export AWS_DEFAULT_REGION=ap-northeast-1
aws logs describe-log-groups --query 'logGroups[*].logGroupName' --output table | \
awk '{print $2}' | grep -v ^$ | while read x; do echo "deleting $x" ; aws logs delete-log-group --log-group-name $x; done
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#!/usr/bin/env python3 | |
from aws_cdk import ( | |
core as cdk_core, | |
aws_s3 as cdk_s3, | |
aws_s3_deployment as cdk_s3_deploy | |
) | |
class FrontendStack(cdk_core.Stack): | |
def __init__(self, app, id, **kwargs): |
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a4b.amazonaws.com | |
access-analyzer.amazonaws.com | |
account.amazonaws.com | |
acm-pca.amazonaws.com | |
acm.amazonaws.com | |
airflow-env.amazonaws.com | |
airflow.amazonaws.com | |
alexa-appkit.amazon.com | |
alexa-connectedhome.amazon.com | |
amazonmq.amazonaws.com |
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#!/bin/bash | |
# Minimum TODOs on a per job basis: | |
# 1. define name, application jar path, main class, queue and log4j-yarn.properties path | |
# 2. remove properties not applicable to your Spark version (Spark 1.x vs. Spark 2.x) | |
# 3. tweak num_executors, executor_memory (+ overhead), and backpressure settings | |
# the two most important settings: | |
num_executors=6 | |
executor_memory=3g |
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Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |