[1]))
finally:
await self._reset_job()
The problem with the following code is that when execute_job message arrives, there is a blocking code running that blocks the whole async-io system.
to solve this problem, I have used the ProcessPoolExecutor and the asyncio event loop, as shown here: https://stackoverflow.com/questions/49978320/asyncio-run-in-executor-using-processpoolexecutor
after using it, the clone/compile functions are executed in another process - so that almost achieves my goals.
the questions I have are:
How can I design the code of the process more elegantly?(right now I have some static functions, and I don't like it...)
one approach is to keep it like that, another one is to pre-initialize an object(let's call it CompileExecuter and create instance of this type, and pre-iniailize it prior starting the process, and then let the process use it)
How can I stop the process in the middle of its execution?(if I received on_cancel_job request)
How can I handle the exception raised by the process correctly?
Other approaches to handle these requirements are welcomed

How to wrap asyncio with iterator

I have the following simplified code:
async def asynchronous_function(*args, **kwds):
statement = await prepare(query)
async with conn.transaction():
async for record in statement.cursor():
??? yield record ???
...
class Foo:
def __iter__(self):
records = ??? asynchronous_function ???
yield from records
...
x = Foo()
for record in x:
...
I don't know how to fill in the ??? above. I want to yield the record data, but it's really not obvious how to wrap asyncio code.
While it is true that asyncio is intended to be used across the board, sometimes it is simply impossible to immediately convert a large piece of software (with all its dependencies) to async. Fortunately there are ways to combine legacy synchronous code with newly written asyncio portions. A straightforward way to do so is by running the event loop in a dedicated thread, and using asyncio.run_coroutine_threadsafe to submit tasks to it.
With those low-level tools you can write a generic adapter to turn any asynchronous iterator into a synchronous one. For example:
import asyncio, threading, queue
# create an asyncio loop that runs in the background to
# serve our asyncio needs
loop = asyncio.get_event_loop()
threading.Thread(target=loop.run_forever, daemon=True).start()
def wrap_async_iter(ait):
"""Wrap an asynchronous iterator into a synchronous one"""
q = queue.Queue()
_END = object()
def yield_queue_items():
while True:
next_item = q.get()
if next_item is _END:
break
yield next_item
# After observing _END we know the aiter_to_queue coroutine has
# completed. Invoke result() for side effect - if an exception
# was raised by the async iterator, it will be propagated here.
async_result.result()
async def aiter_to_queue():
try:
async for item in ait:
q.put(item)
finally:
q.put(_END)
async_result = asyncio.run_coroutine_threadsafe(aiter_to_queue(), loop)
return yield_queue_items()
Then your code just needs to call wrap_async_iter to wrap an async iter into a sync one:
async def mock_records():
for i in range(3):
yield i
await asyncio.sleep(1)
for record in wrap_async_iter(mock_records()):
print(record)
In your case Foo.__iter__ would use yield from wrap_async_iter(asynchronous_function(...)).
If you want to receive all records from async generator, you can use async for or, for shortness, asynchronous comprehensions:
async def asynchronous_function(*args, **kwds):
# ...
yield record
async def aget_records():
records = [
record
async for record
in asynchronous_function()
]
return records
If you want to get result from asynchronous function synchronously (i.e. blocking), you can just run this function in asyncio loop:
def get_records():
records = asyncio.run(aget_records())
return records
Note, however, that once you run some coroutine in event loop you're losing ability to run this coroutine concurrently (i.e. parallel) with other coroutines and thus receive all related benefits.
As Vincent already pointed in comments, asyncio is not a magic wand that makes code faster, it's an instrument that sometimes can be used to run different I/O tasks concurrently with low overhead.
You may be interested in reading this answer to see main idea behind asyncio.

What's the equivalent of moment-yielding (from Tornado) in Twisted?

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.