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configure.py
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configure.py
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from enum import Enum
import json
import os
from pathlib import Path
from ast import literal_eval
#from util.utils import string_to_bool
'''
from config import Heuristic, AZ_KEY_NAME, AZ_PUB_KEY_PATH, AZ_PRV_KEY_PATH, \
AWS_ACCESS_ID, AWS_SECRET_KEY, AZ_APPLICATION_ID, AZ_SECRET, AZ_SUBSCRIPTION_ID, AZ_TENANT_ID,\
ROOT_DIR, CLUSTERS_CFG_FILENAME, CLUSTERS_CFG_PATH, \
PROVIDER, AZ_LOCATION, AZ_SIZE, AZ_IMAGE, AZ_VHD_IMAGE, \
AZ_RESOURCE_GROUP, AZ_STORAGE_ACCOUNT, AZ_SA_SKU, AZ_SA_KIND, AZ_NETWORK, \
AZ_SUBNET, AZ_SECURITY_GROUP, DATA_AMI, CREDENTIAL_PROFILE, REGION, \
KEY_PAIR_PATH, SECURITY_GROUP, PRICE, INSTANCE_TYPE, NUM_INSTANCE, \
EBS_OPTIMIZED, REBOOT, KILL_JAVA, NUM_RUN, PROCESS_ON_SERVER, INSTALL_PYTHON3, \
CLUSTER_ID, TAG, HDFS_MASTER, SPARK_SEQ_HOME, SPARK_2_HOME, C_SPARK_HOME, \
SPARK_HOME, LOG_LEVEL, UPDATE_SPARK, UPDATE_SPARK_MASTER, UPDATE_SPARK_DOCKER, \
ENABLE_EXTERNAL_SHUFFLE, LOCALITY_WAIT, LOCALITY_WAIT_NODE, LOCALITY_WAIT_PROCESS, \
LOCALITY_WAIT_RACK, CPU_TASK, RAM_DRIVER, RAM_EXEC, OFF_HEAP, OFF_HEAP_BYTES, \
CORE_VM, CORE_HT_VM, DISABLE_HT, ALPHA, BETA, DEADLINE, MAX_EXECUTOR, OVER_SCALE,\
K, TI, T_SAMPLE, CORE_QUANTUM, CORE_MIN, CPU_PERIOD, RUN, SYNC_TIME, PREV_SCALE_FACTOR, \
BENCH_NUM_TRIALS, BENCHMARK_PERF, BENCHMARK_BENCH, BENCH_CONF, TERMINATE, HDFS, \
HADOOP_CONF, HADOOP_HOME, DELETE_HDFS, HEURISTIC, STAGE_ALLOCATION, CORE_ALLOCATION, \
DEADLINE_ALLOCATION, CONFIG_DICT, BENCH_LINES, PRIVATE_KEY_PATH, PRIVATE_KEY_NAME, \
TEMPORARY_STORAGE, UPDATE_SPARK_BENCH, UPDATE_SPARK_PERF, SPARK_PERF_FOLDER, \
CLUSTER_MAP, VAR_PAR_MAP, INPUT_RECORD, NUM_TASK, SCALE_FACTOR
'''
#import config as c
class Config(object):
"""
Configuration class for cSpark test benchmark
"""
class Heuristic(Enum):
CONTROL = 0
FIXED = 1
CONTROL_UNLIMITED = 2
REGION = "" #"""Region of AWS to use"""
DATA_AMI = {} #"""AMI id for region and availability zone"""
PROVIDER = "" #"""Provider to be used"""
AZ_KEY_NAME = "" #""" name of Azure private key """
AZ_PUB_KEY_PATH = "" #""" path of Azure public key """
AZ_PRV_KEY_PATH = "" #""" path of Azure private key """
AWS_ACCESS_ID = "" #""" Azure access id """
AWS_SECRET_KEY = "" #""" Azure secret key """
AZ_APPLICATION_ID = "" #""" Azure application id """
AZ_SECRET = "" #""" Azure secret """
AZ_SUBSCRIPTION_ID = "" #""" Azure subscription id """
AZ_TENANT_ID = "" #""" Azure tenant id """
KEY_PAIR_PATH = "" #""" AWS keypair path """
AZ_LOCATION = "" #"""AZURE Datacenter Location"""
AZ_SIZE = "" #"""AZURE VM Size"""
AZ_IMAGE = "" #"""AZURE VM Image"""
AZ_VHD_IMAGE = {} #"""AZURE VHD Image"""
AZ_RESOURCE_GROUP = "" #"""AZURE Resource Group"""
AZ_STORAGE_ACCOUNT = "" #"""AZURE Storage Group"""
AZ_SA_SKU = "" #"""AZURE Storage SKU"""
AZ_SA_KIND = "" #"""AZURE Storage Kind"""
AZ_NETWORK = "" #"""AZURE Virtual Network"""
AZ_SUBNET = "" #"""AZURE Subnet"""
AZ_SECURITY_GROUP = "" #"""AZURE Security Group"""
CREDENTIAL_PROFILE = "" #"""Credential profile name of AWS"""
SECURITY_GROUP = "" #"""Security group of the instance"""
PRICE = "" #"""Aws spot instance
INSTANCE_TYPE = "" #"""Instance type"""
NUM_INSTANCE = 0 #"""Number of instance to use"""
EBS_OPTIMIZED = False
REBOOT = False #"""Reboot the instances of the cluster"""
KILL_JAVA = True #"""Kill every java application on the cluster"""
NUM_RUN = 1 #"""Number of run to repeat the benchmark"""
PROCESS_ON_SERVER = False #"""Download benchmark logs and generate profiles and plots on server """
INSTALL_PYTHON3 = True #"""Install Python3 on cspark master"""
CLUSTER_ID = "CSPARKWORK" #"""Id of the cluster with the launched instances"""
TAG = [{
"Key": "ClusterId",
"Value": CLUSTER_ID
}] #"""Cluster Tag name"""
HDFS_MASTER = "" #"""use private ip for azure!"""
