I have a ddf with lots of partitions
ddf = dd.read_parquet("./input-*", engine='fastparquet')
ddf
Dask DataFrame Structure:
datetime ndvi str utm_x utm_y fpath scl_value
npartitions=71
Dask Name: read-parquet, 71 tasks
In each partition I want to run a custom function
my_df_list = list()
for arg_key, arg_value in my_dict_of_args.items() :
ddf_item = ddf_sliced.map_partitions(myfunc,
my_arg1 = arg_key,
my_arg2 = arg_value,
meta = my_meta)
my_df_list.append(ddf_item)
Things start to get tricky there, I have experienced the following command is too much for my pc, taking forever the beginning of the first item computation and eventually depleting all my ram:
dask.compute(*my_df_list)
Example graph using 2 dfs instead 71, dask.visualize(*my_df_list):
But it can handle easily the computation of each partition, one by one:
my_df_list[0].compute()
...
my_df_list[71].compute()
Example graph using 2 dfs instead 71 my_df_list[0].visualize():
Im struggling understanding the difference since to me its the same iteration scheme.
If it is indeed an overhead I will be glad to get some alternative flows to not call .compute on each element manually.
EDIT 1
After posting the graph images I understand dask.compute(*list) boost parallelism to optimize the df readings. See documentation section, Avoid calling compute repeatedly.
Now I can see the real problem is the initialization of the graph and probably my code: even loading 2 dfs instead of 71, my memory is depleted far before the real computation starts, when using dask.compute(*list)
I am trying to clean up and streamline my code and recently came across named aggregation in Pandas(see link)
This note is on the page:
If your aggregation functions requires additional arguments, partially apply them with functools.partial().
Here is the setup code:
from functools import partial as fpart
import pandas as pd
import numpy as np
inputData = {'groupByVar1':['a','a','a','a','a','a','a','a','a','a','b','b','b','b','b','b','b','b','b','b'],
'groupByVar2':[1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4],
'value':[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]}
df = pd.DataFrame(inputData)
upperPercentile = 0.98
lowerPercentile=0.05
Here is the method I have been using
nsgdf = df.groupby(['groupByVar1','groupByVar2'])[['value']].quantile(upperPercentile).rename({'value':'upperPercentileValue'},axis=1).reset_index(drop=False)
nsgdf['lowerPercentileValue'] = df.groupby(['groupByVar1','groupByVar2'])[['value']].quantile(lowerPercentile).values
Here is the method I would like to use:
fpartUpper = fpart(np.quantile,q=upperPercentile)
fpartLower = fpart(np.quantile,q=lowerPercentile)
bdf = df.groupby(['groupByVar1','groupByVar2']).agg(
upperPercentileValue=pd.NamedAgg(column='value',aggfunc=fpartUpper),
lowerPercentileValue=pd.NamedAgg(column='value',aggfunc=fpartLower)
)
The following Error is returned from Pandas:
pandas.core.base.SpecificationError: Function names must be unique, found multiple named quantile
However if I execute the following I actually get a result:
fpartUpper([1,2,3,4,5])
Out[16]: 4.92
How can I get this particular method to work with pandas? What am i missing? Why is Pandas finding multiple definitions for quantile, where as running the bare function causes no issues?
I have to run a really heavy python function as UDF in Spark and I want to cache some data inside UDF. The case is similar to one mentioned here
I am aware that it is slow and wrong.
But the existing infrastructure is in spark and I don't want set up a new infrastructure and deal with data loading/parallelization/fail safety separately for this case.
This is how my spark program looks like:
from mymodule import my_function # here is my function
from pyspark.sql.types import *
from pyspark.sql.functions import udf
from pyspark.sql.session import SparkSession
spark = SparkSession.builder.getOrCreate()
schema = StructType().add("input", "string")
df = spark.read.format("json").schema(schema).load("s3://input_path")
udf1 = udf(my_function, StructType().add("output", "string"))
df.withColumn("result", udf1(df.input)).write.json("s3://output_path/")
The my_function internally calls a method of an object with a slow constructor.
Therefore I don't want the object to be initialized for every entry and I am trying to cache it:
from my_slow_class import SlowClass
from cachetools import cached
#cached(cache={})
def get_cached_object():
# this call is really slow therefore I am trying
# to cache it with cachetools
return SlowClass()
def my_function(input):
slow_object = get_cached_object()
output = slow_object.call(input)
return {'output': output}
mymodule and my_slow_class are installed as modules on each spark machine.
It seems working. The constructor is called only a few times (only 10-20 times for 100k lines in input dataframe). And that is what I want.
