def __init__(self, ip_addr, kafka_config_infile, s3bucket_config_infile):
if not os.path.exists('./tmp'):
os.makedirs('./tmp')
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s',
filename='./tmp/kafka_producer.log',
filemode='w')
self.logger = logging.getLogger('py4j')
self.kafka_config = helpers.parse_config(kafka_config_infile)
self.s3bucket_config = helpers.parse_config(s3bucket_config_infile)
self.producer = KafkaProducer(bootstrap_servers=ip_addr)
def __init__(self, kafka_configfile, schema_file, s3_configfile):
"""
class constructor that initializes the instance according to the configurations
of the S3 bucket and Kafka
:type kafka_configfile: str path to kafka config file
:type schema_file : str path to schema file
:type s3_configfile : str path to S3 config file
"""
self.kafka_config = helpers.parse_config(kafka_configfile)
self.schema = helpers.parse_config(schema_file)
self.s3_config = helpers.parse_config(s3_configfile)
self.producer = KafkaProducer(
bootstrap_servers=self.kafka_config["BROKERS_IP"])
def __init__(self, s3_configfile, schema_configfile, psql_configfile):
"""
class constructor that initializes the instance according to the configurations
of the S3 bucket, raw data and PostgreSQL table
:type s3_configfile: str path to s3 config file
:type schema_configfile: str path to schema config file
:type psql_configfile: str path to psql config file
"""
self.s3_config = helpers.parse_config(s3_configfile)
self.schema = helpers.parse_config(schema_configfile)
self.psql_config = helpers.parse_config(psql_configfile)
self.sc = pyspark.SparkContext.getOrCreate()
self.sc.setLogLevel("ERROR")
def test_parse_config(self):
# test if correctly parses the config file
conf = {"field1": "val1", "field2": {"subfield1": 2, "subfield2": "3"}}
with patch("__builtin__.open",
mock_open(read_data=json.dumps(conf))) as mock_file:
self.assertEqual(conf, helpers.parse_config(mock_file),
"fail to properly read config from file")
def __init__(self, kafka_configfile, schema_configfile, stream_configfile,
start_offset):
"""
class constructor that initializes the instance according to the configurations
of Kafka (brokers, topic, offsets), data schema and batch interval for streaming
:type kafka_configfile: str path to s3 config file
:type schema_configfile: str path to schema config file
:type stream_configfile: str path to stream config file
:type start_offset: int offset from which to read from partitions of Kafka topic
"""
self.kafka_config = helpers.parse_config(kafka_configfile)
self.stream_config = helpers.parse_config(stream_configfile)
self.schema = helpers.parse_config(schema_configfile)
self.start_offset = start_offset
self.sc = pyspark.SparkContext().getOrCreate()
self.ssc = pyspark.streaming.StreamingContext(
self.sc, self.stream_config["INTERVAL"])
self.sc.setLogLevel("ERROR")
def __init__(self, kafka_config_infile, ecg_spark_config_infile, postgres_config_infile, s3bucket_config_infile,
batch_interval):
if not os.path.exists('./tmp'):
os.makedirs('./tmp')
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s',
filename='./tmp/spark_consumer.log',
filemode='w')
self.logger = logging.getLogger('py4j')
self.logger.setLevel(logging.WARN)
self.ecg_spark_config = helpers.parse_config(ecg_spark_config_infile)
self.postgres_config = helpers.parse_config(postgres_config_infile)
self.s3bucket_config = helpers.parse_config(s3bucket_config_infile)
self.kafka_config = helpers.parse_config(kafka_config_infile)
self.sc = SparkContext(appName='ECGDashboardApp')
self.sc.setLogLevel("FATAL")
self.ssc = StreamingContext(self.sc, batch_interval)
self.logger.warn('Opened spark Context')
self.kafkastream = self.connectToKafkaBrokers()
self.logger.warn('Opened connection to Kafka brokers')
self.a = self.sc.accumulator(0)
def __init__(self, postgres_config_infile):
if not os.path.exists('./tmp'):
os.makedirs('./tmp')
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s',
filename='./tmp/website.log',
filemode='w')
self.logger = logging.getLogger('py4j')
self.postgres_config = helpers.parse_config(postgres_config_infile)
self.cur = self.connectToDB()
self.signal_schema = [
'batchnum', 'signame', 'time', 'ecg1', 'ecg2', 'ecg3'
]
self.hr_schema = ['batchnum', 'signame', 'time', 'hr1', 'hr2', 'hr3']
def __init__(self, subreddit, output_file_name=paths.OUTPUT_FILE):
super().__init__()
self.filter = helpers.Filter(watchlist=True)
self.session = Session()
self.reddit = self.init_connection()
self.subreddit = subreddit
self.output_file_name = output_file_name
self.watchlist = helpers.parse_config("watchlist")
self.watchlist_hits = []
self.submissions = []
self.subs_100_off = []
self.subs_90_off = []
self.subs_80_off = []
self.subs_70_off = []
self.subs_60_off = []
self.subs_50_off = []
self.subs_40_off = []
self.subs_30_off = []
self.subs_20_off = []
self.subs_10_off = []
def __init__(self,
kafka_configfile,
schema_configfile,
stream_configfile,
psql_configfile,
start_offset=0):
"""
class constructor that initializes the instance according to the configurations
of Kafka (brokers, topic, offsets), PostgreSQL database, data schema and batch interval for streaming
:type kafka_configfile: str path to s3 config file
:type schema_configfile: str path to schema config file
:type stream_configfile: str path to stream config file
:type psql_configfile: str path to psql config file
:type start_offset: int offset from which to read from partitions of Kafka topic
"""
SparkStreamerFromKafka.__init__(self, kafka_configfile,
schema_configfile, stream_configfile,
start_offset)
self.psql_config = helpers.parse_config(psql_configfile)
self.sqlContext = pyspark.sql.SQLContext(self.sc)
self.load_batch_data()
self.psql_n = 0
from flask import Flask, jsonify, request, send_file
import ast
import os
import sys
from pymongo import DESCENDING
if os.getcwd().split('/')[-1] != 'fl_sport_betting':
os.chdir("..")
