def create_emr(R): if not boto.config.has_section('Boto'): boto.config.add_section('Boto') boto.config.set('Boto', 'https_validate_certificates', 'False') step = StreamingStep(name='MC_Method example', cache_files=['s3n://bucket774/map.py#map.py'], mapper='map.py', input='s3://bucket774/input/', output='s3://bucket774/output/') conn = EmrConnection(access_id, access_key) instance_groups = [] instance_groups.append( InstanceGroup(num_instances=1, role="MASTER", type='m4.large', market="ON_DEMAND", name="Master nodes")) if R > 1: instance_groups.append( InstanceGroup(num_instances=R - 1, role="CORE", type='m4.large', market="ON_DEMAND", name="Slave nodes")) cluster_id = conn.run_jobflow(name='test MC_method run', instance_groups=instance_groups, enable_debugging=False, steps=[step], visible_to_all_users=True, keep_alive=True, job_flow_role="EMR_EC2_DefaultRole", service_role="EMR_DefaultRole", hadoop_version='2.4.0', log_uri='s3://bucket774/log') return cluster_id, conn
def add_steps(cluster_id, key): try: emr_connection = EmrConnection() emr_connection.add_jobflow_steps(cluster_id, get_steps(key, key)) return True except Exception, e: return False
def start_hadoop_cluster(nodenum): try: hadoop_params = ['-m','mapred.tasktracker.map.tasks.maximum=1', '-m', 'mapred.child.java.opts=-Xmx10g'] configure_hadoop_action = BootstrapAction('configure_hadoop', 's3://elasticmapreduce/bootstrap-actions/configure-hadoop', hadoop_params) emr_connection = EmrConnection() bucket_name = "udk-bucket" steps = [] copy_jar_step = JarStep(name='copy-jar', jar='s3n://' + bucket_name + '/copy-to-hdfs.jar', step_args=['s3n://' + bucket_name + '/pipeline.pear', '/mnt/pipeline.pear']) steps.append(copy_jar_step) jobflow_id = emr_connection.run_jobflow(name='udk', log_uri='s3://udk-bucket/jobflow_logs', master_instance_type='m2.xlarge', slave_instance_type='m2.xlarge', num_instances=nodenum, keep_alive=True, enable_debugging=False, bootstrap_actions=[configure_hadoop_action], hadoop_version='1.0.3', steps=steps) emr_connection.set_termination_protection(jobflow_id, True) return jobflow_id except Exception, e: return "none"
def run(self): """Run the Hive job on EMR cluster """ # copy the data source to a new object # (Hive deletes/moves the original) copy_s3_file(self.input_path, self.data_path) # and create the hive script self._generate_and_upload_hive_script() logger.info("Waiting {} seconds for S3 eventual consistency".format( self.s3_sync_wait_time)) time.sleep(self.s3_sync_wait_time) # TODO more options like setting aws region conn = EmrConnection(self.aws_access_key_id, self.aws_secret_access_key) setup_step = InstallHiveStep(self.hive_version) run_step = HiveStep(self.job_name, self.script_path) jobid = conn.run_jobflow( self.job_name, self.log_path, action_on_failure='CANCEL_AND_WAIT', master_instance_type=self.master_instance_type, slave_instance_type=self.slave_instance_type, ami_version=self.ami_version, num_instances=self.num_instances) conn.add_jobflow_steps(jobid, [setup_step, run_step]) self._wait_for_job_to_complete(conn, jobid) logger.info("Output file is in: {0}".format(self.output_path))
def __init__(self, team_id, access_key, secret_key, bucket='cs144students'): """ (constructor) Creates a new instance of the Rankmaniac class for a specific team using the provided credentials. Arguments: team_id <str> the team identifier, which may be differ slightly from the actual team name. access_key <str> the AWS access key identifier. secret_key <str> the AWS secret acess key. Keyword arguments: bucket <str> the S3 bucket name. """ region = RegionInfo(None, self.DefaultRegionName, self.DefaultRegionEndpoint) self._s3_bucket = bucket self._s3_conn = S3Connection(access_key, secret_key) self._emr_conn = EmrConnection(access_key, secret_key, region=region) self.team_id = team_id self.job_id = None self._reset() self._num_instances = 1
def __init__(self, prop): '''Constructor, initialize EMR connection.''' self.prop = prop self.conn = EmrConnection(self.prop.ec2.key, self.prop.ec2.secret) self.jobid = None self.retry = 0 self.level = 0 self.last_update = -1
def get_cluster_status(cluster_id): try: emr_connection = EmrConnection() flow = emr_connection.describe_jobflow(cluster_id) if flow == None: return "none" return flow.state except Exception, e: return "none"
def terminate(cluster_id): try: emr_connection = EmrConnection() emr_connection.set_termination_protection(cluster_id, False) emr_connection.terminate_jobflow(cluster_id) return True except Exception, e: print e return False
def create_data_source_variable(cluster_id, cr): """ Creates a data source variable .json file using the cluster_id of an EMR cluster_id @PARAM: cluster_id: ID of an EMR cluster return: True if success, creates a file in the pwd 'default_emr.json' Object created should look like: HADOOP_DATA_SOURCE_NAME="emr_data_source" HADOOP_DATA_SOURCE_DISTRO="Cloudera CDH5.4-5.7" HADOOP_DATA_SOURCE_HOST="emr_master_dns_hostname" HADOOP_DATA_SOURCE_PORT=8020 HADOOP_DATA_SOURCE_USER="******" HADOOP_DATA_SOURCE_GROUP="hadoop" HADOOP_DATA_SOURCE_JT_HOST="emr_master_dns_hostname" HADOOP_DATA_SOURCE_JT_PORT=8032 CONNECTION_PARAMETERS='[{"key":"mapreduce.jobhistory.address", "value":"0.0.0.0:10020"}, ' \ '{"key":"mapreduce.jobhistory.webapp.address", "value":"cdh5hakerberosnn.alpinenow.local:19888"}, ' \ '{"key":"yarn.app.mapreduce.am.staging-dir", "value":"/tmp/hadoop-yarn/staging"}, ' \ '{"key":"yarn.resourcemanager.admin.address", "value":"cdh5hakerberosnn.alpinenow.local:8033"}, ' \ '{"key":"yarn.resourcemanager.resource-tracker.address", "value":"cdh5hakerberosnn.alpinenow.local:8031"}, ' \ '{"key":"yarn.resourcemanager.scheduler.address", "value":"cdh5hakerberosnn.alpinenow.local:8030"}]' """ conn = EmrConnection( cr.get_config("aws_access_key"), cr.get_config("aws_secret_key"), region = RegionInfo(name = cr.get_config("aws_region"), endpoint = cr.get_config("aws_region") + ".elasticmapreduce.amazonaws.com" )) emr_cluster = conn.describe_cluster(cluster_id) master_dns_hostname = emr_cluster.masterpublicdnsname # Build up connection parameters conn_params = [] conn_params.append({"key": "mapreduce.jobhistory.address", "value": "{0}:10020".format(master_dns_hostname)}) conn_params.append({"key": "mapreduce.jobhistory.webapp.address", "value": "{0}:19888".format(master_dns_hostname)}) conn_params.append({"key": "yarn.app.mapreduce.am.staging-dir", "value": "/user"}) conn_params.append({"key": "yarn.resourcemanager.admin.address", "value": "{0}:8033".format(master_dns_hostname)}) conn_params.append({"key": "yarn.resourcemanager.scheduler.address", "value": "{0}:8030".format(master_dns_hostname)}) conn_params_str = "CONNECTION_PARAMETERS=\"{0}\"".format(conn_params) email_str = "EMAIL=\"avalanche_{0}.alpinenow.com\"".format(random.randint(1,99999)) with open("emr_default.conf", "w") as f: f.writelines("HADOOP_DATA_SOURCE_NAME=\"{0}\"\n".format(cr.get_config("emr_cluster_name"))) f.writelines("HADOOP_DATA_SOURCE_DISTRO=\"{0}\"\n".format("Amazon EMR5")) f.writelines("HADOOP_DATA_SOURCE_HOST=\"{0}\"\n".format(master_dns_hostname)) f.writelines("HADOOP_DATA_SOURCE_POST=\"8020\"\n") f.writelines("HADOOP_DATA_SOURCE_USER=\"hdfs\"\n") f.writelines("HADOOP_DATA_SOURCE_GROUP=\"hadoop\"\n") f.writelines("HADOOP_DATA_SOURCE_JT_HOST=\"{0}\"\n".format(master_dns_hostname)) f.writelines("HADOOP_DATA_SOURCE_JT_PORT=\"8032\"\n") f.writelines(email_str) f.writelines(conn_params_str)
def get_job_flow_objects(conf_path, max_days_ago=None, now=None): """Get relevant job flow information from EMR. Args: conf_path: is a string that is either None or has an alternate path to load the configuration file. max_days_ago: A float where if set, dont fetch job flows created longer than this many days ago. now: the current UTC time as a datetime.datetime object. defaults to the current time. Returns: job_flows: A list of boto job flow objects. """ if now is None: now = datetime.datetime.utcnow() emr_conn = None emr_conn = EmrConnection() # if --max-days-ago is set, only look at recent jobs created_after = None if max_days_ago is not None: created_after = now - datetime.timedelta(days=max_days_ago) return describe_all_job_flows(emr_conn, created_after=created_after)
def __init__(self, team_id, access_key, secret_key): '''Rankmaniac class constructor Creates a new instance of the Rankmaniac Wrapper for a specific team. Arguments: team_id string the team ID. access_key string AWS access key. secret_key string AWS secret key. ''' self.s3_bucket = 'cs144caltech' self.team_id = team_id self.emr_conn = EmrConnection(access_key, secret_key) self.s3_conn = S3Connection(access_key, secret_key) self.job_id = None
def get_internal_ips_from_emr(cluster_id, cr): """ Retrieves a list of internal IP addresses for a given EMR cluster """ # Open connection to EMR conn = EmrConnection( cr.get_config("aws_access_key"), cr.get_config("aws_secret_key"), region = RegionInfo(name = cr.get_config("aws_region"), endpoint = cr.get_config("aws_region") + ".elasticmapreduce.amazonaws.com" )) # Build list of internal ips from list_instances EMR API emr_internal_ips = [] emr_instances = conn.list_instances(cluster_id).instances for instance in emr_instances: emr_internal_ips.append(instance.privateipaddress) return emr_internal_ips
def get_internal_ips_from_emr(cluster_id, cr): """ Retrieves a list of internal IP addresses for a given EMR cluster """ # Open connection to EMR conn = EmrConnection( cr.get_config("aws_access_key"), cr.get_config("aws_secret_key"), region=RegionInfo(name=cr.get_config("aws_region"), endpoint=cr.get_config("aws_region") + ".elasticmapreduce.amazonaws.com")) # Build list of internal ips from list_instances EMR API emr_internal_ips = [] emr_instances = conn.list_instances(cluster_id).instances for instance in emr_instances: emr_internal_ips.append(instance.privateipaddress) return emr_internal_ips
def __init__(self, parameters): try: self.region_name = parameters["region_name"] self.access_key = parameters["access_key"] self.secret_key = parameters["secret_key"] self.ec2_keypair_name = parameters["ec2_keypair_name"] self.base_bucket = parameters["base_bucket"] self.log_dir = parameters["log_dir"] self.emr_status_wait = parameters["emr_status_wait"] self.step_status_wait = parameters["step_status_wait"] self.emr_cluster_name = parameters["emr_cluster_name"] except: logging.error("Something went wrong initializing EmrManager") sys.exit() # Establishing EmrConnection self.connection = EmrConnection(self.access_key, self.secret_key, region=RegionInfo(name=self.region_name, endpoint=self.region_name + '.elasticmapreduce.amazonaws.com')) self.log_bucket_name = self.base_bucket + self.log_dir
def __init__(self, region_name='us-east-1', aws_access_key_id=None, aws_secret_access_key=None): # If the access key is not specified, get it from the luigi config.cfg file if not aws_access_key_id: aws_access_key_id = luigi.configuration.get_config().get( 'aws', 'aws_access_key_id') if not aws_secret_access_key: aws_secret_access_key = luigi.configuration.get_config().get( 'aws', 'aws_secret_access_key') # Create the region in which to run region_endpoint = u'elasticmapreduce.%s.amazonaws.com' % (region_name) region = RegionInfo(name=region_name, endpoint=region_endpoint) self.emr_connection = EmrConnection( aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, region=region)