My concern is multithreading/multiprocessing inside Spark executors and if the cached SlowObject instance is shared between many parallel my_function calls.
Can I rely on the fact that my_function is called once at a time inside python processes on worker nodes? Does spark use any multiprocessing/multithreading in python process that executes my UDF?
Spark forks Python process to create individual workers, however all processing in the individual worker process is sequential, unless multithreading or multiprocessing is used explicitly by the UserDefinedFunction.
So as long as state is used for caching and slow_object.call is a pure function you have nothing to worry about.
In dask distributed I get the following warning, which I would not expect:
/home/miniconda3/lib/python3.6/site-packages/distributed/worker.py:739: UserWarning: Large object of size 1.95 MB detected in task graph:
(['int-58e78e1b34eb49a68c65b54815d1b158', 'int-5cd ... 161071d7ae7'],)
Consider scattering large objects ahead of time
with client.scatter to reduce scheduler burden and
keep data on workers
future = client.submit(func, big_data) # bad
big_future = client.scatter(big_data) # good
future = client.submit(func, big_future) # good
% (format_bytes(len(b)), s))
The reason I'm suprised is, that I'm doing exactly what the warning is suggesting:
import dask.dataframe as dd
import pandas
from dask.distributed import Client, LocalCluster
c = Client(LocalCluster())
dask_df = dd.from_pandas(pandas.DataFrame.from_dict({'A':[1,2,3,4,5]*1000}), npartitions=10)
filter_list = c.scatter(list(range(2,100000,2)))
mask = c.submit(dask_df['A'].isin, filter_list)
dask_df[mask.result()].compute()
So my question is: Am I doing something wrong or is this a bug?
pandas='0.22.0'
dask='0.17.0'
The main reason why dask is complaining isn't the list, it's the pandas dataframe inside the dask dataframe.
dask_df = dd.from_pandas(pandas.DataFrame.from_dict({'A':[1,2,3,4,5]*1000}), npartitions=10)
You are creating a biggish amount of data locally when you create a pandas dataframe in your local session. Then you operate with it on the cluster. This will require moving your pandas dataframe to the cluster.
You're welcome to ignore these warnings, but in general I would not be surprised if performance here is worse than with pandas alone.
There are a few other things going on here. Your scatter of a list produces a bunch of futures, which may not be what you want. You're calling submit on a dask object, which is usually unnecessary.
So this is a problem that I have not been able to solve, and neither do I know of a good way to make a MCVE out of. Essentially, it has been briefly discussed here, but as the comments show, there was some disagreement, and the final verdict is still out. Hence I am posting a similar question again, hoping to get a better answer.
Background
I have sensor data from a couple of thousand sensors, that I get every minute. My interest lies in forecasting the data. For this I am using the ARIMA family of forecasting models. Long story short, after discussion with the rest of my research group, we decided to use the Arima function available in the R package forecast, instead of the statsmodels implementation of the same.
Problem Definition
Since, I have data from a few thousand sensors, for which I would like to at least analyse a whole week's worth of data (to begin with), and since a week has 7 days, I have 7 times the number of sensors data with me. Essentially a some 14k sensor-day combinations. Finding the best ARIMA order (which minimizes BIC) and forecasting the next day of week data takes about 1 minute for each sensor-day combination. Which means upwards of 11 days to just process one week data on a single core!
This is obviously a waste, when I have 15 more cores just idling away the whole time. So, obviously, this is a problem for parallel processing. Note that each sensor-day combination does not influence any other sensor-day combination. Also, the rest of my code is fairly well profiled, and optimized.
Issue
The issue is that I get this weird error that I cannot catch anywhere. Here is the error reproduced:
Exception in thread Thread-3:
Traceback (most recent call last):
File "/home/kartik/miniconda3/lib/python3.5/threading.py", line 914, in _bootstrap_inner
self.run()
File "/home/kartik/miniconda3/lib/python3.5/threading.py", line 862, in run
self._target(*self._args, **self._kwargs)
File "/home/kartik/miniconda3/lib/python3.5/multiprocessing/pool.py", line 429, in _handle_results
task = get()
File "/home/kartik/miniconda3/lib/python3.5/multiprocessing/connection.py", line 251, in recv
return ForkingPickler.loads(buf.getbuffer())
File "/home/kartik/miniconda3/lib/python3.5/site-packages/rpy2/robjects/robject.py", line 55, in _reduce_robjectmixin
rinterface_level=rinterface_factory(rdumps, rtypeof)
ValueError: Mismatch between the serialized object and the expected R type (expected 6 but got 24)
Here are a few characteristics of this error that I have discovered:
It is raised in the rpy2 package
It has something to do with Thread 3. Since Python is zero indexed, I am guessing this is the fourth thread. Therefore, 4x6 = 24, which adds up to the numbers shown in the final error statement
rpy2 is being used in only one place in my code where it might have to recode returned values into Python types. Protecting that line in try: ... except: ... does not catch that exception
The exception is not raised when I ditch the multiprocessing and call the function within a loop
The exception does not crash the program, just suspends it forever (till I Ctrl+C it into terminating)
All that I tried till now, have had no effect in resolving the error
Things Tried
I have tried everything from extreme procedural coding, with functions to deal with the least cases (that is only one function to be called in parallel), to extreme encapsulation, where the executable block in the if __name__ == '__main__': calls a function which reads in the data, does the necessary grouping, then passes the groups to another function, which imports multiprocessing and calls another function in parallel, which imports the processing module that imports rpy2, and passes the data to the Arima function in R.
