Hello, I am running a spark streaming program on a dataset which is a sequence of numbers in a text file format. I read the text file and load it into a Kafka topic and then run the Spark streaming program on the DStream and finally write the result into an output text file. But I'm getting almost totally different result compared to run the program without Spark streaming.
I'm using maPartitions and it seems it shuffles the data and messes it up. Here is my code in Spark streaming and using Kafka: from __future__ import print_function import sys from operator import add from pyspark.sql import SparkSession from pyspark import SparkContext from pyspark.streaming import StreamingContext from pyspark.streaming.kafka import KafkaUtils import numpy as np from collections import deque import matplotlib.pyplot as plt import pandas as pd #--------------- def classic_sta_lta_py(a): nsta = 2 nlta = 30 # print("a=", a) sta = np.cumsum(a ** 2) print("sta1=", sta) # sta = np.cumsum(a * a, dtype=float) # print("{}. sta array is: ".format(sta)) # Convert to float sta = np.require(sta, dtype=np.float) print("sta2=", sta) # Copy for LTA lta = sta.copy() print("lta=", lta) # Compute the STA and the LTA sta[nsta:] = sta[nsta:] - sta[:-nsta] sta /= nsta lta[nlta:] = lta[nlta:] - lta[:-nlta] lta /= nlta # Pad zeros sta[:nlta - 1] = 0 # Avoid division by zero by setting zero values to tiny float dtiny = np.finfo(0.0).tiny idx = lta < dtiny lta[idx] = dtiny return sta / lta #--------------- def trigger_onset(charfct): """ Calculate trigger on and off times. Given thres1 and thres2 calculate trigger on and off times from characteristic function. This method is written in pure Python and gets slow as soon as there are more then 1e6 triggerings ("on" AND "off") in charfct --- normally this does not happen. :type charfct: NumPy :class:`~numpy.ndarray` :param charfct: Characteristic function of e.g. STA/LTA trigger :type thres1: float :param thres1: Value above which trigger (of characteristic function) is activated (higher threshold) :type thres2: float :param thres2: Value below which trigger (of characteristic function) is deactivated (lower threshold) :type max_len: int :param max_len: Maximum length of triggered event in samples. A new event will be triggered as soon as the signal reaches again above thres1. :type max_len_delete: bool :param max_len_delete: Do not write events longer than max_len into report file. :rtype: List :return: Nested List of trigger on and of times in samples """ # 1) find indices of samples greater than threshold # 2) calculate trigger "of" times by the gap in trigger indices # above the threshold i.e. the difference of two following indices # in ind is greater than 1 # 3) in principle the same as for "of" just add one to the index to get # start times, this operation is not supported on the compact # syntax # 4) as long as there is a on time greater than the actual of time find # trigger on states which are greater than last of state an the # corresponding of state which is greater than current on state # 5) if the signal stays above thres2 longer than max_len an event # is triggered and following a new event can be triggered as soon as # the signal is above thres1 thres1 = 4 thres2 = 2 max_len = 9e99 max_len_delete = False #charfct = [] # for x in iterator: # print(x) # charfct.append(x) ind1 = np.where(charfct > thres1)[0] if len(ind1) == 0: return [] ind2 = np.where(charfct > thres2)[0] # on = deque([ind1[0]]) of = deque([-1]) # determine the indices where charfct falls below off-threshold ind2_ = np.empty_like(ind2, dtype=bool) ind2_[:-1] = np.diff(ind2) > 1 # last occurence is missed by the diff, add it manually ind2_[-1] = True of.extend(ind2[ind2_].tolist()) on.extend(ind1[np.where(np.diff(ind1) > 1)[0] + 1].tolist()) # include last pick if trigger is on or drop it if max_len_delete: # drop it of.extend([1e99]) on.extend([on[-1]]) else: # include it of.extend([ind2[-1]]) # pick = [] while on[-1] > of[0]: while on[0] <= of[0]: on.popleft() while of[0] < on[0]: of.popleft() if of[0] - on[0] > max_len: if max_len_delete: on.popleft() continue of.appendleft(on[0] + max_len) pick.append([on[0], of[0]]) return np.array(pick, dtype=np.int64) # #--------------- def saveRec(rdd): rdd.foreach(lambda rec: open("/Users/zeinab/kafka_2.11-1.1.0/outputFile4.txt", "a").write(rec+"\n")) if __name__ == "__main__": print("hello spark") sc = SparkContext(appName="STALTA") ssc = StreamingContext(sc, 5) broker, topic = sys.argv[1:] # Connect to Kafka kvs = KafkaUtils.createStream(ssc, broker, "raw-event-streaming-consumer",{topic:1}) lines = kvs.map(lambda x: x[1]) ds = lines.flatMap(lambda line: line.strip().split("\n")).map(lambda strelem: float(strelem)) mapped = ds.mapPartitions(lambda i: classic_sta_lta_py(np.array(list(i)))) mapped1 = mapped.mapPartitions(lambda j: trigger_onset(np.array(list(j)))) lines2 = mapped1.map(lambda y: y) mapped2 = lines2.map(lambda w: str(w)) mapped2.count().pprint() mapped2.foreachRDD(saveRec) ssc.start() ssc.awaitTermination() And here is my code without Spark streaming: #!/usr/bin/env python # -*- coding: utf-8 -*- # ------------------------------------------------------------------- # Filename: trigger.py # Purpose: Python trigger/picker routines for seismology. # Author: Moritz Beyreuther, Tobias Megies # Email: moritz.beyreut...