def run(self): """Run the Hive job on EMR cluster """ # copy the data source to a new object # (Hive deletes/moves the original) copy_s3_file(self.input_path, self.data_path) # and create the hive script self._generate_and_upload_hive_script() logger.info("Waiting {} seconds for S3 eventual consistency".format( self.s3_sync_wait_time)) time.sleep(self.s3_sync_wait_time) # TODO more options like setting aws region conn = EmrConnection(self.aws_access_key_id, self.aws_secret_access_key) setup_step = InstallHiveStep(self.hive_version) run_step = HiveStep(self.job_name, self.script_path) cluster_id = conn.run_jobflow( self.job_name, self.log_path, action_on_failure='CANCEL_AND_WAIT', master_instance_type=self.master_instance_type, slave_instance_type=self.slave_instance_type, ami_version=self.ami_version, num_instances=self.num_instances, job_flow_role=self.iam_instance_profile, service_role=self.iam_service_role) conn.add_jobflow_steps(cluster_id, [setup_step, run_step]) logger.info("Job started on cluster {0}".format(cluster_id)) self._wait_for_job_to_complete(conn, cluster_id) logger.info("Output file is in: {0}".format(self.output_path))
def __init__(self, spec_filename="spec.json"): import boto from boto.emr.connection import EmrConnection, RegionInfo super(HiveRuntime, self).__init__(spec_filename) p = self.settings.Param self.s3_conn = boto.connect_s3(p.AWS_ACCESS_KEY_ID, p.AWS_ACCESS_KEY_SECRET) self.s3_bucket = self.s3_conn.get_bucket(p.S3_BUCKET) self.region = p.AWS_Region self.emr_conn = EmrConnection(p.AWS_ACCESS_KEY_ID, p.AWS_ACCESS_KEY_SECRET, region = RegionInfo(name = self.region, endpoint = self.region + '.elasticmapreduce.amazonaws.com')) self.job_flow_id = p.EMR_jobFlowId
def __init__(self, spec_filename="spec.json"): import boto from boto.emr.connection import EmrConnection, RegionInfo super(EmrRuntime, self).__init__(spec_filename) p = self.settings.Param self.s3_conn = boto.connect_s3(p.AWS_ACCESS_KEY_ID, p.AWS_ACCESS_KEY_SECRET) self.s3_bucket = self.s3_conn.get_bucket(p.S3_BUCKET) self.region = p.AWS_Region self.emr_conn = EmrConnection(p.AWS_ACCESS_KEY_ID, p.AWS_ACCESS_KEY_SECRET, region = RegionInfo(name = self.region, endpoint = self.region + '.elasticmapreduce.amazonaws.com')) self.job_flow_id = p.EMR_jobFlowId
class EMR: def creating_a_connection(self): #Creating a connection from boto.emr.connection import EmrConnection self.conn = EmrConnection('', '') def creating_streaming_job(self): #Creating Streaming JobFlow Steps from boto.emr.step import StreamingStep self.step = StreamingStep(name='my bigdata task', mapper='s3n://eth-src/raw_to_stations.py', #mapper='s3n://elasticmapreduce/samples/wordcount/wordSplitter.py', reducer='s3n://eth-src/stations_to_features.py', #reducer='aggregate', input='s3n://eth-input/2007.csv', #input='s3n://elasticmapreduce/samples/wordcount/input', output='s3n://eth-middle/2007') def creating_jobflows(self): #Creating JobFlows #import boto.emr #self.conn = boto.emr.connect_to_region('eu-west-1') job_id = self.conn.run_jobflow(name='My jobflow', log_uri='s3://eth-log/jobflow_logs', master_instance_type='m3.xlarge', slave_instance_type='m1.large', num_instances=2, steps=[self.step], ami_version='3.3.1' ) status = self.conn.describe_jobflow(job_id) status.state def terminating_jobflows(self, job_id): #Terminating JobFlows #self.conn = boto.emr.connect_to_region('eu-west-1') self.conn.terminate_jobflow(job_id)
def main(argv): # load the config config = ConfigParser() config.read(os.path.join(os.path.split(argv[0])[0] if not None else '','config.ini')) # load AWS config awsConfig = ConfigParser() awsConfig.read(config.get('Common','aws')) aws_access_key = awsConfig.get('AWS','aws_access_key') aws_secret_key = awsConfig.get('AWS','aws_secret_key') event_bucket = awsConfig.get('AWS','event_bucket') output_bucket = awsConfig.get('AWS','emr_output_bucket') script_bucket = awsConfig.get('AWS','script_bucket') jobId = argv[1] emrConnection = EmrConnection(aws_access_key, aws_secret_key) s3Connection = S3Connection(aws_access_key, aws_secret_key) # clean s3 output bucket = s3Connection.get_bucket(output_bucket) for key in bucket.get_all_keys(prefix=BUCKET_KEY): bucket.delete_key(key) step = StreamingStep(name='Foursquare event deduper', mapper='s3://%s/dedup_mapper.py foursquare' % script_bucket, reducer='s3://%s/dedup_reducer.py' % script_bucket, input='s3://%s/normalized' % event_bucket, output='s3://%s/%s' % (output_bucket,BUCKET_KEY), action_on_failure='CONTINUE') emrConnection.add_jobflow_steps(jobId, step) print 'Successfully started streaming steps'
class EMRInventory(): def __init__(self, region='eu-west-1'): regionEMR = self.get_emr_region(region) self.emrConnection = EmrConnection(region=regionEMR) def list_current_resources(self, region='eu-west-1'): jobFlows = self.emrConnection.describe_jobflows() for jobFlow in jobFlows: print jobFlow.jobflowid def get_emr_region(self, region='eu-west-1'): regionEndpoint = '%s.elasticmapreduce.amazonaws.com' % region regionEMR = RegionInfo (name=region, endpoint=regionEndpoint) return regionEMR
class EmrJarRuntime(ZetRuntime): def __init__(self, spec_filename="spec.json"): import boto from boto.emr.connection import EmrConnection, RegionInfo # super(ZetRuntime, self).__init__() # TODO self.settings = get_settings_from_file(spec_filename) p = self.settings.Param self.s3_conn = boto.connect_s3(p.AWS_ACCESS_KEY_ID, p.AWS_ACCESS_KEY_SECRET) self.s3_bucket = self.s3_conn.get_bucket(p.S3_BUCKET) self.region = p.AWS_Region self.emr_conn = EmrConnection(p.AWS_ACCESS_KEY_ID, p.AWS_ACCESS_KEY_SECRET, region = RegionInfo(name = self.region, endpoint = self.region + '.elasticmapreduce.amazonaws.com')) self.job_flow_id = p.EMR_jobFlowId def get_s3_working_dir(self, path=""): ps = self.settings glb_vars = ps.GlobalParam return os.path.join('zetjob', glb_vars['userName'], "job%s" % glb_vars['jobId'], "blk%s" % glb_vars['blockId'], path) def execute(self, jar_path, args): from boto.emr.step import JarStep s3_jar_path = s3_upload(self.s3_bucket, self.get_s3_working_dir(jar_path), jar_path) # s3_jar_path = "s3://run-jars/jar/mahout-core-1.0-SNAPSHOT-job.jar" print("Uploading jar to s3 : %s -> %s" % (jar_path, s3_jar_path)) print("Add jobflow step") step = JarStep(name='cl_filter', jar=s3_jar_path, step_args=args) self.emr_conn.add_jobflow_steps(self.job_flow_id, steps=[step]) print("Waiting jobflow step done") emr_wait_job(self.emr_conn, self.job_flow_id)
def __init__(self, region_name='us-east-1', aws_access_key_id=None, aws_secret_access_key=None): # If the access key is not specified, get it from the luigi config.cfg file if not aws_access_key_id: aws_access_key_id = luigi.configuration.get_config().get('aws', 'aws_access_key_id') if not aws_secret_access_key: aws_secret_access_key = luigi.configuration.get_config().get('aws', 'aws_secret_access_key') # Create the region in which to run region_endpoint = u'elasticmapreduce.%s.amazonaws.com' % (region_name) region = RegionInfo(name=region_name, endpoint=region_endpoint) self.emr_connection = EmrConnection(aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, region=region)
def __init__(self): try: self.zone_name = "ap-southeast-1" self.access_key = "xxxxxx" self.private_key = "xxxxxxx" self.ec2_keyname = "xxxxxxxx" self.base_bucket = "s3://emr-bucket/" self.bootstrap_script = "custom-bootstrap.sh" self.log_dir = "Logs" self.emr_status_wait = 20 self.conn = "" self.cluster_name = "MyFirstEmrCluster" # Establishing EmrConnection self.conn = EmrConnection(self.access_key, self.private_key, region=RegionInfo(name=self.zone_name, endpoint=self.zone_name + '.elasticmapreduce.amazonaws.com')) self.log_bucket_name = self.base_bucket + self.log_dir self.bootstrap_script_name = self.base_bucket + self.bootstrap_script
k = Key(b) k.key = 'reducer.py' k.set_contents_from_filename('/Users/winteram/Documents/Teaching/reducer.py') k.close() # <codecell> for word in b.list(): print word # <codecell> ### Running code with EMR #emrcon = EmrConnection('<aws access key>', '<aws secret key>') emrcon = EmrConnection('AKIAJRV3RN6NXQTSSTBA', '3e212d6rs99xtiPgwKnfN1QD30WZk2hJwCWjMcGc') # <codecell> # Using EMR's wordcount example step = StreamingStep( name='My wordcount example', mapper='s3n://elasticmapreduce/samples/wordcount/wordSplitter.py', reducer='aggregate', input='s3n://elasticmapreduce/samples/wordcount/input', output='s3n://wambia660fall2013/output/wordcount_output') # <codecell> jobid = emrcon.run_jobflow(name='Word Count Example', log_uri='s3://wambia660fall2013/logs',
class EMRCluster(object): '''Representation of an EMR cluster. TODO: add bridge to boto interface for unit test. ''' emr_status_delay = 10 # in sec emr_status_max_delay = 60 # in sec emr_status_max_error = 30 # number of errors emr_max_idle = 10 * 60 # 10 min (in sec) rate_limit_lock = RateLimitLock() def __init__(self, prop): '''Constructor, initialize EMR connection.''' self.prop = prop self.conn = EmrConnection(self.prop.ec2.key, self.prop.ec2.secret) self.jobid = None self.retry = 0 self.level = 0 self.last_update = -1 @property def priority(self): '''The priority used in EMRManager. The lower value, the higher priority. ''' with EMRCluster.rate_limit_lock: if self.jobid is None: return 1 return 0 def get_instance_groups(self): '''Get instance groups to start a cluster. It calculates the price with self.level, which indicates the price upgrades from the original price. ''' instance_groups = [] for group in self.prop.emr.instance_groups: (num, group_name, instance_type) = group level = max(0, min(self.level, len(self.prop.emr.price_upgrade_rate) - 1)) # 0 <= level < len(...) bprice = self.prop.emr.prices[ instance_type] * self.prop.emr.price_upgrade_rate[level] name = '%s-%s@%f' % (group_name, 'SPOT', bprice) # Use on-demand instance if prices are zero. if bprice > 0: ig = InstanceGroup(num, group_name, instance_type, 'SPOT', name, '%.3f' % bprice) else: ig = InstanceGroup(num, group_name, instance_type, 'ON_DEMAND', name) instance_groups.append(ig) return instance_groups def get_bootstrap_actions(self): '''Get list of bootstrap actions from property''' actions = [] for bootstrap_action in self.prop.emr.bootstrap_actions: assert len(bootstrap_action ) >= 2, 'Wrong bootstrap action definition: ' + str( bootstrap_action) actions.append( BootstrapAction(bootstrap_action[0], bootstrap_action[1], bootstrap_action[2:])) return actions @synchronized def start(self): '''Start a EMR cluster.''' # emr.project_name is required if self.prop.emr.project_name is None: raise ValueError('emr.project_name is not set') self.last_update = time.time() with EMRCluster.rate_limit_lock: self.jobid = self.conn.run_jobflow( name=self.prop.emr.cluster_name, ec2_keyname=self.prop.emr.keyname, log_uri=self.prop.emr.log_uri, ami_version=self.prop.emr.ami_version, bootstrap_actions=self.get_bootstrap_actions(), keep_alive=True, action_on_failure='CONTINUE', api_params={'VisibleToAllUsers': 'true'}, instance_groups=self.get_instance_groups()) message('Job flow created: %s', self.jobid) # Tag EC2 instances to allow future analysis tags = { 'FlowControl': 'Briefly', 'Project': self.prop.emr.project_name } if self.prop.emr.tags is not None: assert isinstance(self.prop.emr.tags, dict) tags = dict(tags.items() + self.prop.emr.tags.items()) self.conn.add_tags(self.jobid, tags) @synchronized def terminate(self, level_upgrade=0): '''Terminate