How to use get_lambda_metrics method in localstack

Best Python code snippet using localstack_python

test_integration.py

Source:test_integration.py

...143    if len(EVENTS) != num_events:144        LOGGER.warning('DynamoDB and Kinesis updates retrieved (actual/expected): %s/%s' % (len(EVENTS), num_events))145    assert len(EVENTS) == num_events146    # check cloudwatch notifications147    stats1 = get_lambda_metrics(TEST_LAMBDA_NAME_STREAM)148    assert len(stats1['Datapoints']) == 2 + num_events_sns149    stats2 = get_lambda_metrics(TEST_LAMBDA_NAME_STREAM, 'Errors')150    assert len(stats2['Datapoints']) == 1151    stats3 = get_lambda_metrics(TEST_LAMBDA_NAME_DDB)152    assert len(stats3['Datapoints']) == 10153def test_kinesis_lambda_forward_chain():154    kinesis = aws_stack.connect_to_service('kinesis')155    s3 = aws_stack.connect_to_service('s3')156    aws_stack.create_kinesis_stream(TEST_CHAIN_STREAM1_NAME, delete=True)157    aws_stack.create_kinesis_stream(TEST_CHAIN_STREAM2_NAME, delete=True)158    s3.create_bucket(Bucket=TEST_BUCKET_NAME)159    # deploy test lambdas connected to Kinesis streams160    zip_file = testutil.create_lambda_archive(load_file(TEST_LAMBDA_PYTHON), get_content=True,161        libs=TEST_LAMBDA_LIBS, runtime=LAMBDA_RUNTIME_PYTHON27)162    testutil.create_lambda_function(func_name=TEST_CHAIN_LAMBDA1_NAME, zip_file=zip_file,163        event_source_arn=get_event_source_arn(TEST_CHAIN_STREAM1_NAME), runtime=LAMBDA_RUNTIME_PYTHON27)164    testutil.create_lambda_function(func_name=TEST_CHAIN_LAMBDA2_NAME, zip_file=zip_file,165        event_source_arn=get_event_source_arn(TEST_CHAIN_STREAM2_NAME), runtime=LAMBDA_RUNTIME_PYTHON27)166    # publish test record167    test_data = {'test_data': 'forward_chain_data_%s' % short_uid()}168    data = clone(test_data)169    data[lambda_integration.MSG_BODY_MESSAGE_TARGET] = 'kinesis:%s' % TEST_CHAIN_STREAM2_NAME170    kinesis.put_record(Data=to_bytes(json.dumps(data)), PartitionKey='testId', StreamName=TEST_CHAIN_STREAM1_NAME)171    # check results172    time.sleep(5)173    all_objects = testutil.list_all_s3_objects()174    testutil.assert_objects(test_data, all_objects)175# ---------------176# HELPER METHODS177# ---------------178def get_event_source_arn(stream_name):179    kinesis = aws_stack.connect_to_service('kinesis')180    return kinesis.describe_stream(StreamName=stream_name)['StreamDescription']['StreamARN']181def get_lambda_metrics(func_name, metric='Invocations'):182    return cloudwatch_util.get_metric_statistics(183        Namespace='AWS/Lambda',184        MetricName=metric,185        Dimensions=[{'Name': 'FunctionName', 'Value': func_name}],186        Period=60,187        StartTime=datetime.now() - timedelta(minutes=1),188        EndTime=datetime.now(),189        Statistics=['Sum']...

aws_lambda_metrics.py

Source:aws_lambda_metrics.py

...44aws_secret_access_key = config.get('xx', 'aws_secret_access_key')45aws_session_token = config.get('xx', 'aws_session_token')46CW_WINDOW = 5 # Min47PERIOD = 300 # Secs48def get_lambda_metrics(function_name,REGION_NAME):49    influx_client = InfluxDBClient(host='localhost', port=8086, database='telegraf')50    client = boto3.client('cloudwatch', region_name=REGION_NAME, aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, aws_session_token=aws_session_token)51    metrics = ['Invocations','Errors','Dead Letter Error','Duration','Throttles','IteratorAge','ConcurrentExecutions','UnreservedConcurrentExecutions']52    for metric in metrics:53        response = client.get_metric_statistics(54            Namespace = 'AWS/Lambda',55            MetricName = metric,56            Dimensions = [57                {58                    'Name': 'FunctionName',59                    'Value': function_name60                },61            ],62            StartTime = datetime.utcnow() - timedelta(minutes=CW_WINDOW),63            EndTime = datetime.utcnow(),64            Period = PERIOD,65            Statistics = [66                'Maximum',67                'Minimum',68                'Average',69                'Sum',70                'SampleCount'71            ],72        )73       74        recs = len(response['Datapoints'])75        #pprint.pprint(response, indent=2)76        count = 077        if recs > 0:78            while count < recs:79                aws_avg = response['Datapoints'][count]['Average']80                aws_max = response['Datapoints'][count]['Maximum']81                aws_min = response['Datapoints'][count]['Minimum']82                aws_sum = response['Datapoints'][count]['Sum']83                aws_smp = response['Datapoints'][count]['SampleCount']84                aws_tsp = response['Datapoints'][count]['Timestamp']85                aws_unt = response['Datapoints'][count]['Unit']86                #print line87                if aws_tsp is not None:88                    pattern = '%Y-%m-%d %H:%M:%S+00:00'89                    aws_tsp = str(int(time.mktime(time.strptime(str(aws_tsp), pattern))))                90                    line = function_name + ',' + metric + ',' + aws_unt + ' ' + 'minimum=' + str(aws_min) + ',' + 'maximum=' + str(aws_max) + ',' + 'average=' + str(aws_avg) + 'sum=' + str(aws_sum) + ',' + 'sample_count=' + str(aws_smp) + ' ' + aws_tsp + '000000000'91                    data = [92                        {93                        "measurement": "LambdaMetrics",94                        "tags": {95                                    "name": function_name,96                                    "unit": aws_unt,97                                    "env": "qa",98                                    "region": REGION_NAME,99                                    "metric": metric,100                                },101                        "time": str(aws_tsp),102                        "fields": 103                                {104                                    "minimum": float(aws_min),105                                    "maximum": float(aws_max),106                                    "average": float(aws_avg),107                                    "sum": float(aws_sum),108                                    "sample": float(aws_smp)109                                }110                            }111                        ]112                    print line113                    print data114                    #time.sleep(1)115                    #influx_client.write_points(data)116                    #print line117                    logger.info('Sending: %s', line)118                count += 1119    return None120#@app.periodic_task(run_every=timedelta(seconds=60))121@app.task122def main():123    REGIONS = ['us-east-1','us-west-2']124    for REGION in REGIONS:125        logger.info('Region: %s', REGION)126        lambda_client = boto3.client('lambda', aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key,aws_session_token=aws_session_token,region_name=REGION)127        funcs = lambda_client.list_functions()128        #pprint.pprint(funcs)129        count = 0130        total_fcn = len(funcs['Functions'])131    for key, value in funcs.items():132        if key == 'Functions':133            while count < total_fcn:134                #print 'Function Name: %s Description: %s | Runtime: %s | MemorySize: %s' %(value[count]['FunctionName'],value[count]['Description'],value[count]['Runtime'],value[count]['MemorySize'])135                #print value[count]['FunctionName']136                if re.findall(r'h2o',value[count]['FunctionName'],re.IGNORECASE):137                    logger.info('Invoking get_lambda_metrics for %s', value[count]['FunctionName'])138                    get_lambda_metrics(value[count]['FunctionName'],REGION)139                count += 1140if __name__=='__main__':...

verify_lambda.py

Source:verify_lambda.py

...11    }12    if 'AWS_SESSION_TOKEN' in os.environ:13        options['aws_session_token'] = os.environ['AWS_SESSION_TOKEN']14    return boto3.client(service, **options)15def get_lambda_metrics(metric):16    lambda_name = os.environ['LAMBDA']17    metrics = cw.get_metric_statistics(18        Namespace='AWS/Lambda',19        MetricName=metric,20        Dimensions=[{'Name': 'FunctionName', 'Value': lambda_name}],21        Period=60,22        Statistics=['Sum'],23        StartTime=datetime.today() - timedelta(hours=1),24        EndTime=datetime.today(),25    )26    return sum([x['Sum'] for x in metrics['Datapoints']])27def check_instance(instance, launch_config):28    return all([29        launch_config == instance.get('LaunchConfigurationName'),30        instance['HealthStatus'] == 'Healthy',31        instance['LifecycleState'] == 'InService'32    ])33autoscaling = get_client('autoscaling')34cw = get_client('cloudwatch')35print("Checking Autoscaling Group instances...\n")36asg_name = os.environ['ASG']37endtime = datetime.today() + timedelta(minutes=10)38while datetime.today() < endtime:39    response = autoscaling.describe_auto_scaling_groups(AutoScalingGroupNames=[asg_name])40    asg = response['AutoScalingGroups'][0]41    instance_count = len([x for x in asg['Instances'] if check_instance(x, asg['LaunchConfigurationName'])])42    if instance_count == asg['DesiredCapacity']:43        print("{} instances updated and healthy.  Proceeding to invocation checks...\n".format(instance_count))44        break45    print("{} of {} instances updated and healthy.  Timeout in {}".format(instance_count, asg['DesiredCapacity'],46                                                                          endtime - datetime.today()))47    time.sleep(15)48else:49    sys.exit("Error: Timed out waiting for ASG instances to update")50lambda_errors = get_lambda_metrics('Errors')51lambda_invocations = get_lambda_metrics('Invocations')52print("{} out of {} invocations generated errors\n".format(lambda_errors, lambda_invocations))53if lambda_errors > 0:54    sys.exit("Error: Lambda function encountered errors.")55else:...

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