小编Chr*_*now的帖子

一些对查询SQL数据库有很好了解的开发人员很难在Cloudant/CouchDB中实现等效的查询模式.

这些开发人员如何将他们的SQL知识转换为Cloudant/CouchDB？

当我跑:COMPOSE_PROJECT_NAME=zk_test docker-compose up我说错了

"错误:在文件'./docker-compose.yml'中,服务必须是映射,而不是NoneType."

这是我的yml文件:

version: '2'
services:
zoo1:
image: zookeeper
restart: always
container_name: zoo1
ports:
- "2181:2181"
environment:
ZOO_MY_ID: 1
ZOO_SERVERS: server.1=zoo1:2888:3888 server.2=zoo2:2888:3888 server.3=zoo3:2888:3888

zoo2:
image: zookeeper
restart: always
container_name: zoo2
ports:
- "2182:2181"
environment:
ZOO_MY_ID: 2
ZOO_SERVERS: server.1=zoo1:2888:3888 server.2=zoo2:2888:3888 server.3=zoo3:2888:3888

zoo3:
image: zookeeper
restart: always
container_name: zoo3
ports:
- "2183:2181"
environment:
ZOO_MY_ID: 3
ZOO_SERVERS: server.1=zoo1:2888:3888 server.2=zoo2:2888:3888 server.3=zoo3:2888:3888

示例图片:

我有一个类定义,需要build-essential包:

class erlang($version = '17.3') {

  package { "build-essential": 
    ensure => installed
  }
  ...
}

不同模块中的另一个类也需要build-essential包:

class icu {

  package { "build-essential": 
    ensure => installed
  }
  ...
}

但是,当我尝试执行puppet apply时,我收到的错误是:

Error: Duplicate declaration: Package[build-essential] is already declared in file /vagrant/modules/erlang/manifests/init.pp:18; cannot redeclare at /vagrant/modules/libicu/manifests/init.pp:17 on node vagrant-ubuntu-trusty-64.home

我期望类封装他们使用的资源,但似乎并非如此？我该如何解决这个冲突？

我设法在一个ubuntu流浪盒中设置我的Symfony2项目.但是通过它的网络服务器加载网站大约需要20秒.经过一番研究,我想出了使用nfs作为同步文件夹.这是我在Vagrantfile中的设置:

config.vm.network "private_network", ip: "192.168.56.101"
config.vm.synced_folder ".", "/vagrant", :nfs => true, :mount_options => ["dmode=777","fmode=777"]

启动de vagrant box后,我收到以下错误

==> default: Mounting NFS shared folders...
The following SSH command responded with a non-zero exit status.
Vagrant assumes that this means the command failed!

mount -o 'dmode=777,fmode=777' 192.168.56.1:'/Users/marcschenk/Projects/teleboy.ch' /vagrant

Stdout from the command:

Stderr from the command:

stdin: is not a tty
mount.nfs: an incorrect mount option was specified

VM似乎工作,但同步文件夹显然是空的.我错了什么？

我的设置:

• Vagrant 1.6.5&Virtualbox 4.3.18
• 主持人:OS X 10.10
• 嘉宾:Ubuntu 12.04

我已经建立了一个小型Hadoop集群进行测试.使用NameNode(1台机器),SecondaryNameNode(1)和所有DataNodes(3),安装程序运行良好.这些机器被命名为"master","secondary"和"data01","data02"和"data03".所有DNS都已正确设置,无密码SSH已从主/备用配置到所有计算机并返回.

我使用了格式化集群bin/hadoop namenode -format,然后启动所有服务bin/start-all.sh.检查所有节点上的所有进程是否已启动并运行jps.我的基本配置文件如下所示:

<!-- conf/core-site.xml -->
<configuration>
  <property>
    <name>fs.default.name</name>
    <!-- 
      on the master it's localhost
      on the others it's the master's DNS
      (ping works from everywhere)
    -->
    <value>hdfs://localhost:9000</value>
  </property>
  <property>
    <name>hadoop.tmp.dir</name>
    <!-- I picked /hdfs for the root FS -->
    <value>/hdfs/tmp</value>
  </property>
</configuration>

<!-- conf/hdfs-site.xml -->
<configuration>
  <property>
    <name>dfs.name.dir</name>
    <value>/hdfs/name</value>
  </property>
  <property>
    <name>dfs.data.dir</name>
    <value>/hdfs/data</value>
  </property>
  <property>
    <name>dfs.replication</name>
    <value>3</value>
  </property>
</configuration>

# conf/masters
secondary

# conf/slaves
data01
data02
data03

我现在只是想让HDFS正常运行.