this EMR cluster.''' if self.jobid is None: return self.level += level_upgrade # upgrade to another price level message('Terminate jobflow: %s', self.jobid) for i in range(3): try: with EMRCluster.rate_limit_lock: self.conn.terminate_jobflow(self.jobid) break except Exception as e: message('Unable to terminate job flow: %s', self.jobid) message(traceback.format_exc()) # We have to set jobid as None to create new cluster; # otherwise, run_steps will keep launching jobs on the bad cluster. self.jobid = None def is_idle(self): '''Check if this EMR cluster is idle?''' return (not self.jobid is None) and ( (time.time() - self.last_update) > self.emr_max_idle) def get_steps(self, node): '''Get the jar step from the node.''' step = JarStep(name=node.config.sub(node.config.emr.step_name, node_hash=node.hash()), main_class=node.config.main_class, jar=node.config.hadoop.jar, action_on_failure='CONTINUE', step_args=node.process_args(*node.config.args)) return [step] def get_step_index(self, step_id): '''Get the index of a step given step_id (1 based)''' steps = [ step.id for step in reversed(self.conn.list_steps(self.jobid).steps) if step.status is not None ] # revert the index since latest step is on top of the list return steps.index(step_id) + 1 def run_steps(self, node, wait=True): '''Main loop to execute a node. It will block until step complete or failure, and will raise exception for failures so that the step will be retried. TODO: add timeouts for each step? TODO: dynamic increase cluster size? ''' if not self.jobid: self.start() try: with EMRCluster.rate_limit_lock: # Here we just add single step. And get the step_id for fallowing checks. step_id = self.conn.add_jobflow_steps( self.jobid, self.get_steps(node)).stepids[0].value assert step_id is not None except Exception as e: node.log('Unable to add jobflow steps: %s', node.hash()) node.log('%s', traceback.format_exc()) raise HadoopFailure() status_error_counter = 0 step_status = 'PENDING' step_index = None step_start = time.time() # notify the node with status. node.notify_status('Running on EMR: %s', self.jobid) while wait and step_status in ['PENDING', 'RUNNING']: try: # wait first for the status turning to 'RUNNING' from 'WAITING'. Exponential delay for errors. # Cap delay to a predefined limit. delay = min(self.emr_status_delay * (2**status_error_counter), self.emr_status_max_delay) time.sleep(delay) # Keep current cluster alive. self.last_update = time.time() # Get current cluster status. May raise exception due to EMR request throttle. cluster_state = self.conn.describe_cluster( self.jobid).status.state if step_index is None: step_index = self.get_step_index(step_id) node.log('Step #: %d', step_index) node.log('Log URI: %s/%s/steps/%d/', node.config.emr.log_uri, self.jobid, step_index) step_status = self.conn.describe_step(self.jobid, step_id).status.state status_error_counter = 0 # reset counter node.log("%s: %s %s", self.jobid, cluster_state, step_status) if cluster_state in [ 'TERMINATING', 'TERMINATED', 'TERMINATED_WITH_ERRORS' ]: # cluster kill (maybe due to spot price), upgrade. self.terminate(1) break if ( time.time() - step_start ) > node.config.emr.step_timeout: # Step running too long? EMR cluster idle. node.log('Step running too long. Restart with new cluster') self.terminate() break except KeyboardInterrupt: raise except Exception as e: node.log('EMR loop exception: %d error(s)', status_error_counter) status_error_counter += 1 if status_error_counter > self.emr_status_max_error: self.terminate() node.log('Too many errors in EMR loop') node.log('Exception: %s', traceback.format_exc()) raise if step_status != 'COMPLETED': raise HadoopFailure()
class Rankmaniac: """ (wrapper class) This class presents a simple wrapper around the AWS SDK. It strives to provide all the functionality required to run map-reduce (Hadoop) on Amazon. This way the students do not need to worry about learning the API for Amazon S3 and EMR, and instead can focus on computing pagerank quickly! """ DefaultRegionName = 'us-west-2' DefaultRegionEndpoint = 'elasticmapreduce.us-west-2.amazonaws.com' def __init__(self, team_id, access_key, secret_key, bucket='cs144students'): """ (constructor) Creates a new instance of the Rankmaniac class for a specific team using the provided credentials. Arguments: team_id <str> the team identifier, which may be differ slightly from the actual team name. access_key <str> the AWS access key identifier. secret_key <str> the AWS secret acess key. Keyword arguments: bucket <str> the S3 bucket name. """ region = RegionInfo(None, self.DefaultRegionName, self.DefaultRegionEndpoint) self._s3_bucket = bucket self._s3_conn = S3Connection(access_key, secret_key) self._emr_conn = EmrConnection(access_key, secret_key, region=region) self.team_id = team_id self.job_id = None self._reset() self._num_instances = 1 def _reset(self): """ Resets the internal state of the job and submission. """ self._iter_no = 0 self._infile = None self._last_outdir = None self._last_process_step_iter_no = -1 self._is_done = False def __del__(self): """ (destructor) Terminates the map-reduce job if any, and closes the connections to Amazon S3 and EMR. """ if self.job_id is not None: self.terminate() self._s3_conn.close() self._emr_conn.close() def __enter__(self): """ Used for `with` syntax. Simply returns this instance since the set-up has all been done in the constructor. """ return self def __exit__(self, type, value, traceback): """ Refer to __del__(). """ self.__del__() return False # do not swallow any exceptions def upload(self, indir='data'): """ Uploads the local data to Amazon S3 under the configured bucket and key prefix (the team identifier). This way the code can be accessed by Amazon EMR to compute pagerank. Keyword arguments: indir <str> the base directory from which to upload contents. Special notes: This method only uploads **files** in the specified directory. It does not scan through subdirectories. WARNING! This method removes all previous (or ongoing) submission results, so it is unsafe to call while a job is already running (and possibly started elsewhere). """ if self.job_id is not None: raise RankmaniacError('A job is already running.') bucket = self._s3_conn.get_bucket(self._s3_bucket) # Clear out current bucket contents for team keys = bucket.list(prefix=self._get_keyname()) bucket.delete_keys(keys) for filename in os.listdir(indir): relpath = os.path.join(indir, filename) if os.path.isfile(relpath): keyname = self._get_keyname(filename) key = bucket.new_key(keyname) key.set_contents_from_filename(relpath) def set_infile(self, filename): """ Sets the data file to use for the first iteration of the pagerank step in the map-reduce job. """ if self.job_id is not None: raise RankmaniacError('A job is already running.') self._infile = filename def do_iter(self, pagerank_mapper, pagerank_reducer, process_mapper, process_reducer, pagerank_output=None, process_output=None, num_pagerank_mappers=1, num_pagerank_reducers=1): """ Adds a pagerank step and a process step to the current job. """ num_process_mappers = 1 num_process_reducers = 1 if self._iter_no == 0: pagerank_input = self._infile elif self._iter_no > 0: pagerank_input = self._last_outdir if pagerank_output is None: pagerank_output = self._get_default_outdir('pagerank') # Output from the pagerank step becomes input to process step process_input = pagerank_output if process_output is None: process_output = self._get_default_outdir('process') pagerank_step = self._make_step(pagerank_mapper, pagerank_reducer, pagerank_input, pagerank_output, num_pagerank_mappers, num_pagerank_reducers) process_step = self._make_step(process_mapper, process_reducer, process_input, process_output, num_process_mappers, num_process_reducers) steps = [pagerank_step, process_step] if self.job_id is None: self._submit_new_job(steps) else: self._emr_conn.add_jobflow_steps(self.job_id, steps) # Store `process_output` directory so it can be used in # subsequent iteration self._last_outdir = process_output self._iter_no += 1 def is_done(self): """ Returns `True` if the map-reduce job is done, and `False` otherwise. For all process-step output files that have not been fetched, gets the first part of the output file, and checks whether its contents begins with the string 'FinalRank'. Special notes: WARNING! The usage of this method in your code requires that that you used the default output directories in all calls to do_iter(). """ # Cache the result so we can return immediately without hitting # any of the Amazon APIs if self._is_done: return True iter_no = self._get_last_process_step_iter_no() if iter_no < 0: return False while self._last_process_step_iter_no < iter_no: self._last_process_step_iter_no += 1 i = self._last_process_step_iter_no outdir = self._get_default_outdir('process', iter_no=i) keyname = self._get_keyname(outdir, 'part-00000') bucket = self._s3_conn.get_bucket(self._s3_bucket) key = Key(bucket=bucket, name=keyname) contents = key.next() # get first chunk of the output file if contents.startswith('FinalRank'): self._is_done = True # cache result break return self._is_done def is_alive(self): """ Checks whether the jobflow has completed, failed, or been terminated. Special notes: WARNING! This method should only be called **after** is_done() in order to be able to distinguish between the cases where the map-reduce job has outputted 'FinalRank' on its final iteration and has a 'COMPLETED' state. """ jobflow = self.describe() if jobflow.state in ('COMPLETED', 'FAILED', 'TERMINATED'): return False return True def terminate(self): """ Terminates a running map-reduce job. """ if not self.job_id: raise RankmaniacError('No job is running.') self._emr_conn.terminate_jobflow(self.job_id) self.job_id = None self._reset() def download(self, outdir='results'): """ Downloads the results from Amazon S3 to the local directory. Keyword arguments: outdir <str> the base directory to which to download contents. Special notes: This method downloads all keys (files) from the configured bucket for this particular team. It creates subdirectories as needed. """ bucket = self._s3_conn.get_bucket(self._s3_bucket) keys = bucket.list(prefix=self._get_keyname()) for key in keys: keyname = key.name # Ignore folder keys if '$' not in keyname: suffix = keyname.split('/')[1:] # removes team identifier filename = os.path.join(outdir, *suffix) dirname = os.path.dirname(filename) if not os.path.exists(dirname): os.makedirs(dirname) key.get_contents_to_filename(filename) def describe(self): """ Gets the current map-reduce job details. Returns a boto.emr.emrobject.JobFlow object. Special notes: The JobFlow object has the following relevant fields. state <str> the state of the job flow, either COMPLETED | FAILED | TERMINATED | RUNNING | SHUTTING_DOWN | STARTING | WAITING steps <list(boto.emr.emrobject.Step)> a list of the step details in the workflow. The Step object has the following relevant fields. state <str> the state of the step. startdatetime <str> the start time of the job. enddatetime <str> the end time of the job. WARNING! Amazon has an upper-limit on the frequency with which you can call this method; we have had success with calling it at most once every 10 seconds. """ if not self.job_id: raise RankmaniacError('No job is running.') return self._emr_conn.describe_jobflow(self.job_id) def _get_last_process_step_iter_no(self): """ Returns the most recently process-step of the job flow that has been completed. """ steps = self.describe().steps i = 1 while i < len(steps): step = steps[i] if step.state != 'COMPLETED': break i += 2 return i / 2 - 1 def _get_default_outdir(self, name, iter_no=None): """ Returns the default output directory, which is 'iter_no/name/'. """ if iter_no is None: iter_no = self._iter_no # Return iter_no/name/ **with** the trailing slash return '%s/%s/' % (iter_no, name) def _submit_new_job(self, steps): """ Submits a new job to run on Amazon EMR. """ if self.job_id is not None: raise RankmaniacError('A job is already running.') job_name = self._make_name() num_instances = self._num_instances log_uri = self._get_s3_team_uri('job_logs') self.job_id = self._emr_conn.run_jobflow(name=job_name, steps=steps, num_instances=num_instances, log_uri=log_uri) def _make_step(self, mapper, reducer, input, output, num_mappers=1, num_reducers=1): """ Returns a new step that runs the specified mapper and reducer, reading from the specified input and writing to the specified output. """ bucket = self._s3_conn.get_bucket(self._s3_bucket) # Clear out current bucket/output contents for team keys = bucket.list(prefix=self._get_keyname(output)) bucket.delete_keys(keys) step_name = self._make_name() step_args = [ '-jobconf', 'mapred.map.tasks=%d' % (num_mappers), '-jobconf', 'mapred.reduce.tasks=%d' % (num_reducers) ] return StreamingStep(name=step_name, step_args=step_args, mapper=self._get_s3_team_uri(mapper), reducer=self._get_s3_team_uri(reducer), input=self._get_s3_team_uri(input), output=self._get_s3_team_uri(output)) def _make_name(self): return strftime('%%s %m-%d-%Y %H:%M:%S', localtime()) % (self.team_id) def _get_keyname(self, *args): """ Returns the key name to use in the grading bucket (for the particular team). 'team_id/...' """ return '%s/%s' % (self.team_id, '/'.join(args)) def _get_s3_team_uri(self, *args): """ Returns the Amazon S3 URI for the team submissions. """ return 's3n://%s/%s' % (self._s3_bucket, self._get_keyname(*args))
import os import sys import dateutil.parser from dateutil import tz from boto.emr.connection import EmrConnection from boto.s3.connection import S3Connection from ucsd_bigdata.credentials import Credentials import gzip if __name__ == "__main__": credentials = Credentials() aws_access_key_id = credentials.aws_access_key_id aws_secret_access_key = credentials.aws_secret_access_key emr_conn = EmrConnection(aws_access_key_id, aws_secret_access_key) # List EMR Clusters clusters = emr_conn.list_clusters(cluster_states=["RUNNING", "WAITING"]) for index, cluster in enumerate(clusters.clusters): print "[%s] %s" % (index, cluster.id) # if there is a command line arg, use it for the cluster_id if len(sys.argv) > 1: cluster_id = sys.argv[1] else: if len(clusters.clusters) == 0: sys.exit("No EMR clusters running.") selected_cluster = input("Select a Cluster: ") cluster_id = clusters.clusters[int(selected_cluster)].id
class Rankmaniac: '''Rankmaniac Wrapper This class provides a simple wrapper around the Amazon Web Services SDK. It should provide all the functionality required in terms of MapReduce, so students don't need to worry about learning the EMR and S3 API. ''' def __init__(self, team_id, access_key, secret_key): '''Rankmaniac class constructor Creates a new instance of the Rankmaniac Wrapper for a specific team. Arguments: team_id string the team ID. access_key string AWS access key. secret_key string AWS secret key. ''' self.s3_bucket = 'cs144caltech' self.team_id = team_id self.emr_conn = EmrConnection(access_key, secret_key) self.s3_conn = S3Connection(access_key, secret_key) self.job_id = None def __del__(self): if self.job_id: self.terminate_job() def submit_job(self, mapper, reducer, input, output, num_map=1, num_reduce=1): '''Submit a new MapReduce job Submits a new MapReduce job with a single step. To add more steps, call add_step. To terminate this job, call terminate_job. Arguments: mapper string path to the mapper, relative to your data directory. reducer string path to the reducer, relative to your data directory. input string path to the input data, relative to your data directory. To specify a directory as input, ensure your path contains a trailing /. output string path to the desired output directory. num_map int number of map tasks for this job. num_reduce int number of reduce tasks for this job. ''' if self.job_id: raise Exception('There currently already exists a running job.') job_name = self._make_name() step = self._make_step(mapper, reducer, input, output, num_map, num_reduce) self.job_id = \ self.emr_conn.run_jobflow(name=job_name, steps=[step], num_instances=1, log_uri=self._get_s3_url() + 'job_logs', keep_alive=True) def terminate_job(self): '''Terminate a running MapReduce job Stops the current running job. ''' if not self.job_id: raise Exception('No job is running.') self.emr_conn.terminate_jobflow(self.job_id) self.job_id = None def get_job(self): '''Gets the running job details Returns: JobFlow object with relevant fields: state string the state of the job flow, either COMPLETED | FAILED | TERMINATED RUNNING | SHUTTING_DOWN | STARTING WAITING | BOOTSTRAPPING steps list(Step) a list of the step details in the workflow. A Step has the relevant fields: status string startdatetime string enddatetime string Note: Amazon has an upper-limit on the frequency with which you can call this function; we have had success with calling it one every 10 seconds. ''' if not self.job_id: raise Exception('No job is running.') return self.emr_conn.describe_jobflow(self.job_id) def add_step(self, mapper, reducer, input, output, num_map=1, num_reduce=1): '''Add a step to an existing job Adds a new step to an already running job flow. Note: any given job flow can support up to 256 steps. To workaround this limitation, you can always choose to submit a new job once the current job completes. Arguments: mapper string path to the mapper, relative to your data directory. reducer string path to the reducer, relative to your data directory. input string path to the input data, relative to your data directory. To specify a directory as input, ensure your path contains a trailing /. output string path to the desired output directory. ''' if not self.job_id: raise Exception('No job is running.') step = self._make_step(mapper, reducer, input, output, num_map, num_reduce) self.emr_conn.add_jobflow_steps(self.job_id, [step]) def upload(self, in_dir='data'): '''Upload local data to S3 Uploads the files in the specified directory to S3, where it can be used by Elastic MapReduce. Note: this method only uploads files in the root of in_dir. It does NOT scan through subdirectories. Arguments: in_dir string optional, defaults to 'data'. Uses this directory as the base directory from which to upload. ''' bucket = self.s3_conn.get_bucket(self.s3_bucket) keys = bucket.list(prefix='%s/' % self.team_id) bucket.delete_keys(map(lambda k: k.name, keys)) to_upload = [(os.path.join(in_dir, file_name), os.path.join(self.team_id, file_name)) for file_name in os.listdir(in_dir) if os.path.isfile(os.path.join(in_dir, file_name))] for l, r in to_upload: key = Key(bucket) key.key = r key.set_contents_from_filename(l) def download(self, out_dir='data'): '''Download S3 data to local directory Downloads S3 data to the specified directory. Note: this method DOES download the entire directory hierarchy as given by S3. It will create subdirectories as needed. Arguments: out_dir string optional, defaults to 'data'. Downloads files to this directory. ''' bucket = self.s3_conn.get_bucket(self.s3_bucket) keys = bucket.list(prefix='%s/' % self.team_id) for key in keys: fp = os.path.join(out_dir, '/'.join(key.name.split('/')[1:])) fp_dir = os.path.dirname(fp) if os.path.exists(fp): os.remove(fp) elif not os.path.exists(fp_dir): os.makedirs(fp_dir) key.get_contents_to_filename(fp) def _make_name(self): return '%s-%s' % (self.team_id, strftime('%m-%d-%Y %H:%M:%s', localtime())) def _make_step(self, mapper, reducer, input, output, nm=1, nr=1): job_name = self._make_name() team_s3 = self._get_s3_url() bucket = self.s3_conn.get_bucket(self.s3_bucket) keys = bucket.list(prefix='%s/%s' % (self.team_id, output)) bucket.delete_keys(map(lambda k: k.name, keys)) return \ StreamingStep(name=job_name, step_args= ['-jobconf', 'mapred.map.tasks=%d' % nm, '-jobconf', 'mapred.reduce.tasks=%d' % nr], mapper=team_s3 + mapper, reducer=team_s3 + reducer, input=team_s3 + input, output=team_s3 + output) def _get_s3_url(self): return 's3n://%s/%s/' % (self.s3_bucket, self.team_id)
def __init__(self, user=EMR_USER, key=EMR_KEY): self.conn = EmrConnection(user, key)
class EmrClient(object): # The Hadoop version to use HADOOP_VERSION = '1.0.3' # The AMI version to use AMI_VERSION = '2.4.7' # Interval to wait between polls to EMR cluster in seconds CLUSTER_OPERATION_RESULTS_POLLING_SECONDS = 10 # Timeout for EMR creation and ramp up in seconds CLUSTER_OPERATION_RESULTS_TIMEOUT_SECONDS = 60 * 30 def __init__(self, region_name='us-east-1', aws_access_key_id=None, aws_secret_access_key=None): # If the access key is not specified, get it from the luigi config.cfg file if not aws_access_key_id: aws_access_key_id = luigi.configuration.get_config().get( 'aws', 'aws_access_key_id') if not aws_secret_access_key: aws_secret_access_key = luigi.configuration.get_config().get( 'aws', 'aws_secret_access_key') # Create the region in which to run region_endpoint = u'elasticmapreduce.%s.amazonaws.com' % (region_name) region = RegionInfo(name=region_name, endpoint=region_endpoint) self.emr_connection = EmrConnection( aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, region=region) def launch_emr_cluster( self, cluster_name, log_uri, ec2_keyname=None, master_type='m1.small', core_type='m1.small', num_instances=2, hadoop_version='1.0.3', ami_version='2.4.7', ): # TODO Remove # install_pig_step = InstallPigStep() jobflow_id = self.emr_connection.run_jobflow( name=cluster_name, log_uri=log_uri, ec2_keyname=ec2_keyname, master_instance_type=master_type, slave_instance_type=core_type, num_instances=num_instances, keep_alive=True, enable_debugging=True, hadoop_version=EmrClient.HADOOP_VERSION, steps=[], ami_version=EmrClient.AMI_VERSION) # Log important information status = self.emr_connection.describe_jobflow(jobflow_id) logger.info('Creating new cluster %s with following details' % status.name) logger.info('jobflow ID:\t%s' % status.jobflowid) logger.info('Log URI:\t%s' % status.loguri) logger.info('Master Instance Type:\t%s' % status.masterinstancetype) # A cluster of size 1 does not have any slave instances if hasattr(status, 'slaveinstancetype'): logger.info('Slave Instance Type:\t%s' % status.slaveinstancetype) logger.info('Number of Instances:\t%s' % status.instancecount) logger.info('Hadoop Version:\t%s' % status.hadoopversion) logger.info('AMI Version:\t%s' % status.amiversion) logger.info('Keep Alive:\t%s' % status.keepjobflowalivewhennosteps) return self._poll_until_cluster_ready(jobflow_id) def add_pig_step(self, jobflow_id, pig_file, name='Pig Script', pig_versions='latest', pig_args=[]): pig_step = PigStep( name=name, pig_file=pig_file, pig_versions=pig_versions, pig_args=pig_args, # action_on_failure='CONTINUE', ) self.emr_connection.add_jobflow_steps(jobflow_id, [pig_step]) # Poll until the cluster is done working return self._poll_until_cluster_ready(jobflow_id) def shutdown_emr_cluster(self, jobflow_id): self.emr_connection.terminate_jobflow(jobflow_id) return self._poll_until_cluster_shutdown(jobflow_id) def get_jobflow_id(self): # Get the id of the cluster that is WAITING for work return self.emr_connection.list_clusters( cluster_states=['WAITING']).clusters[0].id def get_master_dns(self): """ Get the master node's public address """ # Get the jobflow ID jobflow_id = self.get_master_dns() # Use the jobflow ID to get the status status = self.emr_connection.describe_jobflow(jobflow_id) # Return the master's public dns return status.masterpublicdnsname def _poll_until_cluster_ready(self, jobflow_id): start_time = time.time() is_cluster_ready = False while (not is_cluster_ready) and ( time.time() - start_time < EmrClient.CLUSTER_OPERATION_RESULTS_TIMEOUT_SECONDS): # Get the state state = self.emr_connection.describe_jobflow(jobflow_id).state if state == u'WAITING': logger.info('Cluster intialized and is WAITING for work') is_cluster_ready = True elif (state == u'COMPLETED') or \ (state == u'SHUTTING_DOWN') or \ (state == u'FAILED') or \ (state == u'TERMINATED'): logger.error('Error starting cluster; status: %s' % state) # Poll until cluster shutdown self._poll_until_cluster_shutdown(jobflow_id) raise RuntimeError('Error, cluster failed to start') else: logger.debug('Cluster state: %s' % state) time.sleep(EmrClient.CLUSTER_OPERATION_RESULTS_POLLING_SECONDS) if not is_cluster_ready: # TODO shutdown cluster raise RuntimeError( 'Timed out waiting for EMR cluster to be active') return jobflow_id def _poll_until_cluster_shutdown(self, jobflow_id): start_time = time.time() is_cluster_shutdown = False while (not is_cluster_shutdown) and ( time.time() - start_time < EmrClient.CLUSTER_OPERATION_RESULTS_TIMEOUT_SECONDS): # Get the state state = self.emr_connection.describe_jobflow(jobflow_id).state if (state == u'TERMINATED') or (state == u'COMPLETED'): logger.info('Cluster successfully shutdown with status: %s' % state) return False elif state == u'FAILED': logger.error('Cluster shutdown with FAILED status') return False else: logger.debug('Cluster state: %s' % state) time.sleep(EmrClient.CLUSTER_OPERATION_RESULTS_POLLING_SECONDS) if not is_cluster_shutdown: # TODO shutdown cluster raise RuntimeError( 'Timed out waiting for EMR cluster to shut down') return True
from boto.emr.connection import EmrConnection from boto.emr.step import InstallPigStep, PigStep AWS_ACCESS_KEY = '' # REQUIRED AWS_SECRET_KEY = '' # REQUIRED conn = EmrConnection(AWS_ACCESS_KEY, AWS_SECRET_KEY) pig_file = 's3://elasticmapreduce/samples/pig-apache/do-reports2.pig' INPUT = 's3://elasticmapreduce/samples/pig-apache/input/access_log_1' OUTPUT = '' # REQUIRED, S3 bucket for job output pig_args = ['-p', 'INPUT=%s' % INPUT, '-p', 'OUTPUT=%s' % OUTPUT] pig_step = PigStep('Process Reports', pig_file, pig_args=pig_args) steps = [InstallPigStep(), pig_step] conn.run_jobflow(name='report test', steps=steps, hadoop_version='0.20.205', ami_version='latest', num_instances=2, keep_alive=False)
PageviewsBySubredditAndPath, PageviewsByLanguage, ClickthroughsByCodename, TargetedClickthroughsByCodename, AdImpressionsByCodename, TargetedImpressionsByCodename) RAW_LOG_DIR = g.RAW_LOG_DIR PROCESSED_DIR = g.PROCESSED_DIR AGGREGATE_DIR = g.AGGREGATE_DIR AWS_LOG_DIR = g.AWS_LOG_DIR # the "or None" business is so that a blank string becomes None to cause boto # to look for credentials in other places. s3_connection = S3Connection(g.TRAFFIC_ACCESS_KEY or None, g.TRAFFIC_SECRET_KEY or None) emr_connection = EmrConnection(g.TRAFFIC_ACCESS_KEY or None, g.TRAFFIC_SECRET_KEY or None) traffic_categories = (SitewidePageviews, PageviewsBySubreddit, PageviewsBySubredditAndPath, PageviewsByLanguage, ClickthroughsByCodename, TargetedClickthroughsByCodename, AdImpressionsByCodename, TargetedImpressionsByCodename) traffic_subdirectories = { SitewidePageviews: 'sitewide', PageviewsBySubreddit: 'subreddit', PageviewsBySubredditAndPath: 'srpath', PageviewsByLanguage: 'lang', ClickthroughsByCodename: 'clicks', TargetedClickthroughsByCodename: 'clicks_targeted', AdImpressionsByCodename: 'thing', TargetedImpressionsByCodename: 'thingtarget',
k = Key(b) k.key = 'reducer.py' k.set_contents_from_filename('/Users/winteram/Documents/Teaching/reducer.py') k.close() # <codecell> for word in b.list(): print word # <codecell> ### Running code with EMR #emrcon = EmrConnection('<aws access key>', '<aws secret key>') emrcon = EmrConnection('AKIAJRV3RN6NXQTSSTBA', '3e212d6rs99xtiPgwKnfN1QD30WZk2hJwCWjMcGc') # <codecell> # Using EMR's wordcount example step = StreamingStep(name='My wordcount example', mapper='s3n://elasticmapreduce/samples/wordcount/wordSplitter.py', reducer='aggregate', input='s3n://elasticmapreduce/samples/wordcount/input', output='s3n://wambia660fall2013/output/wordcount_output') # <codecell> jobid = emrcon.run_jobflow(name='Word Count Example', log_uri='s3://wambia660fall2013/logs', steps=[step])
def _emr_connect(self): """Connect to emr. """ self.emr_conn = EmrConnection( aws_access_key_id=self.access_key_id, aws_secret_access_key=self.secret_access_key)
def create_emr_cluster(cr): """ @PARAM: Cluster configuration reader object Creates an EMR cluster given a set of configuration parameters Return: EMR Cluster ID """ #region = cr.get_config("aws_region") #conn = boto.emr.connect_to_region(region) conn = EmrConnection( cr.get_config("aws_access_key"), cr.get_config("aws_secret_key"), region = RegionInfo(name = cr.get_config("aws_region"), endpoint = cr.get_config("aws_region") + ".elasticmapreduce.amazonaws.com" )) # Create list of instance groups: master, core, and task instance_groups = [] instance_groups.append(InstanceGroup( num_instances = cr.get_config("emr_master_node_count"), role = "MASTER", type = cr.get_config("emr_master_node_type"), market = cr.get_config("emr_market_type"), name = "Master Node" )) instance_groups.append(InstanceGroup( num_instances = cr.get_config("emr_core_node_count"), role = "CORE", type = cr.get_config("emr_core_node_type"), market = cr.get_config("emr_market_type"), name = "Core Node" )) # Only create task nodes if specifcally asked for if cr.get_config("emr_task_node_count") > 0: instance_groups.append(InstanceGroup( num_instances = cr.get_config("emr_task_node_count"), role = "TASK", type = cr.get_config("emr_task_node_type"), market = cr.get_config("emr_market_type"), name = "Task Node" )) print "Creating EMR Cluster with instance groups: {0}".format(instance_groups) # Use these params to add overrrides, these will go away in Boto3 api_params = {"Instances.Ec2SubnetId": cr.get_config("aws_subnet_id"), "ReleaseLabel": cr.get_config("emr_version")} # Add step to load data step_args = ["s3-dist-cp","--s3Endpoint=s3-us-west-1.amazonaws.com","--src=s3://alpine-qa/automation/automation_test_data/","--dest=hdfs:///automation_test_data","--srcPattern=.*[a-zA-Z,]+"] step = JarStep(name = "s3distcp for data loading", jar = "command-runner.jar", step_args = step_args, action_on_failure = "CONTINUE" ) cluster_id = conn.run_jobflow( cr.get_config("emr_cluster_name"), instance_groups = instance_groups, action_on_failure = "TERMINATE_JOB_FLOW", keep_alive = True, enable_debugging = True, log_uri = cr.get_config("emr_log_uri"), #hadoop_version = "Amazon 2.7.2", #ReleaseLabel = "emr-5.0.0", #ami_version = "5.0.0", steps = [step], bootstrap_actions = [], ec2_keyname = cr.get_config("ec2_keyname"), visible_to_all_users = True, job_flow_role = "EMR_EC2_DefaultRole", service_role = "EMR_DefaultRole", api_params = api_params ) print "EMR Cluster created, cluster id: {0}".format(cluster_id) state = conn.describe_cluster(cluster_id).status.state while state != u'COMPLETED' and state != u'SHUTTING_DOWN' and state != u'FAILED' and state != u'WAITING': #sleeping to recheck for status. time.sleep(5) state = conn.describe_cluster(cluster_id).status.state print "State is: {0}, sleeping 5s...".format(state) if state == u'SHUTTING_DOWN' or state == u'FAILED': return "ERROR" #Check if the state is WAITING. Then launch the next steps if state == u'WAITING': #Finding the master node dns of EMR cluster master_dns = conn.describe_cluster(cluster_id).masterpublicdnsname print "DNS Name: {0}".format(master_dns) return cluster_id
Created by Brian Tomasette on 2011-11-02. Copyright (c) 2011 __DoublePositive__. All rights reserved. """ import re import time import datetime as dt from boto.s3.connection import S3Connection from boto.s3.key import Key from boto.ses import SESConnection from boto.emr.connection import EmrConnection from boto.emr.step import JarStep conn = S3Connection('AKIAIUCLB3MA3PL2XYNA', 'IbCFVJiiFxgPO6btlz32I67vZc9xoO+qsGpCVLhM') conns = SESConnection('AKIAIUCLB3MA3PL2XYNA', 'IbCFVJiiFxgPO6btlz32I67vZc9xoO+qsGpCVLhM') conne = EmrConnection('AKIAIUCLB3MA3PL2XYNA', 'IbCFVJiiFxgPO6btlz32I67vZc9xoO+qsGpCVLhM') def send(subject, body, to): conns.send_email('*****@*****.**', subject, body, to) message = '' success_message = '' email_content = 'The following files have been archived and a successful Hadoop job was run:\n\n\n' step1 = JarStep(name='Setup Hive', jar='s3://elasticmapreduce/libs/script-runner/script-runner.jar',
# <codecell> b = s3con.get_bucket('wambia660fall2013') # <codecell> k = Key(b) k.key = 'fullNgramNamesBoto.hql' k.set_contents_from_filename('/Users/winteram/Documents/Teaching/BIA_Fall2013/fullNgramNamesBoto.hql') k.close() # <codecell> ### Will run Hive via EMR emrcon = EmrConnection('AKIAJRV3RN6NXQTSSTBA', '3e212d6rs99xtiPgwKnfN1QD30WZk2hJwCWjMcGc') # <codecell> install_hive_step = step.InstallHiveStep(hive_versions='0.11.0.1') # <codecell> names1gram = step.HiveStep("fullNgramNamesBoto", 's3://wambia660fall2013/fullNgramNamesBoto.hql', hive_args=['-d INPUT=s3://datasets.elasticmapreduce/ngrams/books/20090715/eng-us-all/1gram/', '-d OUTPUT=s3://wambia660fall2013/output/']) # <codecell> jobid = emrcon.run_jobflow(name='Names 1gram boto v3',
class EmrManager(object): # Default constructor of the class. Uses default parameters if not provided. def __init__(self, parameters): try: self.region_name = parameters["region_name"] self.access_key = parameters["access_key"] self.secret_key = parameters["secret_key"] self.ec2_keypair_name = parameters["ec2_keypair_name"] self.base_bucket = parameters["base_bucket"] self.log_dir = parameters["log_dir"] self.emr_status_wait = parameters["emr_status_wait"] self.step_status_wait = parameters["step_status_wait"] self.emr_cluster_name = parameters["emr_cluster_name"] except: logging.error("Something went wrong initializing EmrManager") sys.exit() # Establishing EmrConnection self.connection = EmrConnection(self.access_key, self.secret_key, region=RegionInfo(name=self.region_name, endpoint=self.region_name + '.elasticmapreduce.amazonaws.com')) self.log_bucket_name = self.base_bucket + self.log_dir #Method for launching the EMR cluster def launch_cluster(self, master_type, slave_type, num_instances, ami_version): try: #Launching the cluster cluster_id = self.connection.run_jobflow( self.emr_cluster_name, self.log_bucket_name, ec2_keyname=self.ec2_keypair_name, keep_alive=True, action_on_failure = 'CANCEL_AND_WAIT', master_instance_type=master_type, slave_instance_type=slave_type, num_instances=num_instances, ami_version=ami_version) logging.info("Launching