@geophysik.uni-muenchen.de # # Copyright (C) 2008-2012 Moritz Beyreuther, Tobias Megies # ------------------------------------------------------------------- from __future__ import (absolute_import, division, print_function, unicode_literals) from future.builtins import * # NOQA from collections import deque import ctypes as C import warnings import numpy as np from obspy import UTCDateTime from obspy.signal.cross_correlation import templates_max_similarity from obspy.signal.headers import clibsignal, head_stalta_t from numpy import genfromtxt from obspy import read def classic_sta_lta_py(a, nsta, nlta): """ Computes the standard STA/LTA from a given input array a. The length of the STA is given by nsta in samples, respectively is the length of the LTA given by nlta in samples. Written in Python. .. note:: There exists a faster version of this trigger wrapped in C called :func:`~obspy.signal.trigger.classic_sta_lta` in this module! :type a: NumPy :class:`~numpy.ndarray` :param a: Seismic Trace :type nsta: int :param nsta: Length of short time average window in samples :type nlta: int :param nlta: Length of long time average window in samples :rtype: NumPy :class:`~numpy.ndarray` :return: Characteristic function of classic STA/LTA """ # The cumulative sum can be exploited to calculate a moving average (the # cumsum function is quite efficient) sta = np.cumsum(a ** 2) # Convert to float sta = np.require(sta, dtype=np.float) # Copy for LTA lta = sta.copy() # Compute the STA and the LTA sta[nsta:] = sta[nsta:] - sta[:-nsta] sta /= nsta lta[nlta:] = lta[nlta:] - lta[:-nlta] lta /= nlta # Pad zeros sta[:nlta - 1] = 0 # Avoid division by zero by setting zero values to tiny float dtiny = np.finfo(0.0).tiny idx = lta < dtiny lta[idx] = dtiny return sta / lta def trigger_onset(charfct, thres1, thres2, max_len=9e99, max_len_delete=False): """ Calculate trigger on and off times. Given thres1 and thres2 calculate trigger on and off times from characteristic function. This method is written in pure Python and gets slow as soon as there are more then 1e6 triggerings ("on" AND "off") in charfct --- normally this does not happen. :type charfct: NumPy :class:`~numpy.ndarray` :param charfct: Characteristic function of e.g. STA/LTA trigger :type thres1: float :param thres1: Value above which trigger (of characteristic function) is activated (higher threshold) :type thres2: float :param thres2: Value below which trigger (of characteristic function) is deactivated (lower threshold) :type max_len: int :param max_len: Maximum length of triggered event in samples. A new event will be triggered as soon as the signal reaches again above thres1. :type max_len_delete: bool :param max_len_delete: Do not write events longer than max_len into report file. :rtype: List :return: Nested List of trigger on and of times in samples """ # 1) find indices of samples greater than threshold # 2) calculate trigger "of" times by the gap in trigger indices # above the threshold i.e. the difference of two following indices # in ind is greater than 1 # 3) in principle the same as for "of" just add one to the index to get # start times, this operation is not supported on the compact # syntax # 4) as long as there is a on time greater than the actual of time find # trigger on states which are greater than last of state an the # corresponding of state which is greater than current on state # 5) if the signal stays above thres2 longer than max_len an event # is triggered and following a new event can be triggered as soon as # the signal is above thres1 ind1 = np.where(charfct > thres1)[0] if len(ind1) == 0: return [] ind2 = np.where(charfct > thres2)[0] # on = deque([ind1[0]]) of = deque([-1]) # determine the indices where charfct falls below off-threshold ind2_ = np.empty_like(ind2, dtype=bool) ind2_[:-1] = np.diff(ind2) > 1 # last occurence is missed by the diff, add it manually ind2_[-1] = True of.extend(ind2[ind2_].tolist()) on.extend(ind1[np.where(np.diff(ind1) > 1)[0] + 1].tolist()) # include last pick if trigger is on or drop it if max_len_delete: # drop it of.extend([1e99]) on.extend([on[-1]]) else: # include it of.extend([ind2[-1]]) # pick = [] while on[-1] > of[0]: while on[0] <= of[0]: on.popleft() while of[0] < on[0]: of.popleft() if of[0] - on[0] > max_len: if max_len_delete: on.popleft() continue of.appendleft(on[0] + max_len) pick.append([on[0], of[0]]) return np.array(pick, dtype=np.int64) def main(): text_file = open("/home/zeinab/Desktop/STALTA/outFile.txt", "r") lines = text_file.read().strip().split("\n") linestofloat = [] for l in lines: linestofloat.append(float(l)) linestofloat = np.array(linestofloat) charfct = classic_sta_lta_py(linestofloat, 2, 20) triggers = trigger_onset(charfct, 4, 2, max_len=9e99, max_len_delete=False) for elem1 in triggers: print(elem1) print(len(triggers)) if __name__ == '__main__': main() import doctest doctest.testmod(exclude_empty=True) And here the input text file(dataset): https://www.dropbox.com/s/wf2cpdlrbwnip14/inputFile.txt?dl=0 <https://www.dropbox.com/s/wf2cpdlrbwnip14/inputFile.txt?dl=0> some useful commands to run the program with Spark streaming: Command to load the input text file into a Kafka topic: bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test < /Users/zeinab/kafka_2.11-1.1.0/bin/inputFile.txt Command to run the program with Spark streaming: bin/spark-submit --jars jars/spark-streaming-kafka-0-8-assembly_2.11-2.3.1.jar examples/src/main/python/streaming/z1kafka.py localhost:2181 test Command to run the program without Spark streaming printing out the result in console: python z1.py If you have any idea that why i am getting different result with and without Spark please let me know. Thank you, Zeinab -- Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/ --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org