我已经创建了一个用于测试的目录hadoop fs -mkdir …

我已经使用vagrant创建了一个VM并试图插入一个cd,当意识到没有这样的设备来读取它时.

一个选项是转到我的提供者UI,并通过设置添加我需要的内容.

我想知道是否有任何方法可以将此设置(将cdrom设备添加到VM)插入到vagrant文​​件中.

我的提供者是VirtualBox.

>> UPDATE

混合来自这里和这里的信息,并扩展了Vagrantfile中已经存在的一些代码示例我想出了

Vagrant.configure(VAGRANTFILE_API_VERSION) do |config| 
    ...   
    ...   
    config.vm.provider :virtualbox do |vb|
         vb.customize ["storagectl", :id, "--name", "IDEController", "--add", "ide"]
         vb.customize ["storageattach", :id, "--storagectl", "IDEController", "--port", "0", "--device", "0", "--type", "dvddrive", "--medium", "none"]      
         vb.customize ["modifyvm", :id, "--boot1", "disk", "--boot2", "dvd"]
    end
    ...
    ...   
end

在Vagrantfile中.

问题是,现在,当vagrant reload我尝试执行时,我得到了

VBoxManage.exe: error: No storage device attached to device slot 0 on port 0

我正在尝试运行grails list-profiles,但收到以下错误:

snowch@snowch-ws2:~/repos$ grails list-profiles --stacktrace
| Error Error occurred running Grails CLI: null (NOTE: Stack trace has been filtered. Use --verbose to see entire trace.)
java.lang.NullPointerException
    at org.grails.cli.profile.git.GitProfileRepository.getAllProfiles(GitProfileRepository.groovy:72)
    at org.grails.cli.profile.commands.ListProfilesCommand.handle(ListProfilesCommand.groovy:43)
    at org.grails.cli.GrailsCli.execute(GrailsCli.groovy:173)
    at org.grails.cli.GrailsCli.main(GrailsCli.groovy:99)
| Error Error occurred running Grails CLI: null

我的版本是:

snowch@snowch-ws2:~/repos$ grails --version
| Grails Version: 3.0.1
| Groovy Version: 2.4.3
| JVM Version: 1.7.0_75

这是一个全新安装的grails和gvm.

该命令grails create-app myapp可以正常运行.

这个问题类似于Grails 3.0错误,nullpointer,但是,该问题没有说明正在运行什么命令.

我想SELECT TOP 1 ...在 db2/dashDB 中执行等效的查询：

 SELECT TOP 1 * FROM customers

我怎样才能做到这一点？

PySpark文档描述了两个函数:

mapPartitions(f, preservesPartitioning=False)

   Return a new RDD by applying a function to each partition of this RDD.

   >>> rdd = sc.parallelize([1, 2, 3, 4], 2)
   >>> def f(iterator): yield sum(iterator)
   >>> rdd.mapPartitions(f).collect()
   [3, 7]

Run Code Online (Sandbox Code Playgroud)

而......

mapPartitionsWithIndex(f, preservesPartitioning=False)

   Return a new RDD by applying a function to each partition of this RDD, 
   while tracking the index of the original partition.

   >>> rdd = sc.parallelize([1, 2, 3, 4], 4)
   >>> def f(splitIndex, iterator): yield splitIndex
   >>> rdd.mapPartitionsWithIndex(f).sum()
   6

Run Code Online (Sandbox Code Playgroud)

这些功能试图解决哪些用例？我不明白他们为什么会被要求.

我正在预测批量训练模型的流程之间的评级.我正在使用此处概述的方法:ALS模型 - 如何生成full_u*v ^ t*v？

! rm -rf ml-1m.zip ml-1m
! wget --quiet http://files.grouplens.org/datasets/movielens/ml-1m.zip
! unzip ml-1m.zip
! mv ml-1m/ratings.dat .

from pyspark.mllib.recommendation import Rating

ratingsRDD = sc.textFile('ratings.dat') \
               .map(lambda l: l.split("::")) \
               .map(lambda p: Rating(
                                  user = int(p[0]), 
                                  product = int(p[1]),
                                  rating = float(p[2]), 
                                  )).cache()

from pyspark.mllib.recommendation import ALS

rank = 50
numIterations = 20
lambdaParam = 0.1
model = ALS.train(ratingsRDD, rank, numIterations, lambdaParam)

然后提取产品功能......

import json
import numpy as np

pf = model.productFeatures()

pf_vals = pf.sortByKey().values().collect()
pf_keys …