SPARK_SEQ_HOME = "/opt/spark-seq/" # "sequential" Spark home directory
SPARK_2_HOME = "/opt/spark/" # regular Spark home directory
C_SPARK_HOME = "/usr/local/spark/" # "controlled" spark home directory
SPARK_HOME = C_SPARK_HOME # Location of Spark in the ami"""
LOG_LEVEL = "INFO" # Spark log verbosity level
GIT_BRANCH = "exp_2018" # was "xSpark-1.0"
UPDATE_SPARK = False #"""Git pull and build Spark of all the cluster"""
UPDATE_SPARK_MASTER = False #"""Git pull and build Spark only of the master node"""
UPDATE_SPARK_DOCKER = False #"""Pull the docker image in each node of the cluster"""
UPDATE_SPARK_BENCH = False
UPDATE_SPARK_PERF = False
SKEW_TEST = False
ENABLE_EXTERNAL_SHUFFLE = "true"
LOCALITY_WAIT = 0
LOCALITY_WAIT_NODE = 0
LOCALITY_WAIT_PROCESS = 1
LOCALITY_WAIT_RACK = 0
CPU_TASK = 1
RAM_DRIVER = "100g"
RAM_EXEC = '"100g"'
OFF_HEAP = False
OFF_HEAP_BYTES = 30720000000
CORE_VM = 16 # max 16
CORE_HT_VM = 16 # max 16
DISABLE_HT = False
ALPHA = 0.95
BETA = 0.33
DEADLINE = 37600
MAX_EXECUTOR = 8
OVER_SCALE = 2
K = 50
TI = 12000
T_SAMPLE = 1000
CORE_QUANTUM = 0.05
CORE_MIN = 0.0
CPU_PERIOD = 100000
# BENCHMARK
RUN = True
SYNC_TIME = 1
PREV_SCALE_FACTOR = 0 #"""*Important Settings* if it is equals to SCALE_FACTOR no need to generate new data on HDFS"""
IGNORED_TRIALS = 0
BENCH_NUM_TRIALS = 1
BENCHMARK_PERF = [ #"""Spark-perf benchmark to execute"""
# "scala-agg-by-key",
# "scala-agg-by-key-int",
# "scala-agg-by-key-naive",
# "scala-sort-by-key"#,
# "scala-sort-by-key-int",
# "scala-count",
# "scala-count-w-fltr",
]
BENCHMARK_BENCH = [ #"""Spark-bench benchmark to execute"""
"PageRank"#,
# "DecisionTree",
# "KMeans",
# "SVM"
]
BENCH_CONF = { #"""Setting of the supported benchmark"""
"scala-agg-by-key": {
"ScaleFactor": 5,
"num-partitions": 1000,
"unique-keys": 1000,
"reduce-tasks": 1000
},
"scala-agg-by-key-int": {
"ScaleFactor": 5,
"num-partitions": 1000,
"unique-keys": 1000,
"reduce-tasks": 1000
},
"scala-agg-by-key-naive": {
"ScaleFactor": 5,
"num-partitions": 1000,
"unique-keys": 1000,
"reduce-tasks": 1000
},
"scala-sort-by-key": {
"ScaleFactor": 13,
# "skew": 0,
"num-partitions": 1000,
"unique-keys": 1000,
"reduce-tasks": 1000
},
"scala-sort-by-key-int": {
"ScaleFactor": 25,
"num-partitions": 1000,
"unique-keys": 1000,
"reduce-tasks": 1000
},
"scala-count": {
"ScaleFactor": 25,
"num-partitions": 1000,
"unique-keys": 1000,
"reduce-tasks": 1000
},
"scala-count-w-fltr": {
"ScaleFactor": 25,
"num-partitions": 1000,
"unique-keys": 1000,
"reduce-tasks": 1000
},
"PageRank": {
"NUM_OF_PARTITIONS": 1000,
"numV": 3000000,
"mu": 3.0,
"sigma": 0.0,
"MAX_ITERATION": 1,
"NumTrials": 1
},
"KMeans": {
# DataGen
"NUM_OF_POINTS": 100000000,
"NUM_OF_CLUSTERS": 10,
"DIMENSIONS": 10,
"SCALING": 0.6,
"NUM_OF_PARTITIONS": 1000,
# Run
"MAX_ITERATION": 1
},
"DecisionTree": {
"NUM_OF_PARTITIONS": 1000,
"NUM_OF_EXAMPLES": 50000000,
"NUM_OF_FEATURES": 6,
"NUM_OF_CLASS_C": 10,
"MAX_ITERATION": 1
},
"SVM": {
"NUM_OF_PARTITIONS": 1000,
"NUM_OF_EXAMPLES": 100000000,
"NUM_OF_FEATURES": 5,
"MAX_ITERATION": 1
}
}
TERMINATE = False # Terminate instance after benchmark
HDFS = 0 # TODO Fix this variable for plot
HADOOP_CONF = "/usr/local/lib/hadoop-2.7.2/etc/hadoop/"
HADOOP_HOME = "/usr/local/lib/hadoop-2.7.2"
DELETE_HDFS = 0
# HEURISTICS
HEURISTIC = Heuristic.CONTROL_UNLIMITED
# KMEANS
# STAGE_ALLOCATION = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
# CORE_ALLOCATION = [14.6552, 1.3668, 4.2932, 3.2259, 5.9839, 3.1770, 5.2449, 2.5064, 6.5889, 2.6935, 5.9204, 2.8042,
# 9.6728, 1.7509, 3.8915, 0.7313, 12.2620, 3.1288]
# DEADLINE_ALLOCATION = [18584, 1733, 5444, 4090, 7588, 4028, 6651, 3178, 8355, 3415, 7507, 3556, 12266, 2220, 4934, 927,
# 15549, 3967]
# SVM
# STAGE_ALLOCATION = [0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 18, 23]
# CORE_ALLOCATION = [0.3523, 7.9260, 0.6877, 1.3210, 0.5275, 4.1795, 7.8807, 6.6329, 2.2927, 2.9942, 3.2869, 3.2016,
# 0.8377]
# DEADLINE_ALLOCATION = [953, 21451, 1861, 3575, 1427, 11312, 21329, 17952, 6205, 8103, 8896, 8665, 2267]
# AGG BY KEY
# STAGE_ALLOCATION = [0, 1]
# CORE_ALLOCATION = [6.3715 , 2.2592]
# DEADLINE_ALLOCATION = [84158, 29841]
STAGE_ALLOCATION = None
CORE_ALLOCATION = None
DEADLINE_ALLOCATION = None
CONFIG_DICT = {
"Provider": PROVIDER,
"Benchmark": {
"Name": BENCHMARK_PERF[0] if len(BENCHMARK_PERF) > 0 else BENCHMARK_BENCH[0],
"Config": BENCH_CONF[BENCHMARK_PERF[0] if len(BENCHMARK_PERF) > 0 else BENCHMARK_BENCH[0]]
},
"Deadline": DEADLINE,
"Control": {
"Alpha": ALPHA,
"Beta": BETA,
"OverScale": OVER_SCALE,
"MaxExecutor": MAX_EXECUTOR,
"CoreVM": CORE_VM,
"K": K,
"Ti": TI,
"TSample": T_SAMPLE,
"CoreQuantum": CORE_QUANTUM,
"Heuristic": HEURISTIC.name,
"CoreAllocation": CORE_ALLOCATION,
"DeadlineAllocation": DEADLINE_ALLOCATION,
"StageAllocation": STAGE_ALLOCATION
},
"Aws": {
"InstanceType": INSTANCE_TYPE,
"HyperThreading": not DISABLE_HT,
"Price": PRICE,
"AMI": "", # DATA_AMI[REGION]["ami"],
"Region": "", # REGION,
"AZ": "", # DATA_AMI[REGION]["az"],
"SecurityGroup": SECURITY_GROUP,
"KeyPair": "", #DATA_AMI[REGION]["keypair"],
"EbsOptimized": EBS_OPTIMIZED,
"SnapshotId": "", #DATA_AMI[REGION]["snapid"]
},
"Azure": {
"NodeSize": AZ_SIZE,
"NodeImage": AZ_VHD_IMAGE,
"Location": AZ_LOCATION,
"PubKeyPath": AZ_PUB_KEY_PATH,
"ClusterId": CLUSTER_ID,
"ResourceGroup": AZ_RESOURCE_GROUP,
"StorageAccount": {"Name": AZ_STORAGE_ACCOUNT,