sys.path.append(os.path.abspath(os.curdir))
from storage.mongodb_storage import MongoDBStorage as mdb
import helpers
config = helpers.parse_config('server')
client = mdb().client
app = Flask(__name__)
def group_forecasts(forecasts):
grouped = {}
for forecast in forecasts:
grouped.setdefault(forecast['forecast_type'], [])
grouped[forecast['forecast_type']].append(forecast)
return grouped
def get_file_link(resource, file_name):
pattern = '{server_domain}/api/files/{resource}/{file_name}'
file_link = pattern.format(server_domain=config['domain'],
resource=resource,
file_name=file_name)
return file_link
topic=cfg['kafka_broker']['producer_topic_deals'],
message=json):
influxdb_client.write_deal()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
env_list = ['dev', 'sit', 'prd']
parser.add_argument('-env',
choices=env_list,
metavar=str(env_list),
required=True,
help='parser environment type')
# Парсинг аргументов
args = vars(parser.parse_args())
env_type = args['env']
root_path = os.path.dirname(os.path.realpath(__file__))
cfg = helpers.parse_config(env_type=env_type, root_path=root_path)
logger = helpers.create_timed_rotating_log(cfg=cfg, root_path=root_path)
start_time = datetime.combine(datetime.now().date(), time(4, 0, 0))
if datetime.now() < start_time:
logger.info('service will be starting at 04:00:00')
t.sleep((start_time - datetime.now()).seconds)
read_from_efx()
import os
import sys
sys.path.append('../python')
import boto3
import helpers
# TODO: Fix brokers unavailable issue now switched over the confluent-kafka.
session = 'k1'
kafka_config = helpers.parse_config('../../.config/kafka.config')
s3bucket_config = helpers.parse_config('../../.config/s3bucket.config')
ipaddr = kafka_config['ip-addr'].split(',')
s3 = boto3.client('s3')
obj = s3.get_object(Bucket="testsmalldata", Key="RECORDS_abridged.txt")
records = obj['Body'].read().decode('utf-8').split('\n')
records_per_node = int(round(len(records) / len(ipaddr)))
print(records_per_node)
#Open x number of file threads on y nodes. Visualized using tmux.
os.system('tmux kill-session -t %s' % session)
os.system('tmux new-session -s %s -n bash -d' % session)
for i in range(len(ipaddr)):
start = i * records_per_node
if i == len(ipaddr):
stop = len(records) - 1
else:
stop = (i + 1) * records_per_node
ip = ipaddr[i]
records_interval = records[start:stop]
os.system('echo %s' % ip)
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.operators.bash_operator import BashOperator
import psycopg2
import pandas as pd
import boto3
from StringIO import StringIO
sys.path.append('../../python/')
import helpers
s3bucket_config_infile = '../../../.config/s3bucket.config'
postgres_config_infile = '../../../.config/postgres.config'
s3bucket_config = helpers.parse_config(s3bucket_config_infile)
postgres_config = helpers.parse_config(postgres_config_infile)
schema = ['id', 'batchnum', 'signame', 'time', 'ecg1', 'ecg2', 'ecg3']
def connectToDB(postgres_config):
"""
:return: database cursor
"""
try:
conn = psycopg2.connect(host=postgres_config['host'],
database=postgres_config['database'],
port=postgres_config['port'],
user=postgres_config['user'],
password=postgres_config['password'])
sys.path.append("/home/ubuntu/TaxiOptimizer/helpers/")
import os
import time, json
from app import app
from datetime import datetime
from flask import jsonify, render_template, request
import helpers
from math import floor
from more_itertools import peekable
import psycopg2
import random
# configure connection string for PostgreSQL database
app.dbconfig = helpers.parse_config('/home/ubuntu/TaxiOptimizer/config/postgresql.config')
app.conn_str = "host='%s' dbname='%s' user='******' password='******'" % (app.dbconfig["host"],
app.dbconfig["dbname"],
app.dbconfig["user"],
app.dbconfig["password"])
# set default vehicle_id and the list of coordinates to display
app.vid = []
app.res = []
app.coords = []
# time will be in the range from 10am to 10pm
app.curtime = 600
def print_time(t):