cluster: " + cluster_id + ". Please be patient. Check the status of your cluster in your AWS Console") # Checking the state of EMR cluster state = self.connection.describe_jobflow(cluster_id).state while state != u'COMPLETED' and state != u'SHUTTING_DOWN' and state != u'FAILED' and state != u'WAITING': #sleeping to recheck for status. time.sleep(int(self.emr_status_wait)) state = self.connection.describe_jobflow(cluster_id).state logging.info("Creating cluster " + cluster_id + ". Status: " + state) if state == u'SHUTTING_DOWN' or state == u'FAILED': logging.error("Launching EMR cluster failed") return "ERROR" #Check if the state is WAITING. Then launch the next steps if state == u'WAITING': #Finding the master node dns of EMR cluster master_dns = self.connection.describe_jobflow(cluster_id).masterpublicdnsname logging.info("Launched EMR Cluster Successfully with cluster id:" + cluster_id) logging.info("Master node DNS of EMR " + master_dns) return cluster_id except: logging.error("Launching EMR cluster failed") return "FAILED" # run scripting step in cluster def run_scripting_step(self, cluster_id, name, script_path): try: step = ScriptRunnerStep(name=name, step_args=[script_path], action_on_failure="CONTINUE") return self._run_step(cluster_id, step) except: logging.error("Running scripting step in cluster " + cluster_id + " failed.") return "FAILED" # run streaming step in cluster def run_streaming_step(self, cluster_id, name, mapper_path, reducer_path, input_path, output_path): try: # bundle files with the job files = [] if mapper_path != "NONE": files.append(mapper_path) mapper_path = mapper_path.split("/")[-1] if reducer_path != "NONE": files.append(reducer_path) reducer_path = reducer_path.split("/")[-1] # build streaming step logging.debug("Launching streaming step with mapper: " + mapper_path + " reducer: " + reducer_path + " and files: " + str(files)) step = StreamingStep(name=name, step_args=["-files"] + files, mapper=mapper_path, reducer=reducer_path, input=input_path, output=output_path, action_on_failure="CONTINUE") return self._run_step(cluster_id, step) except: logging.error("Running streaming step in cluster " + cluster_id + " failed.") return "FAILED" # run mapreduce jar step in cluster def run_jar_step(self, cluster_id, name, jar_path, class_name, input_path, output_path): try: # build streaming step logging.debug("Launching jar step with jar: " + jar_path + " class name: " + class_name + " input: " + input_path + " and output: " + output_path) step = JarStep(name=name, jar=jar_path, step_args= [class_name, input_path, output_path]) return self._run_step(cluster_id, step) except: logging.error("Running jar step in cluster " + cluster_id + " failed.") return "FAILED" def _run_step(self, cluster_id, step): step_list = self.connection.add_jobflow_steps(cluster_id, [step]) step_id = step_list.stepids[0].value logging.info("Starting step " + step_id + " in cluster " + cluster_id + ". Please be patient. Check the progress of the job in your AWS Console") # Checking the state of the step state = self._find_step_state(cluster_id, step_id) while state != u'NOT_FOUND' and state != u'ERROR' and state != u'FAILED' and state!=u'COMPLETED': #sleeping to recheck for status. time.sleep(int(self.step_status_wait)) state = self._find_step_state(cluster_id, step_id) logging.info("Starting step " + step_id + " in cluster " + cluster_id + ". Status: " + state) if state == u'FAILED': logging.error("Step " + step_id + " failed in cluster: " + cluster_id) return "FAILED" if state == u'NOT_FOUND': logging.error("Step " + step_id + " could not be found in cluster: " + cluster_id) return "NOT_FOUND" if state == u'ERROR': logging.error("Step " + step_id + " produced an error in _find_step_state in cluster: " + cluster_id) return "ERROR" #Check if the state is WAITING. Then launch the next steps if state == u'COMPLETED': #Finding the master node dns of EMR cluster logging.info("Step " + step_id + " succesfully completed in cluster: " + cluster_id) return step_id def _find_step_state(self, cluster_id, step_id): try: step_summary_list = self.connection.list_steps(cluster_id) for step_summary in step_summary_list.steps: if step_summary.id == step_id: return step_summary.status.state return "NOT_FOUND" except: return "ERROR" #Method for terminating the EMR cluster def terminate_cluster(self, cluster_id): self.connection.terminate_jobflow(cluster_id)
class Rankmaniac: """ (wrapper class) This class presents a simple wrapper around the AWS SDK. It strives to provide all the functionality required to run map-reduce (Hadoop) on Amazon. This way the students do not need to worry about learning the API for Amazon S3 and EMR, and instead can focus on computing pagerank quickly! """ DefaultRegionName = 'us-west-2' DefaultRegionEndpoint = 'elasticmapreduce.us-west-2.amazonaws.com' def __init__(self, team_id, access_key, secret_key, bucket='cs144students'): """ (constructor) Creates a new instance of the Rankmaniac class for a specific team using the provided credentials. Arguments: team_id <str> the team identifier, which may be differ slightly from the actual team name. access_key <str> the AWS access key identifier. secret_key <str> the AWS secret acess key. Keyword arguments: bucket <str> the S3 bucket name. """ region = RegionInfo(None, self.DefaultRegionName, self.DefaultRegionEndpoint) self._s3_bucket = bucket self._s3_conn = S3Connection(access_key, secret_key) self._emr_conn = EmrConnection(access_key, secret_key, region=region) self.team_id = team_id self.job_id = None self._reset() self._num_instances = 1 def _reset(self): """ Resets the internal state of the job and submission. """ self._iter_no = 0 self._infile = None self._last_outdir = None self._last_process_step_iter_no = -1 self._is_done = False def __del__(self): """ (destructor) Terminates the map-reduce job if any, and closes the connections to Amazon S3 and EMR. """ if self.job_id is not None: self.terminate() self._s3_conn.close() self._emr_conn.close() def __enter__(self): """ Used for `with` syntax. Simply returns this instance since the set-up has all been done in the constructor. """ return self def __exit__(self, type, value, traceback): """ Refer to __del__(). """ self.__del__() return False # do not swallow any exceptions def upload(self, indir='data'): """ Uploads the local data to Amazon S3 under the configured bucket and key prefix (the team identifier). This way the code can be accessed by Amazon EMR to compute pagerank. Keyword arguments: indir <str> the base directory from which to upload contents. Special notes: This method only uploads **files** in the specified directory. It does not scan through subdirectories. WARNING! This method removes all previous (or ongoing) submission results, so it is unsafe to call while a job is already running (and possibly started elsewhere). """ if self.job_id is not None: raise RankmaniacError('A job is already running.') bucket = self._s3_conn.get_bucket(self._s3_bucket) # Clear out current bucket contents for team keys = bucket.list(prefix=self._get_keyname()) bucket.delete_keys(keys) for filename in os.listdir(indir): relpath = os.path.join(indir, filename) if os.path.isfile(relpath): keyname = self._get_keyname(filename) key = bucket.new_key(keyname) key.set_contents_from_filename(relpath) def set_infile(self, filename): """ Sets the data file to use for the first iteration of the pagerank step in the map-reduce job. """ if self.job_id is not None: raise RankmaniacError('A job is already running.') self._infile = filename def do_iter(self, pagerank_mapper, pagerank_reducer, process_mapper, process_reducer, pagerank_output=None, process_output=None, num_pagerank_mappers=1, num_pagerank_reducers=1): """ Adds a pagerank step and a process step to the current job. """ num_process_mappers = 1 num_process_reducers = 1 if self._iter_no == 0: pagerank_input = self._infile elif self._iter_no > 0: pagerank_input = self._last_outdir if pagerank_output is None: pagerank_output = self._get_default_outdir('pagerank') # Output from the pagerank step becomes input to process step process_input = pagerank_output if process_output is None: process_output = self._get_default_outdir('process') pagerank_step = self._make_step(pagerank_mapper, pagerank_reducer, pagerank_input, pagerank_output, num_pagerank_mappers, num_pagerank_reducers) process_step = self._make_step(process_mapper, process_reducer, process_input, process_output, num_process_mappers, num_process_reducers) steps = [pagerank_step, process_step] if self.job_id is None: self._submit_new_job(steps) else: self._emr_conn.add_jobflow_steps(self.job_id, steps) # Store `process_output` directory so it can be used in # subsequent iteration self._last_outdir = process_output self._iter_no += 1 def is_done(self): """ Returns `True` if the map-reduce job is done, and `False` otherwise. For all process-step output files that have not been fetched, gets the first part of the output file, and checks whether its contents begins with the string 'FinalRank'. Special notes: WARNING! The usage of this method in your code requires that that you used the default output directories in all calls to do_iter(). """ # Cache the result so we can return immediately without hitting # any of the Amazon APIs if self._is_done: return True iter_no = self._get_last_process_step_iter_no() if iter_no < 0: return False while self._last_process_step_iter_no < iter_no: self._last_process_step_iter_no += 1 i = self._last_process_step_iter_no outdir = self._get_default_outdir('process', iter_no=i) keyname = self._get_keyname(outdir, 'part-00000') bucket = self._s3_conn.get_bucket(self._s3_bucket) key = Key(bucket=bucket, name=keyname) contents = key.next() # get first chunk of the output file if contents.startswith('FinalRank'): self._is_done = True # cache result break return self._is_done def is_alive(self): """ Checks whether the jobflow has completed, failed, or been terminated. Special notes: WARNING! This method should only be called **after** is_done() in order to be able to distinguish between the cases where the map-reduce job has outputted 'FinalRank' on its final iteration and has a 'COMPLETED' state. """ jobflow = self.describe() if jobflow.state in ('COMPLETED', 'FAILED', 'TERMINATED'): return False return True def terminate(self): """ Terminates a running map-reduce job. """ if not self.job_id: raise RankmaniacError('No job is running.') self._emr_conn.terminate_jobflow(self.job_id) self.job_id = None self._reset() def download(self, outdir='results'): """ Downloads the results from Amazon S3 to the local directory. Keyword arguments: outdir <str> the base directory to which to download contents. Special notes: This method downloads all keys (files) from the configured bucket for this particular team. It creates subdirectories as needed. """ bucket = self._s3_conn.get_bucket(self._s3_bucket) keys = bucket.list(prefix=self._get_keyname()) for key in keys: keyname = key.name # Ignore folder keys if '$' not in keyname: suffix = keyname.split('/')[1:] # removes team identifier filename = os.path.join(outdir, *suffix) dirname = os.path.dirname(filename) if not os.path.exists(dirname): os.makedirs(dirname) key.get_contents_to_filename(filename) def describe(self): """ Gets the current map-reduce job details. Returns a boto.emr.emrobject.JobFlow