"Sku": AZ_SA_SKU,
"Kind": AZ_SA_KIND},
"Network": AZ_NETWORK,
"Subnet": AZ_SUBNET,
"SecurityGroup": AZ_SECURITY_GROUP
},
"Spark": {
"ExecutorCore": CORE_VM,
"ExecutorMemory": RAM_EXEC,
"ExternalShuffle": ENABLE_EXTERNAL_SHUFFLE,
"LocalityWait": LOCALITY_WAIT,
"LocalityWaitProcess": LOCALITY_WAIT_PROCESS,
"LocalityWaitNode": LOCALITY_WAIT_NODE,
"LocalityWaitRack": LOCALITY_WAIT_RACK,
"CPUTask": CPU_TASK,
"SparkHome": SPARK_HOME
},
"HDFS": bool(HDFS),
}
# Line needed for enabling/disabling benchmark in spark-perf config.py
BENCH_LINES = {"scala-agg-by-key": ["225", "226"],
"scala-agg-by-key-int": ["229", "230"],
"scala-agg-by-key-naive": ["232", "233"],
"scala-sort-by-key": ["236", "237"],
"scala-sort-by-key-int": ["239", "240"],
"scala-count": ["242", "243"],
"scala-count-w-fltr": ["245", "246"]}
PRIVATE_KEY_PATH = None
PRIVATE_KEY_NAME = None
TEMPORARY_STORAGE = None
SPARK_PERF_FOLDER = "spark-perf"
CLUSTER_MAP = {
'hdfs': 'CSPARKHDFS',
'spark': 'CSPARKWORK',
'generic': 'ZOT'
}
VAR_PAR_MAP = {
'pagerank': {
'var_name': 'num_v',
'default': 10000000,
'profile_name': 'Spark_PageRank_Application',
'stage_allocation': None,
'core_allocation': None,
'deadline_allocation': None
},
'kmeans': {
'var_name': 'num_of_points',
'default': 10000000,
'profile_name': 'Spark_KMeans_Example',
'stage_allocation': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
'core_allocation': [14.6552, 1.3668, 4.2932, 3.2259, 5.9839, 3.1770, 5.2449, 2.5064, 6.5889, 2.6935, 5.9204, 2.8042,
9.6728, 1.7509, 3.8915, 0.7313, 12.2620, 3.1288],
'deadline_allocation': [18584, 1733, 5444, 4090, 7588, 4028, 6651, 3178, 8355, 3415, 7507, 3556, 12266, 2220, 4934, 927,
15549, 3967]
},
"decisiontree": {
'profile_name': 'DecisionTree_classification_Example',
'stage_allocation': None,
'core_allocation': None,
'deadline_allocation': None
},
'sort_by_key': {
'var_name': 'scale_factor',
'default': 15,
'profile_name': 'TestRunner__sort-by-key',
'stage_allocation': None,
'core_allocation': None,
'deadline_allocation': None
},
'SVM': {
'var_name': 'scale_factor',
'default': 10000000,
'profile_name': 'SVM_Classifier_Example',
'stage_allocation': [0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 18, 23],
'core_allocation': [0.3523, 7.9260, 0.6877, 1.3210, 0.5275, 4.1795, 7.8807, 6.6329, 2.2927, 2.9942, 3.2869, 3.2016,
0.8377],
'deadline_allocation': [953, 21451, 1861, 3575, 1427, 11312, 21329, 17952, 6205, 8103, 8896, 8665, 2267]
},
'svm': {
'var_name': 'scale_factor',
'default': 10000000,
'profile_name': 'SVM_Classifier_Example',
'stage_allocation': [0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 18, 23],
'core_allocation': [0.3523, 7.9260, 0.6877, 1.3210, 0.5275, 4.1795, 7.8807, 6.6329, 2.2927, 2.9942, 3.2869, 3.2016,
0.8377],
'deadline_allocation': [953, 21451, 1861, 3575, 1427, 11312, 21329, 17952, 6205, 8103, 8896, 8665, 2267]
},
"scala-agg-by-key": {
'profile_name': 'TestRunner__aggregate-by-key',
'stage_allocation': [0, 1],
'core_allocation': [6.3715 , 2.2592],
'deadline_allocation': [84158, 29841]
},
"scala-agg-by-key-int": {
'profile_name': 'TestRunner__aggregate-by-key-int',
'stage_allocation': None,
'core_allocation': None,
'deadline_allocation': None
},
"scala-agg-by-key-naive": {
'profile_name': 'TestRunner__aggregate-by-key-naive',
'stage_allocation': None,
'core_allocation': None,
'deadline_allocation': None
},
"scala-sort-by-key": {
'var_name': 'scale_factor',
'default': 15,
'profile_name': 'TestRunner__sort-by-key',
'stage_allocation': None,
'core_allocation': None,
'deadline_allocation': None
},
"scala-sort-by-key-int": {
'profile_name': 'TestRunner__sort-by-key-int',
'stage_allocation': None,
'core_allocation': None,
'deadline_allocation': None
},
"scala-count": {
'profile_name': 'TestRunner__count',
'stage_allocation': None,
'core_allocation': None,
'deadline_allocation': None
},
"scala-count-w-fltr": {
'profile_name': 'TestRunner__count-with-filter',
'stage_allocation': None,
'core_allocation': None,
'deadline_allocation': None
},
"PageRank": {
'var_name': 'num_v',
'default': 10000000,
'profile_name': 'Spark_PageRank_Application',
'stage_allocation': None,
'core_allocation': None,
'deadline_allocation': None
},
"KMeans": {
'var_name': 'num_of_points',
'default': 10000000,
'profile_name': 'Spark_KMeans_Example',
'stage_allocation': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
'core_allocation': [14.6552, 1.3668, 4.2932, 3.2259, 5.9839, 3.1770, 5.2449, 2.5064, 6.5889, 2.6935, 5.9204, 2.8042,
9.6728, 1.7509, 3.8915, 0.7313, 12.2620, 3.1288],
'deadline_allocation': [18584, 1733, 5444, 4090, 7588, 4028, 6651, 3178, 8355, 3415, 7507, 3556, 12266, 2220, 4934, 927,
15549, 3967]
},
"DecisionTree": {
'profile_name': '',
'stage_allocation': None,
'core_allocation': None,
'deadline_allocation': None
}
}
SCALE_FACTOR = ""
NUM_TASK = ""
INPUT_RECORD = ""
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
CLUSTERS_CFG_FILENAME = 'cfg_clusters.ini'
CLUSTERS_CFG_PATH = os.path.join(ROOT_DIR, CLUSTERS_CFG_FILENAME)
cfg_dict = { "AzKeyName": AZ_KEY_NAME,
"AzPubKeyPath": AZ_PUB_KEY_PATH,
"AzPrvKeyPath": AZ_PRV_KEY_PATH,
"AwsAccessId": AWS_ACCESS_ID,
"AwsSecretKey": AWS_SECRET_KEY,
"AzApplicationId": AZ_APPLICATION_ID,
"AzSecret": AZ_SECRET,
"AzSubscriptionId": AZ_SUBSCRIPTION_ID,
"AzTenantId": AZ_TENANT_ID,
"KeyPairPath": KEY_PAIR_PATH,
"AzLocation": AZ_LOCATION,
"AzSize": AZ_SIZE,
"AzImage": AZ_IMAGE,
"AzVhdImage": AZ_VHD_IMAGE,
"AzResourceGroup": AZ_RESOURCE_GROUP,
"AzStorageAccount": AZ_STORAGE_ACCOUNT,
"AzSaSku": AZ_SA_SKU,
"AzSaKind": AZ_SA_KIND,
"AzNetwork": AZ_NETWORK,
"AzSubnet": AZ_SUBNET,
"AzSecurityGroup": AZ_SECURITY_GROUP,
"DataAmi": DATA_AMI,
"CredentialProfile": CREDENTIAL_PROFILE,
"Provider": PROVIDER,
"Region": REGION,
"SecurityGroup": SECURITY_GROUP,
"Price": PRICE,
"InstanceType": INSTANCE_TYPE,
"NumInstance": NUM_INSTANCE,
"EbsOptimized": EBS_OPTIMIZED,
"Reboot": REBOOT,
"KillJava": KILL_JAVA,
"NumRun": NUM_RUN,
"ProcessOnServer": PROCESS_ON_SERVER,
"InstallPython3": INSTALL_PYTHON3,
"ClusterId": CLUSTER_ID,
"Tag": TAG,
"HdfsMaster": HDFS_MASTER,
"SparkSeqHome": SPARK_SEQ_HOME,
"Spark2Home": SPARK_2_HOME,
"CSparkHome": C_SPARK_HOME,
"SparkHome": SPARK_HOME,
"LogLevel": LOG_LEVEL,
"UpdateSpark": UPDATE_SPARK,
"UpdateSparkMaster": UPDATE_SPARK_MASTER,
"UpdateSparkDocker": UPDATE_SPARK_DOCKER,
"UpdateSparkBench": UPDATE_SPARK_BENCH,
"UpdateSparkPerf": UPDATE_SPARK_PERF,
"SkewTest": SKEW_TEST,
"EnableExternalShuffle": ENABLE_EXTERNAL_SHUFFLE,
"LocalityWait": LOCALITY_WAIT,
"LocalityWaitNode": LOCALITY_WAIT_NODE,
"LocalityWaitProcess": LOCALITY_WAIT_PROCESS,
"LocalityWaitRack": LOCALITY_WAIT_RACK,
"CpuTask": CPU_TASK,
"RamDriver": RAM_DRIVER,
"RamExec": RAM_EXEC,
"OffHeap": OFF_HEAP,
"OffHeapbytes": OFF_HEAP_BYTES,
"CoreVM": CORE_VM,
"CoreHTVM": CORE_HT_VM,
"DisableHT": DISABLE_HT,
"Alpha": ALPHA,
"Beta": BETA,
"Deadline": DEADLINE,
"MaxExecutor": MAX_EXECUTOR,
"OverScale": OVER_SCALE,
"K": K,
"Ti": TI,
"TSample": T_SAMPLE,
"CoreQuantum": CORE_QUANTUM,
"CoreMin": CORE_MIN,
"CpuPeriod": CPU_PERIOD,
"Run": RUN,
"SyncTime": SYNC_TIME,
"PrevScaleFactor": PREV_SCALE_FACTOR,
"BenchNumTrials": BENCH_NUM_TRIALS,
"BenchmarkPerf": BENCHMARK_PERF,
"BenchmarkBench": BENCHMARK_BENCH,
"BenchConf": BENCH_CONF,
"Terminate": TERMINATE,
"Hdfs": HDFS,
"HadoopConf": HADOOP_CONF,
"HadoopHome": HADOOP_HOME,
"DeleteHdfs": DELETE_HDFS,
"Heuristic": HEURISTIC.name,
"StageAllocation": STAGE_ALLOCATION,
"CoreAllocation": CORE_ALLOCATION,
"DeadlineAllocation": DEADLINE_ALLOCATION,
"ConfigDict": CONFIG_DICT,
"BenchLines": BENCH_LINES,
"PrivateKeyPath": PRIVATE_KEY_PATH,
"PrivateKeyName": PRIVATE_KEY_NAME,
"TemporaryStorage": TEMPORARY_STORAGE,
"SparkPerfFolder": SPARK_PERF_FOLDER,
"ClusterMap": CLUSTER_MAP,
"VarParMap": VAR_PAR_MAP,
"ScaleFactor": SCALE_FACTOR,
"NumTask": NUM_TASK,
"InputRecord": INPUT_RECORD,
"RootDir": ROOT_DIR,
"ClustersCfgFilename": CLUSTERS_CFG_FILENAME,
"ClustersCfgPath": CLUSTERS_CFG_PATH,
"GitBranch": GIT_BRANCH
}
def __init__(self):
self.config_credentials("credentials.json")
self.config_setup("setup.json")
self.config_control("control.json")
self.update_config_parms(self)
print("Config instance initialized.")
def config_experiment(self, filepath, cfg):
exp_file = Path(filepath)
if exp_file.exists():
experiment = json.load(open(filepath))
if len(experiment) > 0:
cfg["experiment"] = {}
keys = experiment.keys()
benchmark = ""
is_benchmark_bench = False
is_benchmark_perf = False
if "ReuseDataset" in keys:
self.cfg_dict["DeleteHdfs"] = self.DELETE_HDFS = not experiment["ReuseDataset"]
self.cfg_dict["PrevScaleFactor"] = self.PREV_SCALE_FACTOR = self.NUM_TASK if experiment["ReuseDataset"] else 0
cfg["experiment"]["reusedataset"] = str(experiment["ReuseDataset"])
if 'delete_hdfs' in cfg['main']:
cfg['main']['delete_hdfs'] = str(not cfg.getboolean('experiment', 'reusedataset'))
if "Deadline" in keys:
self.cfg_dict["Deadline"] = self.DEADLINE = experiment["Deadline"]
cfg["experiment"]["deadline"] = str(experiment["Deadline"])
if "BenchmarkConf" in keys and "BenchmarkName" in keys:
cfg["BenchmarkConf"] = {}
benchmark = experiment["BenchmarkName"]
cfg["experiment"]["benchmarkname"] = experiment["BenchmarkName"]
is_benchmark_bench = benchmark in ["PageRank", "KMeans", "DecisionTree", "SVM"]
is_benchmark_perf = benchmark in ["scala-agg-by-key", "scala-agg-by-key-int",
"scala-agg-by-key-naive", "scala-sort-by-key",
"scala-sort-by-key-int", "scala-count",
"scala-count-w-fltr"]
for k_bench_conf in experiment["BenchmarkConf"].keys():
mapped_parm = self.exp_par_map(k_bench_conf)
if is_benchmark_bench and k_bench_conf != "NumTrials":
self.cfg_dict["BenchConf"][benchmark][mapped_parm] = self.BENCH_CONF[benchmark][mapped_parm] = experiment["BenchmarkConf"][k_bench_conf]
elif is_benchmark_perf or (is_benchmark_bench and k_bench_conf == "NumTrials"):
self.cfg_dict["BenchConf"][benchmark][mapped_parm] = self.BENCH_CONF[benchmark][mapped_parm] = experiment["BenchmarkConf"][k_bench_conf]
keys = self.BENCH_CONF[benchmark].keys()
for key in keys:
inv_mapped_parm = self.exp_inverse_par_map(key)
cfg['BenchmarkConf'][inv_mapped_parm] = str(self.BENCH_CONF[benchmark][key]) \
if key != "NumTrials" else str(self.BENCH_CONF[benchmark][key])
self.cfg_dict["BenchmarkBench"] = self.BENCHMARK_BENCH = [benchmark] if is_benchmark_bench else []
self.cfg_dict["BenchmarkPerf"] = self.BENCHMARK_PERF = [benchmark] if is_benchmark_perf else []
#if "SyncTime" in keys:
# SYNC_TIME = experiment["SyncTime"]
#if "PrevScaleFactor" in keys:
# PREV_SCALE_FACTOR = experiment["PrevScaleFactor"]
#if "BenchNumTrials" in keys:
# BENCH_NUM_TRIALS = experiment["BenchNumTrials"]
#if "BenchmarkPerf" in keys:
# BENCHMARK_PERF = experiment["BenchmarkPerf"]
#if "BenchmarkBench" in keys:
# BENCHMARK_BENCH = experiment["BenchmarkBench"]
#if "BenchmarkConf" in keys:
# BENCH_CONF = experiment["BenchmarkConf"] else BENCH_CONF
# k_BenchmarkConf = experiment["BenchmarkConf"].keys()
# print("config_experiment: " + str(BENCHMARK_BENCH + BENCHMARK_PERF))
# for bench in BENCHMARK_BENCH + BENCHMARK_PERF:
# if bench in k_BenchmarkConf:
# BENCH_CONF[bench] = experiment["BenchmarkConf"][bench]
self.update_config_parms(self)
else:
print("Experiment file: "+ filepath + " is empty: using defaults")
else:
print("Experiment file: "+ filepath + " not found: exiting program")
exit(-1)
def config_benchmark(self, benchmark, cfg):
#benchmark = cfg['main']['benchmark'] if 'main' in cfg and 'benchmark' in cfg['main'] else ''
is_benchmark_bench = self.ini_par_map(benchmark) in ["PageRank", "KMeans", "DecisionTree", "SVM"]
is_benchmark_perf = self.ini_par_map(benchmark) in ["scala-agg-by-key", "scala-agg-by-key-int",
"scala-agg-by-key-naive", "scala-sort-by-key",
"scala-sort-by-key-int", "scala-count",
"scala-count-w-fltr"]
if benchmark == 'pagerank':
numV = cfg['pagerank']['num_v']
self.cfg_dict["BenchConf"]["PageRank"]["numV"] = self.BENCH_CONF["PageRank"]["numV"] = literal_eval(numV)
num_partitions = cfg['pagerank']['num_partitions']
self.cfg_dict["BenchConf"]["PageRank"]["NUM_OF_PARTITIONS"] = self.BENCH_CONF["PageRank"]["NUM_OF_PARTITIONS"] = literal_eval(num_partitions)
print('setting numV as {}'.format(self.BENCH_CONF["PageRank"]["numV"]))
print('setting NUM_OF_PARTITIONS as {}'.format(self.BENCH_CONF["PageRank"]["NUM_OF_PARTITIONS"]))
elif benchmark == 'kmeans':
num_of_points = cfg['kmeans']['num_of_points']
self.cfg_dict["BenchConf"]["KMeans"]["NUM_OF_POINTS"] = self.BENCH_CONF["KMeans"]["NUM_OF_POINTS"] = literal_eval(num_of_points)
num_partitions = cfg['kmeans']['num_partitions']
self.cfg_dict["BenchConf"]["KMeans"]["NUM_OF_PARTITIONS"] = self.BENCH_CONF["KMeans"]["NUM_OF_PARTITIONS"] = literal_eval(num_partitions)
print('setting NUM_OF_POINTS as {}'.format(self.BENCH_CONF["KMeans"]["NUM_OF_POINTS"]))
print('setting NUM_OF_PARTITIONS as {}'.format(self.BENCH_CONF["KMeans"]["NUM_OF_PARTITIONS"]))
elif benchmark == 'decisiontree':
num_of_examples = cfg['decisiontree']['num_of_examples']
self.cfg_dict["BenchConf"]["DecisionTree"]["NUM_OF_EXAMPLES"] = self.BENCH_CONF["DecisionTree"]["NUM_OF_EXAMPLES"] = literal_eval(num_of_examples)
num_partitions = cfg['decisiontree']['num_partitions']
self.cfg_dict["BenchConf"]["DecisionTree"]["NUM_OF_PARTITIONS"] = self.BENCH_CONF["DecisionTree"]["NUM_OF_PARTITIONS"] = literal_eval(num_partitions)
print('setting NUM_OF_EXAMPLES as {}'.format(self.BENCH_CONF["DecisionTree"]["NUM_OF_EXAMPLES"]))
print('setting NUM_OF_PARTITIONS as {}'.format(self.BENCH_CONF["DecisionTree"]["NUM_OF_PARTITIONS"]))
elif benchmark == 'svm':
scale_factor = cfg['svm']['scale_factor']
self.cfg_dict["BenchConf"]["SVM"]["ScaleFactor"] = self.BENCH_CONF["SVM"]["ScaleFactor"] = literal_eval(scale_factor)
num_partitions = cfg['svm']['num_partitions']
self.cfg_dict["BenchConf"]["SVM"]["NUM_OF_PARTITIONS"] = self.BENCH_CONF["SVM"]["NUM_OF_PARTITIONS"] = literal_eval(num_partitions)
print('setting NUM_OF_EXAMPLES as {}'.format(self.BENCH_CONF["SVM"]["NUM_OF_EXAMPLES"]))
print('setting NUM_OF_PARTITIONS as {}'.format(self.BENCH_CONF["SVM"]["NUM_OF_PARTITIONS"]))
elif benchmark == 'sort_by_key':
scale_factor = cfg['sort_by_key']['scale_factor']
self.cfg_dict["BenchConf"]["scala-sort-by-key"]["ScaleFactor"] = self.BENCH_CONF["scala-sort-by-key"]["ScaleFactor"] = literal_eval(scale_factor)
num_partitions = cfg['sort_by_key']['num_partitions']
self.cfg_dict["BenchConf"]["scala-sort-by-key"]["NUM_OF_PARTITIONS"] = self.BENCH_CONF["scala-sort-by-key"]["NUM_OF_PARTITIONS"] = literal_eval(num_partitions)
print('setting ScaleFactor as {}'.format(self.BENCH_CONF["scala-sort-by-key"]["ScaleFactor"]))
print('setting NUM_OF_PARTITIONS as {}'.format(self.BENCH_CONF["scala-sort-by-key"]["NUM_OF_PARTITIONS"]))
elif benchmark == 'sort_by_key_int':
scale_factor = cfg['sort_by_key_int']['scale_factor']
self.cfg_dict["BenchConf"]["scala-sort-by-key-int"]["ScaleFactor"] = self.BENCH_CONF["scala-sort-by-key-int"]["ScaleFactor"] = literal_eval(scale_factor)
num_partitions = cfg['sort_by_key_int']['num_partitions']
self.cfg_dict["BenchConf"]["scala-sort-by-key-int"]["NUM_OF_PARTITIONS"] = self.BENCH_CONF["scala-sort-by-key-int"]["NUM_OF_PARTITIONS"] = literal_eval(num_partitions)
print('setting ScaleFactor as {}'.format(self.BENCH_CONF["scala-sort-by-key-int"]["ScaleFactor"]))
print('setting NUM_OF_PARTITIONS as {}'.format(self.BENCH_CONF["scala-sort-by-key-int"]["NUM_OF_PARTITIONS"]))
elif benchmark == 'agg_by_key':
scale_factor = cfg['agg_by_key']['scale_factor']
self.cfg_dict["BenchConf"]["scala-agg-by-key"]["ScaleFactor"] = self.BENCH_CONF["scala-agg-by-key"]["ScaleFactor"] = literal_eval(scale_factor)
num_partitions = cfg['agg_by_key']['num_partitions']
self.cfg_dict["BenchConf"]["scala-agg-by-key"]["NUM_OF_PARTITIONS"] = self.BENCH_CONF["scala-agg-by-key"]["NUM_OF_PARTITIONS"] = literal_eval(num_partitions)
print('setting ScaleFactor as {}'.format(self.BENCH_CONF["scala-agg-by-key"]["ScaleFactor"]))
print('setting NUM_OF_PARTITIONS as {}'.format(self.BENCH_CONF["scala-agg-by-key"]["NUM_OF_PARTITIONS"]))
