This solution is how to succeed in macOS Catalina 10.15.3 (19D76).
Dell TB16 Dock on Macbook Pro Thunderbolt does not work (macOS = 10.15.x)
Nice meet you and this page is @tomjyoutwit's update version!! (https://superuser.com/a/1354399/1147451)
Please follow this manual process carefully. It worked for me but i'm yet to get the display(s) working, but you may have more luck using the other display outputs on the dock.
| 부록 A Zeppelin Open Source 기여 방법 | |
| 나에게 필요한 기능 구현 또는 버그를 개선하고 싶다면 - jira 확인 | |
| [이슈검색 하기] | |
| 오래전에 fork 해두었다면, 여기 참조 http://www.notforme.kr/archives/1631 | |
| + jira 이슈 고르기 (쉬운 것부터) | |
| 기능개발 또는 버그픽스 |
| def dotProduct(vector: Array[Int], matrix: Array[Array[Int]]): Array[Int] = { | |
| // ignore dimensionality checks for simplicity of example | |
| (0 to (matrix(0).size - 1)).toArray.map( colIdx => { | |
| val colVec: Array[Int] = matrix.map( rowVec => rowVec(colIdx) ) | |
| val elemWiseProd: Array[Int] = (vector zip colVec).map( entryTuple => entryTuple._1 * entryTuple._2 ) | |
| elemWiseProd.sum | |
| } ) | |
| } | |
| import spark.SparkContext | |
| import SparkContext._ | |
| /** | |
| * A port of [[http://blog.echen.me/2012/02/09/movie-recommendations-and-more-via-mapreduce-and-scalding/]] | |
| * to Spark. | |
| * Uses movie ratings data from MovieLens 100k dataset found at [[http://www.grouplens.org/node/73]] | |
| */ | |
| object MovieSimilarities { |
| 개발서버에 Redis 를 구성해보자 | |
| Redis Cluster가 나온지 어언 1년이 넘었다. | |
| 이제 사용할만하다는 소문이 들린다. | |
| 구성 참조 : https://www.linode.com/docs/applications/big-data/how-to-install-and-configure-a-redis-cluster-on-ubuntu-1604 | |
| (Ubuntu 버전이지만, 그래도 괜찮아~) | |
| http://redisgate.com/redis/configuration/persistence.php (한국어) | |
| http://intro2libsys.com/focused-redis-topics/day-one/intro-redis-cluster |
추천시스템을 제공하기위해서 대규모 데이터를 핸들링하기 위한 RDBMS가 필요했습니다. (이 글을 보시는 분들께,,, 가급적 Parquet 또는 다른 하둡 에코를 이용하시는게 정신건강에 이롭습니다.)
찾아본 솔루션은 다음과 같습니다.
| #!/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 |
| #!/bin/sh | |
| BOOT2DOCKER_CERTS_DIR=/var/lib/boot2docker/certs | |
| CERTS_DIR=/etc/ssl/certs | |
| CAFILE=${CERTS_DIR}/ca-certificates.crt | |
| for cert in $(/bin/ls -1 ${BOOT2DOCKER_CERTS_DIR}); do | |
| SRC_CERT_FILE=${BOOT2DOCKER_CERTS_DIR}/${cert} | |
| CERT_FILE=${CERTS_DIR}/${cert} | |
| HASH_FILE=${CERTS_DIR}/$(/usr/local/bin/openssl x509 -noout -hash -in ${SRC_CERT_FILE} 2>/dev/null) |
| [rhc client tool 설치] | |
| https://www.openshift.com/developers/rhc-client-tools-install | |
| [ssh public key 생성] | |
| http://git-scm.com/book/ko/Git-%EC%84%9C%EB%B2%84-SSH-%EA%B3%B5%EA%B0%9C%ED%82%A4-%EB%A7%8C%EB%93%A4%EA%B8%B0 | |
| https://www.openshift.com/developers/remote-access#keys | |
| [Application 생성/설치] | |
| https://github.com/openshift/jbossas-mongoDB-quickstart |