object. Special notes: The JobFlow object has the following relevant fields. state <str> the state of the job flow, either COMPLETED | FAILED | TERMINATED | RUNNING | SHUTTING_DOWN | STARTING | WAITING steps <list(boto.emr.emrobject.Step)> a list of the step details in the workflow. The Step object has the following relevant fields. state <str> the state of the step. startdatetime <str> the start time of the job. enddatetime <str> the end time of the job. WARNING! Amazon has an upper-limit on the frequency with which you can call this method; we have had success with calling it at most once every 10 seconds. """ if not self.job_id: raise RankmaniacError('No job is running.') return self._emr_conn.describe_jobflow(self.job_id) def _get_last_process_step_iter_no(self): """ Returns the most recently process-step of the job flow that has been completed. """ steps = self.describe().steps i = 1 while i < len(steps): step = steps[i] if step.state != 'COMPLETED': break i += 2 return i / 2 - 1 def _get_default_outdir(self, name, iter_no=None): """ Returns the default output directory, which is 'iter_no/name/'. """ if iter_no is None: iter_no = self._iter_no # Return iter_no/name/ **with** the trailing slash return '%s/%s/' % (iter_no, name) def _submit_new_job(self, steps): """ Submits a new job to run on Amazon EMR. """ if self.job_id is not None: raise RankmaniacError('A job is already running.') job_name = self._make_name() num_instances = self._num_instances log_uri = self._get_s3_team_uri('job_logs') self.job_id = self._emr_conn.run_jobflow(name=job_name, steps=steps, num_instances=num_instances, log_uri=log_uri) def _make_step(self, mapper, reducer, input, output, num_mappers=1, num_reducers=1): """ Returns a new step that runs the specified mapper and reducer, reading from the specified input and writing to the specified output. """ bucket = self._s3_conn.get_bucket(self._s3_bucket) # Clear out current bucket/output contents for team keys = bucket.list(prefix=self._get_keyname(output)) bucket.delete_keys(keys) step_name = self._make_name() step_args = ['-jobconf', 'mapred.map.tasks=%d' % (num_mappers), '-jobconf', 'mapred.reduce.tasks=%d' % (num_reducers)] return StreamingStep(name=step_name, step_args=step_args, mapper=self._get_s3_team_uri(mapper), reducer=self._get_s3_team_uri(reducer), input=self._get_s3_team_uri(input), output=self._get_s3_team_uri(output)) def _make_name(self): return strftime('%%s %m-%d-%Y %H:%M:%S', localtime()) % (self.team_id) def _get_keyname(self, *args): """ Returns the key name to use in the grading bucket (for the particular team). 'team_id/...' """ return '%s/%s' % (self.team_id, '/'.join(args)) def _get_s3_team_uri(self, *args): """ Returns the Amazon S3 URI for the team submissions. """ return 's3n://%s/%s' % (self._s3_bucket, self._get_keyname(*args))
def create_data_source_variable(cluster_id, cr): """ Creates a data source variable .json file using the cluster_id of an EMR cluster_id @PARAM: cluster_id: ID of an EMR cluster return: True if success, creates a file in the pwd 'default_emr.json' Object created should look like: HADOOP_DATA_SOURCE_NAME="emr_data_source" HADOOP_DATA_SOURCE_DISTRO="Cloudera CDH5.4-5.7" HADOOP_DATA_SOURCE_HOST="emr_master_dns_hostname" HADOOP_DATA_SOURCE_PORT=8020 HADOOP_DATA_SOURCE_USER="******" HADOOP_DATA_SOURCE_GROUP="hadoop" HADOOP_DATA_SOURCE_JT_HOST="emr_master_dns_hostname" HADOOP_DATA_SOURCE_JT_PORT=8032 CONNECTION_PARAMETERS='[{"key":"mapreduce.jobhistory.address", "value":"0.0.0.0:10020"}, ' \ '{"key":"mapreduce.jobhistory.webapp.address", "value":"cdh5hakerberosnn.alpinenow.local:19888"}, ' \ '{"key":"yarn.app.mapreduce.am.staging-dir", "value":"/tmp/hadoop-yarn/staging"}, ' \ '{"key":"yarn.resourcemanager.admin.address", "value":"cdh5hakerberosnn.alpinenow.local:8033"}, ' \ '{"key":"yarn.resourcemanager.resource-tracker.address", "value":"cdh5hakerberosnn.alpinenow.local:8031"}, ' \ '{"key":"yarn.resourcemanager.scheduler.address", "value":"cdh5hakerberosnn.alpinenow.local:8030"}]' """ conn = EmrConnection( cr.get_config("aws_access_key"), cr.get_config("aws_secret_key"), region=RegionInfo(name=cr.get_config("aws_region"), endpoint=cr.get_config("aws_region") + ".elasticmapreduce.amazonaws.com")) emr_cluster = conn.describe_cluster(cluster_id) master_dns_hostname = emr_cluster.masterpublicdnsname # Build up connection parameters conn_params = [] conn_params.append({ "key": "mapreduce.jobhistory.address", "value": "{0}:10020".format(master_dns_hostname) }) conn_params.append({ "key": "mapreduce.jobhistory.webapp.address", "value": "{0}:19888".format(master_dns_hostname) }) conn_params.append({ "key": "yarn.app.mapreduce.am.staging-dir", "value": "/user" }) conn_params.append({ "key": "yarn.resourcemanager.admin.address", "value": "{0}:8033".format(master_dns_hostname) }) conn_params.append({ "key": "yarn.resourcemanager.scheduler.address", "value": "{0}:8030".format(master_dns_hostname) }) conn_params_str = "CONNECTION_PARAMETERS=\"{0}\"".format(conn_params) email_str = "EMAIL=\"avalanche_{0}.alpinenow.com\"".format( random.randint(1, 99999)) with open("emr_default.conf", "w") as f: f.writelines("HADOOP_DATA_SOURCE_NAME=\"{0}\"\n".format( cr.get_config("emr_cluster_name"))) f.writelines( "HADOOP_DATA_SOURCE_DISTRO=\"{0}\"\n".format("Amazon EMR5")) f.writelines( "HADOOP_DATA_SOURCE_HOST=\"{0}\"\n".format(master_dns_hostname)) f.writelines("HADOOP_DATA_SOURCE_POST=\"8020\"\n") f.writelines("HADOOP_DATA_SOURCE_USER=\"hdfs\"\n") f.writelines("HADOOP_DATA_SOURCE_GROUP=\"hadoop\"\n") f.writelines( "HADOOP_DATA_SOURCE_JT_HOST=\"{0}\"\n".format(master_dns_hostname)) f.writelines("HADOOP_DATA_SOURCE_JT_PORT=\"8032\"\n") f.writelines(email_str) f.writelines(conn_params_str)
from boto.emr.connection import EmrConnection # Description: # EmrConnection can be used to create a new emr job # initialize emr connection conn = EmrConnection("<aws-access-key-id>", "<aws-secret-access-key>") # run job flow with 10 instances conn.run_jobflow(num_instances=10, master_instance_type="m1.small", slave_instance_type="m1.small")
class EMRCluster(object): '''Representation of an EMR cluster. TODO: add bridge to boto interface for unit test. ''' emr_status_delay = 10 # in sec emr_status_max_delay = 60 # in sec emr_status_max_error = 30 # number of errors emr_max_idle = 10 * 60 # 10 min (in sec) rate_limit_lock = RateLimitLock() def __init__(self, prop): '''Constructor, initialize EMR connection.''' self.prop = prop self.conn = EmrConnection(self.prop.ec2.key, self.prop.ec2.secret) self.jobid = None self.retry = 0 self.level = 0 self.last_update = -1 @property def priority(self): '''The priority used in EMRManager. The lower value, the higher priority. ''' with EMRCluster.rate_limit_lock: if self.jobid is None: return 1 return 0 def get_instance_groups(self): '''Get instance groups to start a cluster. It calculates the price with self.level, which indicates the price upgrades from the original price. ''' instance_groups = [] for group in self.prop.emr.instance_groups: (num, group_name, instance_type) = group level = max(0, min(self.level, len(self.prop.emr.price_upgrade_rate) - 1)) # 0 <= level < len(...) bprice = self.prop.emr.prices[instance_type] * self.prop.emr.price_upgrade_rate[level] name = '%s-%s@%f' % (group_name, 'SPOT', bprice) # Use on-demand instance if prices are zero. if bprice > 0: ig = InstanceGroup(num, group_name, instance_type, 'SPOT', name, '%.3f' % bprice) else: ig = InstanceGroup(num, group_name, instance_type, 'ON_DEMAND', name) instance_groups.append(ig) return instance_groups def get_bootstrap_actions(self): '''Get list of bootstrap actions from property''' actions = [] for bootstrap_action in self.prop.emr.bootstrap_actions: assert len(bootstrap_action) >= 2, 'Wrong bootstrap action definition: ' + str(bootstrap_action) actions.append(BootstrapAction(bootstrap_action[0], bootstrap_action[1], bootstrap_action[2:])) return actions @synchronized def start(self): '''Start a EMR cluster.''' # emr.project_name is required if self.prop.emr.project_name is None: raise ValueError('emr.project_name is not set') self.last_update = time.time() with EMRCluster.rate_limit_lock: self.jobid = self.conn.run_jobflow(name=self.prop.emr.cluster_name, ec2_keyname=self.prop.emr.keyname, log_uri=self.prop.emr.log_uri, ami_version=self.prop.emr.ami_version, bootstrap_actions=self.get_bootstrap_actions(), keep_alive=True, action_on_failure='CONTINUE', api_params={'VisibleToAllUsers': 'true'}, instance_groups=self.get_instance_groups()) message('Job flow created: %s', self.jobid) # Tag EC2 instances to allow future analysis tags = {'FlowControl': 'Briefly', 'Project': self.prop.emr.project_name} if self.prop.emr.tags is not None: assert isinstance(self.prop.emr.tags, dict) tags = dict(tags.items() + self.prop.emr.tags.items()) self.conn.add_tags(self.jobid, tags) @synchronized def terminate(self, level_upgrade=0): '''Terminate this EMR cluster.''' if self.jobid is None: return self.level += level_upgrade # upgrade to another price level message('Terminate jobflow: %s', self.jobid) for i in xrange(3): try: with EMRCluster.rate_limit_lock: self.conn.terminate_jobflow(self.jobid) break except Exception, e: message('Unable to terminate job flow: %s', self.jobid) message(traceback.format_exc()) # We have to set jobid as None to create new cluster; # otherwise, run_steps will keep launching jobs on the bad cluster. self.jobid = None
def create_emr_cluster(cr): """ @PARAM: Cluster configuration reader object Creates an EMR cluster given a set of configuration parameters Return: EMR Cluster ID """ #region = cr.get_config("aws_region") #conn = boto.emr.connect_to_region(region) conn = EmrConnection( cr.get_config("aws_access_key"), cr.get_config("aws_secret_key"), region=RegionInfo(name=cr.get_config("aws_region"), endpoint=cr.get_config("aws_region") + ".elasticmapreduce.amazonaws.com")) # Create list of instance groups: master, core, and task instance_groups = [] instance_groups.append( InstanceGroup(num_instances=cr.get_config("emr_master_node_count"), role="MASTER", type=cr.get_config("emr_master_node_type"), market=cr.get_config("emr_market_type"), name="Master Node")) instance_groups.append( InstanceGroup(num_instances=cr.get_config("emr_core_node_count"), role="CORE", type=cr.get_config("emr_core_node_type"), market=cr.get_config("emr_market_type"), name="Core Node")) # Only create task nodes if specifcally asked for if cr.get_config("emr_task_node_count") > 0: instance_groups.append( InstanceGroup(num_instances=cr.get_config("emr_task_node_count"), role="TASK", type=cr.get_config("emr_task_node_type"), market=cr.get_config("emr_market_type"), name="Task Node")) print "Creating EMR Cluster with instance groups: {0}".format( instance_groups) # Use these params to add overrrides, these will go away in Boto3 api_params = { "Instances.Ec2SubnetId": cr.get_config("aws_subnet_id"), "ReleaseLabel": cr.get_config("emr_version") } # Add step to load data step_args = [ "s3-dist-cp", "--s3Endpoint=s3-us-west-1.amazonaws.com", "--src=s3://alpine-qa/automation/automation_test_data/", "--dest=hdfs:///automation_test_data", "--srcPattern=.*[a-zA-Z,]+" ] step = JarStep(name="s3distcp for data loading", jar="command-runner.jar", step_args=step_args, action_on_failure="CONTINUE") cluster_id = conn.run_jobflow( cr.get_config("emr_cluster_name"), instance_groups=instance_groups, action_on_failure="TERMINATE_JOB_FLOW", keep_alive=True, enable_debugging=True, log_uri=cr.get_config("emr_log_uri"), #hadoop_version = "Amazon 2.7.2", #ReleaseLabel = "emr-5.0.0", #ami_version = "5.0.0", steps=[step], bootstrap_actions=[], ec2_keyname=cr.get_config("ec2_keyname"), visible_to_all_users=True, job_flow_role="EMR_EC2_DefaultRole", service_role="EMR_DefaultRole", api_params=api_params) print "EMR Cluster created, cluster id: {0}".format(cluster_id) state = conn.describe_cluster(cluster_id).status.state while state != u'COMPLETED' and state != u'SHUTTING_DOWN' and state != u'FAILED' and state != u'WAITING': #sleeping to recheck for status. time.sleep(5) state = conn.describe_cluster(cluster_id).status.state print "State is: {0}, sleeping 5s...".format(state) if state == u'SHUTTING_DOWN' or state == u'FAILED': return "ERROR" #Check if the state is WAITING. Then launch the next steps if state == u'WAITING': #Finding the master node dns of EMR cluster master_dns = conn.describe_cluster(cluster_id).masterpublicdnsname print "DNS Name: {0}".format(master_dns) return cluster_id
from boto.emr.connection import EmrConnection from boto.emr.step import StreamingStep import boto AWS_KEY='AKIAIQ7VG4UORIN75ZSA' AWS_SECRET='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' conn = EmrConnection(AWS_KEY, AWS_SECRET) filelist="""split_!.txt split_".txt split_$.txt split_%.txt split_&.txt split_'.txt split_(.txt split_).txt split_*.txt split_+.txt split_,.txt split_-.txt split_0.txt split_1.txt split_2.txt split_3.txt split_4.txt split_5.txt split_6.txt split_7.txt split_8.txt split_9.txt
class EmrHiveRuntime(HiveRuntime): def __init__(self, spec_filename="spec.json"): import boto from boto.emr.connection import EmrConnection, RegionInfo super(HiveRuntime, self).__init__(spec_filename) p = self.settings.Param self.s3_conn = boto.connect_s3(p.AWS_ACCESS_KEY_ID, p.AWS_ACCESS_KEY_SECRET) self.s3_bucket = self.s3_conn.get_bucket(p.S3_BUCKET) self.region = p.AWS_Region self.emr_conn = EmrConnection(p.AWS_ACCESS_KEY_ID, p.AWS_ACCESS_KEY_SECRET, region = RegionInfo(name = self.region, endpoint = self.region + '.elasticmapreduce.amazonaws.com')) self.job_flow_id = p.EMR_jobFlowId def get_s3_working_dir(self, path=""): ps = self.settings glb_vars = ps.GlobalParam return os.path.join('zetjob', glb_vars['userName'], "job%s" % glb_vars['jobId'], "blk%s" % glb_vars['blockId'], path) def get_emr_job_name(self): ps = self.settings glb_vars = ps.GlobalParam return os.path.join('zetjob', glb_vars['userName'], "job%s" % glb_vars['jobId'], "blk%s" % glb_vars['blockId']) def s3_upload_dir(self, local_dir): print("EmrHiveRuntime.s3_uploader()") print("s3_upload_dir :::: %s" % local_dir) s3_upload_dir = self.get_s3_working_dir(local_dir) ext_files = [f for f in sorted(os.listdir(local_dir)) if os.path.isfile(os.path.join(local_dir,f))] for f in ext_files: f_local = os.path.join(local_dir, f) f_remote = os.path.join(s3_upload_dir, local_dir, f) f_remote_full = os.path.join("s3://", self.s3_bucket.name, f_remote) print("S3 Upload :: %s ====> %s" % (f_local, s3_upload_dir)) print("S3 remote_full :: %s" % f_remote_full) yield s3_upload(self.s3_bucket, f_remote, f_local) def files_uploader(self, local_dir): return self.s3_upload_dir(local_dir) def clean_s3_working_dir(self): s3_working_dir = self.get_s3_working_dir() if not s3_delete(self.s3_bucket, s3_working_dir): # TODO : refactor to 'HiveException' raise Exception("Can not clean s3 path : %s" % s3_working_dir) def clean_working_dir(self): self.clean_s3_working_dir() def emr_execute_hive(self, s3_hive_script): from boto.emr.step import HiveStep hive_step = HiveStep(name=self.get_emr_job_name(), hive_file=s3_hive_script) self.emr_conn.add_jobflow_steps(self.job_flow_id, steps=[hive_step]) emr_wait_job(self.emr_conn, self.job_flow_id) def execute(self, main_hive_script, generated_hive_script=None): self.clean_working_dir() hive_script_local = self.generate_script(main_hive_script, generated_hive_script) s3_working_dir = self.get_s3_working_dir() hive_script_remote = os.path.join(s3_working_dir, os.path.basename(hive_script_local)) hive_script_remote_full = s3_upload(self.s3_bucket, hive_script_remote, hive_script_local) print(hive_script_remote_full) print("EmrHiveRuntime.execute()") self.emr_execute_hive(hive_script_remote_full)
from boto.emr.connection import EmrConnection from boto.emr.step import StreamingStep import boto AWS_KEY='AKIAIQ7VG4UORIN75ZSA' AWS_SECRET='jzxajGx8gzwX+ymYXJ0/5heCjkPtWLQkICYRn7Vj' conn = EmrConnection(AWS_KEY, AWS_SECRET) step = StreamingStep(name='My wordcount example', mapper='s3n://css739/wordcount/bigramSplitter.py', reducer='aggregate', input='s3n://smalldata/wikipedia_titles.txt', output='s3n://css739/wordcount/bigram_count_output2', cache_files=['s3n://css739/wordcount/english_stoplist.py']) jobid = conn.run_jobflow(name='My jobflow', log_uri='s3n://css739/wordcount/jobflow_logs',steps=[step]) conn.describe_jobflow(jobid).state
# the nodes of an EMR(Elastic Map Reduce) job. # build up our instance groups namenode_instance_group = InstanceGroup(num_instances=1, role="MASTER", type="c1.xlarge", market="ON_DEMAND", name="MASTER_GROUP") core_nodes = InstanceGroup(num_instances=20, role="MASTER", type="c1.xlarge", market="SPOT", name="MASTER_GROUP") task_nodes = InstanceGroup(num_instances=10, role="TASK", type="c1.xlarge", market="ON_DEMAND", name="INITIAL_TASK_GROUP") instance_groups = [namenode_instance_group, core_nodes, task_nodes] # run the job conn = EmrConnection("<aws-access-key-id>", "<aws-secret-access-key>") conn.run_jobflow(name="My Job Flow", instance_groups=instance_groups)
from boto.emr.bootstrap_action import BootstrapAction from boto.emr.connection import EmrConnection # Description: # BootstrapAction is an object reperesenting a bootstrap action in Elastic Map # Reduce (EMR), a script that gets run before the EMR job executes. # initialize a bootstrap action bootstrapSetup = BootstrapAction("Bootstrap Name", "s3://<my-bucket>/<my-bootstrap-action>", ["arg1=hello", "arg2=world"]) # initialize emr connection emr_job = EmrConnection("<aws-access-key-id>", "<aws-secret-access-key>") # run emr job flow with defined bootstrap action emr_job.run_jobflow(bootstrap_actions=[bootstrapSetup])
k.set_contents_from_filename( "/Users/winteram/Documents/Teaching/WebAnalytics_2013S/BIA660-2013S/course_docs/20130319/mapper.py" ) k.close() k = Key(b) k.key = "reducer.py" k.set_contents_from_filename( "/Users/winteram/Documents/Teaching/WebAnalytics_2013S/BIA660-2013S/course_docs/20130319/reducer.py" ) k.close() ### Running code with EMR # emrcon = EmrConnection('<aws access key>', '<aws secret key>') emrcon = EmrConnection("0CY3BC386720ZYZNWZ02", "Jv37SHb/XNeqpY8vMrGeclcL6abfKHKd9Eeh5fmy") step = StreamingStep( name="Alcohol Step", mapper="s3n://bia660-winter/mapper.py", reducer="s3n://bia660-winter/reducer.py", input="s3://datasets.elasticmapreduce/ngrams/books/20090715/eng-us-all/3gram/data", output="s3n://bia660-winter/output/alcohol_religion", ) jobid = emrcon.run_jobflow( name="Alcohol Religion 10", log_uri="s3://bia660-winter/logfiles", steps=[step], num_instances=4 ) print "Job created: %s" % jobid status = emrcon.describe_jobflow(jobid)
#add your amazon creds to bash environment following the below s3_bkt = os.environ['S3_BKT'] aws_access_key = os.environ['AWSAccessKeyId'] aws_secret_key = os.environ['AWSSecretKeyId'] def status_check(emr_conn, jobid): status = 0 while status not in ['COMPLETED', 'FAILED', 'TERMINATED' ]: status = emr_conn.describe_jobflow(jobid).state print 'running %s: state is %s' % (jobid, status) time.sleep(30) if __name__ == '__main__': #connect to s3 and emr emr_conn = EmrConnection(aws_access_key, aws_secret_key) s3_conn = S3Connection(aws_access_key, aws_secret_key) #upload mapper bucket = s3_conn.create_bucket(s3_bkt) k = Key(bucket) k.key = 'mapper.py' k.set_contents_from_filename('mapper.py') #where data comes from mapper_uri = 's3n://%s/mapper.py' % (s3_bkt) output_uri = 's3n://%s/output' % (s3_bkt) log_uri = 's3n://%s/log' % (s3_bkt) #configure the step wc_step = StreamingStep(name='My Hello World Count',
def post(self): if not boto.config.has_section('Boto'): boto.config.add_section('Boto') boto.config.set('Boto', 'https_validate_certificates', 'False') note = '' data_para = [0, 0, 0, 0, 0] s3_connection = S3Connection(access_id, access_key) bucket = s3_connection.get_bucket('bucket774') k = Key(bucket) k.key = 'temp_para.json' temp_para = json.loads(k.get_contents_as_string()) if (temp_para[6] == 1): k.key = 'cluster_id' cluster_id = k.get_contents_as_string() conn = EmrConnection(access_id, access_key) if (temp_para[7] == 0): status = conn.describe_cluster(cluster_id) if (status.status.state == 'WAITING'): PYdata = get_output() conn.terminate_jobflow(cluster_id) data = in_circle_to_pi(PYdata, temp_para[0]) k.key = 'temp_para.json' temp_para[6] = 0 k.set_contents_from_string(json.dumps(temp_para)) data_para[0:4] = temp_para[0:4] data_para[4] = json.loads(data)[-1] note = 'last emr job done, reslut have been updated' save_result(data, json.dumps(data_para)) else: note = 'last emr calculation havet finished,please waitting.' k.key = 'record.json' data = k.get_contents_as_string() k.key = 'record_para.json' data_para_json = k.get_contents_as_string() data_para = json.loads(data_para_json) elif (temp_para[7] == 1): status = conn.describe_cluster(cluster_id) if (status.status.state == 'WAITING'): k.key = 'temp_data.json' PYdata = np.array(json.loads(k.get_contents_as_string())) PYdata += get_output() if (round( np.sum(PYdata) / (temp_para[3] * temp_para[5]), temp_para[4]) == round(math.pi, temp_para[4])): for i in range(1, len(PYdata)): PYdata[i] += PYdata[i - 1] PYdata[i - 1] /= temp_para[0] * (i) * temp_para[5] PYdata[len(PYdata) - 1] /= temp_para[0] * len(PYdata) * temp_para[5] data = json.dumps( PYdata.tolist()) #covernt numpy array to list k.key = 'temp_para.json' temp_para[6] = 0 k.set_contents_from_string(json.dumps(temp_para)) data_para[0:4] = temp_para[0:4] data_para[4] = json.loads(data)[-1] conn.terminate_jobflow(cluster_id) note = 'last emr job done,result have been updated' save_result(data, json.dumps(data_para)) else: note = str(np.sum(PYdata)) + ',' + str( temp_para[3]) + ',' + str(temp_para[5]) add_step_emr(conn, cluster_id) save_temp_result(PYdata) for key in bucket.list(prefix='output/'): key.delete() temp_para[5] += 1 k.key = 'temp_para.json' k.set_contents_from_string(json.dumps(temp_para)) #note='havet find the given accuracy in last run, keep working' k.key = 'record.json' data = k.get_contents_as_string() k.key = 'record_para.json' data_para_json = k.get_contents_as_string() data_para = json.loads(data_para_json) else: note = 'last emr calculation havet finished,please waitting.' k.key = 'record.json' data = k.get_contents_as_string() k.key = 'record_para.json' data_para_json = k.get_contents_as_string() data_para = json.loads(data_para_json) else: k.key = 'record.json' data = k.get_contents_as_string() k.key = 'record_para.json' data_para_json = k.get_contents_as_string() data_para = json.loads(data_para_json) doRender( self, 'chart.htm', { 'Data': data, 'shots_each_threat': data_para[0], 'R': data_para[1], 'Q': data_para[2], 'pi': math.pi, 'shots': data_para[3], 'result': data_para[4], 'note': note })