elif benchmark == 'agg_by_key_int':
scale_factor = cfg['agg_by_key_int']['scale_factor']
self.cfg_dict["BenchConf"]["scala-agg-by-key-int"]["ScaleFactor"] = self.BENCH_CONF["scala-agg-by-key-int"]["ScaleFactor"] = literal_eval(scale_factor)
num_partitions = cfg['agg_by_key_int']['num_partitions']
self.cfg_dict["BenchConf"]["scala-agg-by-key-int"]["NUM_OF_PARTITIONS"] = self.BENCH_CONF["scala-agg-by-key-int"]["NUM_OF_PARTITIONS"] = literal_eval(num_partitions)
print('setting ScaleFactor as {}'.format(self.BENCH_CONF["scala-agg-by-key-int"]["ScaleFactor"]))
print('setting NUM_OF_PARTITIONS as {}'.format(self.BENCH_CONF["scala-agg-by-key-int"]["NUM_OF_PARTITIONS"]))
elif benchmark == 'agg_by_key_naive':
scale_factor = cfg['agg_by_key_naive']['scale_factor']
self.cfg_dict["BenchConf"]["scala-agg-by-key-naive"]["ScaleFactor"] = self.BENCH_CONF["scala-agg-by-key-naive"]["ScaleFactor"] = literal_eval(scale_factor)
num_partitions = cfg['agg_by_key_naive']['num_partitions']
self.cfg_dict["BenchConf"]["scala-agg-by-key-naive"]["NUM_OF_PARTITIONS"] = self.BENCH_CONF["scala-agg-by-key-naive"]["NUM_OF_PARTITIONS"] = literal_eval(num_partitions)
prnaive('setting ScaleFactor as {}'.format(self.BENCH_CONF["scala-agg-by-key-naive"]["ScaleFactor"]))
prnaive('setting NUM_OF_PARTITIONS as {}'.format(self.BENCH_CONF["scala-agg-by-key-naive"]["NUM_OF_PARTITIONS"]))
elif benchmark == 'count':
scale_factor = cfg['count']['scale_factor']
self.cfg_dict["BenchConf"]["scala-count"]["ScaleFactor"] = self.BENCH_CONF["scala-count"]["ScaleFactor"] = literal_eval(scale_factor)
num_partitions = cfg['count']['num_partitions']
self.cfg_dict["BenchConf"]["scala-count"]["NUM_OF_PARTITIONS"] = self.BENCH_CONF["scala-count"]["NUM_OF_PARTITIONS"] = literal_eval(num_partitions)
print('setting ScaleFactor as {}'.format(self.BENCH_CONF["scala-count"]["ScaleFactor"]))
print('setting NUM_OF_PARTITIONS as {}'.format(self.BENCH_CONF["scala-count"]["NUM_OF_PARTITIONS"]))
elif benchmark == 'count_w_fltr':
scale_factor = cfg['count_w_fltr']['scale_factor']
selscale_factorf.cfg_dict["BenchConf"]["scala-count-w-fltr"]["ScaleFactor"] = self.BENCH_CONF["scala-count-w-fltr"]["ScaleFactor"] = literal_eval(scale_factor)
num_partitions = cfg['count_w_fltr']['num_partitions']
self.cfg_dict["BenchConf"]["scala-count-w-fltr"]["NUM_OF_PARTITIONS"] = self.BENCH_CONF["scala-count-w-fltr"]["NUM_OF_PARTITIONS"] = literal_eval(num_partitions)
print('setting ScaleFactor as {}'.format(self.BENCH_CONF["scala-count-w-fltr"]["ScaleFactor"]))
print('setting NUM_OF_PARTITIONS as {}'.format(self.BENCH_CONF["scala-count-w-fltr"]["NUM_OF_PARTITIONS"]))
else:
print("ERROR: unknown benchmark '{}'".format(benchmark))
exit(1)
self.cfg_dict["BenchmarkBench"] = self.BENCHMARK_BENCH = [self.ini_par_map(benchmark)] if is_benchmark_bench else []
self.cfg_dict["BenchmarkPerf"] = self.BENCHMARK_PERF = [self.ini_par_map(benchmark)] if is_benchmark_perf else []
self.update_config_parms(self)
def update_exp_parms(self, cfg):
benchmark = ""
if "experiment" in cfg:
if "reusedataset" in cfg["experiment"]:
self.cfg_dict["DeleteHdfs"] = self.DELETE_HDFS = not cfg.getboolean('experiment', 'reusedataset')
self.cfg_dict["PrevScaleFactor"] = self.PREV_SCALE_FACTOR = self.NUM_TASK if cfg.getboolean('experiment', 'reusedataset') else 0
if "deadline" in cfg["experiment"]:
self.cfg_dict["Deadline"] = self.DEADLINE = cfg["experiment"]["deadline"]
if "BenchmarkConf" in cfg:
if "benchmarkname" in cfg:
benchmark = cfg["benchmarkname"]
is_benchmark_bench = benchmark in ["PageRank", "KMeans", "DecisionTree", "SVM"]
is_benchmark_perf = benchmark in ["scala-agg-by-key", "scala-agg-by-key-int",
"scala-agg-by-key-naive", "scala-sort-by-key",
"scala-sort-by-key-int", "scala-count",
"scala-count-w-fltr"]
for p_bench_conf in cfg["BenchmarkConf"]:
mapped_parm = self.ini_par_map(p_bench_conf)
if is_benchmark_bench and p_bench_conf != "NumTrials":
self.cfg_dict["BenchConf"][benchmark][mapped_parm] = self.BENCH_CONF[benchmark][mapped_parm] = experiment["BenchmarkConf"][p_bench_conf]
elif is_benchmark_perf or is_benchmark_bench and p_bench_conf == "NumTrials":
self.cfg_dict["BenchConf"][benchmark][mapped_parm] = self.BENCH_CONF[benchmark][mapped_parm] = experiment["BenchmarkConf"][p_bench_conf]
self.cfg_dict["BenchmarkBench"] = self.BENCHMARK_BENCH = [benchmark] if is_benchmark_bench else []
self.cfg_dict["BenchmarkPerf"] = self.BENCHMARK_PERF = [benchmark] if is_benchmark_perf else []
self.update_config_parms(self)
def update_ext_parms(self, cfg_dict):
self.NUM_RUN = self.cfg_dict["NumRun"] = cfg_dict["NumRun"]
self.HDFS_MASTER = self.cfg_dict["HdfsMaster"] = cfg_dict["HdfsMaster"]
self.DEADLINE = self.cfg_dict["Deadline"] = cfg_dict["Deadline"]
self.MAX_EXECUTOR = self.cfg_dict["MaxExecutor"] = cfg_dict["MaxExecutor"]
self.RUN = self.cfg_dict["Run"] = cfg_dict["Run"]
self.PREV_SCALE_FACTOR = self.cfg_dict["PrevScaleFactor"] = cfg_dict["PrevScaleFactor"]
self.BENCHMARK_PERF = self.cfg_dict["BenchmarkPerf"] = cfg_dict["BenchmarkPerf"]
self.BENCHMARK_BENCH = self.cfg_dict["BenchmarkBench"] = cfg_dict["BenchmarkBench"]
self.BENCH_CONF = self.cfg_dict["BenchConf"] = cfg_dict["BenchConf"]
self.HDFS = self.cfg_dict["Hdfs"] = cfg_dict["Hdfs"]
self.DELETE_HDFS = self.cfg_dict["DeleteHdfs"] = cfg_dict["DeleteHdfs"]
#self.CONFIG_DICT = self.cfg_dict["ConfigDict"] = cfg_dict["ConfigDict"]