Basically, it doesn't matter if rpy2 is called and initialized deep inside function nests, such that it has no idea another instance might be initialized, or if it is called and initialized once, globally, the error is raised if multiprocessing is involved.
Pseudo Code
Here is an attempt to present at least some basic pseudo code such that the error might be reproduced.
import numpy as np
import pandas as pd
def arima_select(y, order):
from rpy2 import robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
pandas2ri.activate()
forecast = importr('forecast')
res = forecast.Arima(y, order=ro.FloatVector(order))
return res
def arima_wrapper(data):
data = data[['tstamp', 'val']]
data.set_index('tstamp', inplace=True)
return arima_select(data, (1,1,1))
def applyParallel(groups, func):
from multiprocessing import Pool, cpu_count
with Pool(cpu_count()) as p:
ret_list = p.map(func, [group for _, group in groups])
return pd.concat(ret_list, keys=[name for name, _ in groups])
def wrapper():
df = pd.read_csv('file.csv', parse_dates=[1], infer_datetime_format=True)
df['day'] = df['tstamp'].dt.day
res = applyParallel(df.groupby(['sensor', 'day']), arima_wrapper)
print(res)
Obviously, the above code can be encapsulated further, but I think it should reproduce the error quite accurately.
Data Sample
Here is the output of print(data.head(6)) when placed immediately below data.set_index('tstamp', inplace=True) in arima_wrapper from the pseudo code above:
Or alternatively, data for a sensor, for a whole week can be generated simply with:
def data_gen(start_day):
r = pd.Series(pd.date_range('2016-09-{}'.format(str(start_day)),
periods=24*60, freq='T'),
name='tstamp')
d = pd.Series(np.random.randint(10, 80, 1440), name='val')
s = pd.Series(['sensor1']*1440, name='sensor')
return pd.concat([s, r, d], axis=1)
df = pd.concat([data_gen(day) for day in range(1,8)], ignore_index=True)
Observations and Questions
The first observation is that this error is only raised when multiprocessing is involved, not when the function (arima_wrapper) is called in a loop. Therefore, it must be associated somehow with multiprocessing issues. R is not very multiprocess friendly, but when written in the way shown in the pseudo code, each instance of R should not know about the existence of the other instances.
The way the pseudo code is structured, there must be an initialization of rpy2 for each call inside the multiple subprocesses spawned by multiprocessing. If that were true, each instance of rpy2 should have spawned its own instance of R, which should just execute one function, and terminate. That would not raise any errors, because it would be similar to the single threaded operation. Is my understanding here accurate, or am I completely or partially missing the point?
Were all instances of rpy2 to somehow share an instance of R, then I might reasonably expect the error. What is true: is R shared among all instances of rpy2, or is there an instance of R for each instance of rpy2?
How might this issue be overcome?
Since SO hates question threads with multiple questions in them, I will prioritize my questions such that partial answers will be accepted. Here is my priority list:
How might this issue be overcome? A working code example that does not raise the issue will be accepted as answer even if it does not answer any other question, provided no other answer does better, or was posted earlier.
Is my understanding of Python imports accurate, or am I missing the point about multiple instances of R? If I am wrong, how should I edit the import statements such that a new instance is created within each subprocess? Answers to this question are likely to point me towards a probable solution, and will be accepted, provided no answer does better, or was posted earlier
Is R shared among all instances of rpy2 or is there an instance of R for each instance of rpy2? Answers to this question will be accepted only if they lead to a resolution of the problem.
(...) Long story short (...)
Really ?
How might this issue be overcome? A working code example that does not
raise the issue will be accepted as answer even if it does not answer
any other question, provided no other answer does better, or was
posted earlier.
Answers may leave a quite bit of work on your end...
Is my understanding of Python imports accurate, or am
I missing the point about multiple instances of R? If I am wrong, how
should I edit the import statements such that a new instance is
created within each subprocess? Answers to this question are likely to
point me towards a probable solution, and will be accepted, provided
no answer does better, or was posted earlier
Python packages/modules are "uniquely" imported across your process which means that all code using the package/module within the process is using the same single import (you don't have a copy per import in a given block).
Because of this, I'd recommend to use an initialization function when creating your Pool rather than repeatedly import rpy2 and setup the conversion each time a task is sent to a worker. You may also gain in performance if each task is short.
def arima_select(y, order):
# FIXME: check whether the rpy2.robjects package
# should be (re) imported as ro to be visible
res = forecast.Arima(y, order=ro.FloatVector(order))
return res
forecast = None
def worker_init():
from rpy2 import robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
pandas2ri.activate()
global forecast
forecast = importr('forecast')
def applyParallel(groups, func):
from multiprocessing import Pool, cpu_count
with Pool(cpu_count(), worker_init) as p:
ret_list = p.map(func, [group for _, group in groups])
return pd.concat(ret_list, keys=[name for name, _ in groups])
Is R shared among all
instances of rpy2 or is there an instance of R for each instance of
rpy2? Answers to this question will be accepted only if they lead to a
resolution of the problem.
rpy2 is making R available by linking its C shared library. One such library per Python process, and that's as a stateful library (R not able to handle concurrency). I think that your issue has more to do with object serialization (see http://rpy2.readthedocs.io/en/version_2.8.x/robjects_serialization.html#object-serialization) than with concurrency.
What is happening is some apparent confusion when reconstructing the R objects after Python pickled the rpy2 object. More specifically, when looking that the R object types mentioned in the error message:
>>> from rpy2.rinterface import str_typeint
>>> str_typeint(6)
'LANGSXP'
>>> str_typeint(24)
'RAWSXP'
I am guessing that the R object returned by forecast.Arima contains an unevaluated R expression (for example the call that lead to that result object) and when serializing and unserializing it is coming back as something different (a raw vector of bytes). This is possibly a bug with R's own serialization mechanism (since rpy2 is using it behind the hood). For now, and solve your issue, you may want to extract what forecast.Arima what you care most about and only return that from the function call ran by the worker.
The following changes to the arima_select function in the pesudo code presented in the question work:
import numpy as np
import pandas as pd
from rpy2 import rinterface as ri
ri.initr()
def arima_select(y, order):
def rimport(packname):
as_environment = ri.baseenv['as.environment']
require = ri.baseenv['require']
require(ri.StrSexpVector([packname]),
quiet = ri.BoolSexpVector((True, )))
packname = ri.StrSexpVector(['package:' + str(packname)])
pack_env = as_environment(packname)
return pack_env
frcst = rimport("forecast")
args = (('y', ri.FloatSexpVector(y)),
('order', ri.FloatSexpVector(order)),
('include.constant', ri.StrSexpVector(const)))
return frcst['Arima'].rcall(args, ri.globalenv)
Keeping the rest of the pseudo code the same. Note that I have since optimized the code further, and it does not require all the functions presented in the question. Basically, the following is necessary and sufficient:
import numpy as np
import pandas as pd
from rpy2 import rinterface as ri
ri.initr()
def arima(y, order=(1,1,1)):
# This is the same as arima_select above, just renamed to arima
...
def applyParallel(groups, func):
from multiprocessing import Pool, cpu_count
with Pool(cpu_count(), worker_init) as p:
ret_list = p.map(func, [group for _, group in groups])
return pd.concat(ret_list, keys=[name for name, _ in groups])
def main():
# Create your df in your favorite way:
def data_gen(start_day):
r = pd.Series(pd.date_range('2016-09-{}'.format(str(start_day)),
periods=24*60, freq='T'),
name='tstamp')
d = pd.Series(np.random.randint(10, 80, 1440), name='val')
s = pd.Series(['sensor1']*1440, name='sensor')
return pd.concat([s, r, d], axis=1)
df = pd.concat([data_gen(day) for day in range(1,8)], ignore_index=True)
applyParallel(df.groupby(['sensor', pd.Grouper(key='tstamp', freq='D')]),
arima) # Note one may use partial from functools to pass order to arima
Note that I also do not call arima directly from applyParallel since my goal is to find the best model for the given series (for a sensor and day). I use a function arima_wrapper to iterate through the order combinations, and call arima at each iteration.