self.SCALE_FACTOR = self.cfg_dict["ScaleFactor"] = cfg_dict["ScaleFactor"]
self.INPUT_RECORD = self.cfg_dict["InputRecord"] = cfg_dict["InputRecord"]
self.SPARK_HOME = self.cfg_dict["SparkHome"] = cfg_dict["SparkHome"]
self.update_config_parms(self)
def config_credentials(self, filepath):
credentials_file = Path(filepath)
if credentials_file.exists():
credentials = json.load(open("credentials.json"))
if len(credentials) > 0:
keys = credentials.keys()
if "AzTenantId" in keys:
self.cfg_dict["AzTenantId"] = self.AZ_TENANT_ID = credentials["AzTenantId"]
if "AzSubscriptionId" in keys:
self.cfg_dict["AzSubscriptionId"] = self.AZ_SUBSCRIPTION_ID = credentials["AzSubscriptionId"]
if "AzApplicationId" in keys:
self.cfg_dict["AzApplicationId"] = self.AZ_APPLICATION_ID = credentials["AzApplicationId"]
if "AzSecret" in keys:
self.cfg_dict["AzSecret"] = self.AZ_SECRET = credentials["AzSecret"]
if "AzPubKeyPath" in keys:
self.cfg_dict["AzPubKeyPath"] = self.AZ_PUB_KEY_PATH = credentials["AzPubKeyPath"]
if "AzPrvKeyPath" in keys:
self.cfg_dict["AzPrvKeyPath"] = self.AZ_PRV_KEY_PATH = credentials["AzPrvKeyPath"]
if "AwsAccessId" in keys:
self.cfg_dict["AwsAccessId"] = self.AWS_ACCESS_ID = credentials["AwsAccessId"]
if "AwsSecretId" in keys:
self.cfg_dict["AwsSecretId"] = self.AWS_SECRET_KEY = credentials["AwsSecretId"]
if "KeyPairPath" in keys:
self.cfg_dict["KeyPairPath"] = self.KEY_PAIR_PATH = credentials["KeyPairPath"]
print("Configuration from " + filepath + " done")
else:
print("credentials file: " + filepath + " is empty: using defaults")
else:
print("credentials file: " + filepath + " found: using defaults")
def config_setup(self, filepath):
setup_file = Path(filepath)
if setup_file.exists():
setup = json.load(open("setup.json"))
if len(setup) > 0:
keys = setup.keys()
if "Provider" in keys:
self.cfg_dict["Provider"] = self.PROVIDER = setup["Provider"]
if "ProcessOnServer" in keys:
self.cfg_dict["ProcessOnServer"] = self.PROCESS_ON_SERVER = setup["ProcessOnServer"]
if "InstallPython3" in keys:
self.cfg_dict["InstallPython3"] = self.INSTALL_PYTHON3 = setup["InstallPython3"]
if "UpdateSpark" in keys:
self.cfg_dict["UpdateSpark"] = self.UPDATE_SPARK = setup["UpdateSpark"]
if "UpdateSparkMaster" in keys:
self.cfg_dict["UpdateSparkMaster"] = self.UPDATE_SPARK_MASTER = setup["UpdateSparkMaster"]
if "UpdateSparkDocker" in keys:
self.cfg_dict["UpdateSparkDocker"] = self.UPDATE_SPARK_DOCKER = setup["UpdateSparkDocker"]
if "UpdateSparkBench" in keys:
self.cfg_dict["UpdateSparkBench"] = self.UPDATE_SPARK_BENCH = setup["UpdateSparkBench"]
if "UpdateSparkPerf" in keys:
self.cfg_dict["UpdateSparkPerf"] = self.UPDATE_SPARK_PERF = setup["UpdateSparkPerf"]
#if "NumInstance" in keys: NUM_INSTANCE = setup["NumInstance"]
#if "Reboot" in keys: REBOOT = setup["Reboot"]
#if "KillJava" in keys: KILL_JAVA = setup["KillJava"]
#if "NumRun" in keys: NUM_RUN = setup["NumRun"]
#if "ClusterId" in keys: CLUSTER_ID = setup["ClusterId"]
#if "Tag" in keys: TAG = setup["Tag"]
#if "HdfsMaster" in keys: HDFS_MASTER = setup["HdfsMaster"]
#if "Terminate" in keys: TERMINATE = setup["Terminate"]
#if "HadoopConf" in keys: HADOOP_CONF = setup["HadoopConf"]
#if "HadoopHome" in keys: HADOOP_HOME = setup["HadoopHome"]
#if "SparkPerfFolder" in keys: SPARK_PERF_FOLDER = setup["SparkPerfFolder"]
#if "ClusterMap" in keys: CLUSTER_MAP = setup["ClusterMap"]
#if "VarParMap" in keys: VAR_PAR_MAP = setup["VarParMap"]
#if "BenchLines" in keys: BENCH_LINES = setup["BenchLines"]
#if "HDFS" in keys: HDFS = setup["HDFS"]
#LOG_LEVEL = setup["Spark"]["LogLevel"] if "LogLevel" in keys else LOG_LEVEL
#RAM_DRIVER = setup["Spark"]["RamDriver"] if "RamDriver" in keys else RAM_DRIVER
#OFF_HEAP = setup["Spark"]["OffHeap"] if "OffHeap" in keys else OFF_HEAP
#OFF_HEAP_BYTES = setup["Spark"]["OffHeapBytes"] if "OffHeapBytes" in keys else OFF_HEAP_BYTES
#DISABLE_HT = not setup["Spark"]["HyperThreading"] if "HyperThreading" in keys else DISABLE_HT
'''
if "Credentials" in keys:
k_Credentials = setup["Credentials"].keys()
if "AzTenantId" in k_Credentials:
self.cfg_dict["AzTenantId"] = self.AZ_TENANT_ID = setup["Credentials"]["AzTenantId"]
if "AzSubscriptionId" in k_Credentials:
self.cfg_dict["AzSubscriptionId"] = self.AZ_SUBSCRIPTION_ID = setup["Credentials"]["AzSubscriptionId"]
if "AzApplicationId" in k_Credentials:
self.cfg_dict["AzApplicationId"] = self.AZ_APPLICATION_ID = setup["Credentials"]["AzApplicationId"]
if "AzSecret" in k_Credentials:
self.cfg_dict["AzSecret"] = self.AZ_SECRET = setup["Credentials"]["AzSecret"]
if "AzPubKeyPath" in k_Credentials:
self.cfg_dict["AzPubKeyPath"] = self.AZ_PUB_KEY_PATH = setup["Credentials"]["AzPubKeyPath"]
if "AzPrvKeyPath" in k_Credentials:
self.cfg_dict["AzPrvKeyPath"] = self.AZ_PRV_KEY_PATH = setup["Credentials"]["AzPrvKeyPath"]
if "AwsAccessId" in k_Credentials:
self.cfg_dict["AwsAccessId"] = self.AWS_ACCESS_ID = setup["Credentials"]["AwsAccessId"]
if "AwsSecretId" in k_Credentials:
self.cfg_dict["AwsSecretId"] = self.AWS_SECRET_KEY = setup["Credentials"]["AwsSecretId"]
if "KeyPairPath" in k_Credentials:
self.cfg_dict["KeyPairPath"] = self.KEY_PAIR_PATH = setup["Credentials"]["KeyPairPath"]
'''
if "VM" in keys:
k_VM = setup["VM"].keys()
if "Core" in k_VM:
self.cfg_dict["CoreVM"] = self.CORE_VM = setup["VM"]["Core"]
if "Memory" in k_VM:
self.cfg_dict["RamExec"] = self.RAM_EXEC = '"' + setup["VM"]["Memory"] + '"'
if "Azure" in keys:
k_Azure = setup["Azure"].keys()
if "ResourceGroup" in k_Azure:
self.cfg_dict["AzResourceGroup"] = self.AZ_RESOURCE_GROUP = setup["Azure"]["ResourceGroup"]
if "SecurityGroup" in k_Azure:
self.cfg_dict["AzSecurityGroup"] = self.AZ_SECURITY_GROUP = setup["Azure"]["SecurityGroup"]
if "StorageAccount" in k_Azure:
k_Azure_StorageAccount = setup["Azure"]["StorageAccount"].keys()
if "Name" in k_Azure_StorageAccount:
self.cfg_dict["AzStorageAccount"] = self.AZ_STORAGE_ACCOUNT = setup["Azure"]["StorageAccount"]["Name"]
if "Sku" in k_Azure_StorageAccount:
self.cfg_dict["AzSaSku"] = self.AZ_SA_SKU = setup["Azure"]["StorageAccount"]["Sku"]
if "Kind" in k_Azure_StorageAccount:
self.cfg_dict["AzSaKind"] = self.AZ_SA_KIND = setup["Azure"]["StorageAccount"]["Kind"]
if "Subnet" in k_Azure:
self.cfg_dict["AzSubnet"] = self.AZ_SUBNET = setup["Azure"]["Subnet"]
if "NodeSize" in k_Azure:
self.cfg_dict["AzSize"] = self.AZ_SIZE = setup["Azure"]["NodeSize"]
if "Network" in k_Azure:
self.cfg_dict["AzNetwork"] = self.AZ_NETWORK = setup["Azure"]["Network"]
if "Location" in k_Azure:
self.cfg_dict["AzLocation"] = self.AZ_LOCATION = setup["Azure"]["Location"]
if "ImageName" in k_Azure:
self.cfg_dict["AzImageName"] = self.AZ_IMAGE = setup["Azure"]["ImageName"]
if "NodeImage" in k_Azure:
self.cfg_dict["AzVhdImage"] = self.AZ_VHD_IMAGE = setup["Azure"]["NodeImage"]
if "Aws" in keys:
k_Aws = setup["Aws"].keys()
if "Region" in k_Aws:
self.cfg_dict["Region"] = self.REGION = setup["Aws"]["Region"]
if "SecurityGroup" in k_Aws:
self.cfg_dict["SecurityGroup"] = self.SECURITY_GROUP = setup["Aws"]["SecurityGroup"]
if "Price" in k_Aws:
self.cfg_dict["Price"] = self.PRICE = setup["Aws"]["Price"]
if "InstanceType" in k_Aws:
self.cfg_dict["InstanceType"] = self.INSTANCE_TYPE = setup["Aws"]["InstanceType"]
if "EbsOptimized" in k_Aws:
self.cfg_dict["EbsOptimized"] = self.EBS_OPTIMIZED = setup["Aws"]["EbsOptimized"]
if "AwsRegions" in k_Aws:
self.cfg_dict["DataAmi"] = self.DATA_AMI = setup["Aws"]["AwsRegions"]
if "Spark" in keys:
k_Spark = setup["Spark"].keys()
if "Home" in k_Spark:
self.cfg_dict["Spark2Home"] = self.SPARK_2_HOME = setup["Spark"]["Home"]
if "ExternalShuffle" in keys:
self.cfg_dict["EnableExternalShuffle"] = self.ENABLE_EXTERNAL_SHUFFLE = setup["Spark"]["ExternalShuffle"]
if "LocalityWait" in keys:
self.cfg_dict["LocalityWait"] = self.LOCALITY_WAIT = setup["Spark"]["LocalityWait"]
if "LocalityWaitNode" in keys:
self.cfg_dict["LocalityWaitNode"] = self.LOCALITY_WAIT_NODE = setup["Spark"]["LocalityWaitNode"]
if "LocalityWaitProcess" in keys:
self.cfg_dict["LocalityWaitProcess"] = self.LOCALITY_WAIT_PROCESS = setup["Spark"]["LocalityWaitProcess"]
if "LocalityWaitRack" in keys:
self.cfg_dict["LocalityWaitRack"] = self.LOCALITY_WAIT_RACK = setup["Spark"]["LocalityWaitRack"]
if "CpuTask" in keys:
self.cfg_dict["CpuTask"] = self.CPU_TASK = setup["Spark"]["CpuTask"]
if "xSpark" in keys:
k_xSpark = setup["xSpark"].keys()
if "Home" in k_xSpark:
self.cfg_dict["CSparkHome"] = self.C_SPARK_HOME = setup["xSpark"]["Home"]
#C_SPARK_HOME = setup["Spark"]["CSparkHome"] if "CSparkHome" in keys else C_SPARK_HOME
#SPARK_SEQ_HOME = setup["Spark"]["SparkSeqHome"] if "SparkSeqHome" in keys else SPARK_SEQ_HOME
#SPARK_2_HOME = setup["Spark"]["Spark2Home"] if "Spark2Home" in keys else SPARK_2_HOME
if "SparkSeq" in keys:
k_SparkSeq = setup["SparkSeq"].keys()
if "Home" in k_SparkSeq:
self.cfg_dict["SparkSeqHome"] = self.SPARK_SEQ_HOME = setup["SparkSeq"]["Home"]
self.cfg_dict["SparkHome"] = self.SPARK_HOME = self.C_SPARK_HOME
#if "ControlledMode" in keys:
# if setup["ControlledMode"]:
# if "xSpark" in keys and "Home"in setup["xSpark"]:
# self.cfg_dict["SparkHome"] = self.SPARK_HOME = setup["xSpark"]["Home"]
# elif "Spark" in keys and "Home"in setup["Spark"]:
# self.cfg_dict["SparkHome"] = self.SPARK_HOME = setup["Spark"]["Home"]
print("Configuration from " + filepath + " done")
else:
print("Setup file: " + filepath + " is empty: using defaults")
else:
print("Setup file: " + filepath + " found: using defaults")
def config_control(self, filepath):
control_file = Path(filepath)
if control_file.exists():
control = json.load(open(filepath))
if len(control) > 0:
keys = control.keys()
if "Alpha" in keys:
self.cfg_dict["Alpha"] = self.ALPHA = control["Alpha"]
if "Beta" in keys:
self.cfg_dict["Beta"] = self.BETA = control["Beta"]
if "OverScale" in keys:
self.cfg_dict["OverScale"] = self.OVER_SCALE = control["OverScale"]
if "K" in keys:
self.cfg_dict["K"] = self.K = control["K"]
if "Ti" in keys:
self.cfg_dict["Ti"] = self.TI = control["Ti"]
if "TSample" in keys:
self.cfg_dict["TSample"] = self.T_SAMPLE = control["TSample"]
if "Heuristic" in keys:
self.HEURISTIC = eval("self.Heuristic." + control["Heuristic"])
self.cfg_dict["Heuristic"] = self.HEURISTIC.name
if "CoreQuantum" in keys:
self.cfg_dict["CoreQuantum"] = self.CORE_QUANTUM = control["CoreQuantum"]
if "CoreMin" in keys:
self.cfg_dict["CoreMin"] = self.CORE_MIN = control["CoreMin"]
if "CpuPeriod" in keys:
self.cfg_dict["CpuPeriod"] = self.CPU_PERIOD = control["CpuPeriod"]
#DEADLINE = control["Deadline"] if "Deadline" in keys else DEADLINE
#MAX_EXECUTOR = control["MaxExecutor"] if "MaxExecutor" in keys else MAX_EXECUTOR
#CORE_VM = control["CoreVM"] if "CoreVM" in keys else CORE_VM