运行上面的集群请至少保证docker拥有3g以上，因为Elasticsearch by default deploys with a 1GB heap，开始只使用了2g时就会出现内存不足导致es启动失败，如果是下面几种错误信息可以尝试增加内存
master not discovered or elected yet, an election requires a node with id
或
master not discovered yet, this node has not previously joined a bootstrapped (v7+) cluster, and this node must discover master-eligible nodes
或
max virtual memory areas vm.max_map_count [65530] is too low, increase to at least [262144]
using System;
using ToolGood.Words;
namespace KeywordTest
{
class Program
{
static void Main(string[] args)
{
var stopwatch = System.Diagnostics.Stopwatch.StartNew();
string post = System.IO.File.ReadAllText(@"D:\Projects\Opensource\ToolGood.Words\csharp\KeywordTest\sample_post");
string[] spams = System.IO.File.ReadAllLines(@"D:\Projects\Opensource\ToolGood.Words\csharp\KeywordTest\SpamWordsCN.min.txt");
StringSearch iwords = new StringSearch();
iwords.SetKeywords(spams);
stopwatch.Start();
for (var i = 0; i < 500; i++)
{ var f = iwords.FindFirst(post); }
stopwatch.Stop();
var s = stopwatch.ElapsedMilliseconds;
Console.WriteLine("测试用时（ms）：" + s);
}
}
}
def test2():
from clr_loader import get_coreclr
from pythonnet import set_runtime
rt = get_coreclr("./runtimeconfig.json")
set_runtime(rt)
import clr
clr.AddReference('ToolGood.Words')
import clr
from System import String
from ToolGood.Words import StringSearch
stringSearch=StringSearch()
with open('./sample_post',encoding='utf-8') as f:
test_post = f.read()
from System.Collections.Generic import List
spam_words=List[String]()
with open('././SpamWordsCN.min.txt',encoding='utf-8') as f:
for line in [line.rstrip() for line in f]:
spam_words.Add(String(line))
stringSearch.SetKeywords(spam_words)
import time
start = time.time()
times = 500
while times > 0:
f = stringSearch.FindFirst(test_post)
times -= 1
end = time.time()
print('程序运行时间:%s毫秒' % ((end - start) * 1000))
def test3():
with open('./sample_post', encoding='utf-8') as f:
test_post = f.read()
spam_words={}
with open('././SpamWordsCN.min.txt', encoding='utf-8') as f:
words=[line.strip() for line in f]
for w in words:
if len(w)>0:
spam_words[w[0]]=w
import time
start = time.time()
times = 500
while times > 0:
for i,c in enumerate(test_post):
if c in spam_words:
is_found=True
for j,sc in enumerate(spam_words):
if sc!=test_post[i+j]:
# compare failed
is_found=False
break
# found
if is_found:
#print('找到了%s'%spam_words)
break
times -= 1
end = time.time()
print('程序运行时间:%s毫秒' % ((end - start) * 1000))
from StringSearch import StringSearch
with open('./sample_post',encoding='utf-8') as f:
test_post = f.read()
with open('././SpamWordsCN.min.txt',encoding='utf-8') as f:
spam_words = [line.rstrip() for line in f]
import time
search = StringSearch()
search.SetKeywords(spam_words)
start = time.time()
times = 500
while times > 0:
f = search.FindFirst(test_post)
times -= 1
end = time.time()
print('程序运行时间:%s毫秒' % ((end - start) * 1000))
我有四万七千张照片，别问我为啥有那么多照片，手机更新了，但是每次都是恢复以前的一切，但是照片庞大到传统的各种导出很无力，特别是使用了icloud也是。这里icloud一定要用微软商店中的版本，老版本就是有个安装包的那个可以点击下载所有文件，问题是不会自动下载最新的，然后下载还容易中断。最新版本会再本地出现一个icloud挂载文件夹，也是windows10最新支持的，出于备份的目的我需要有的不仅仅是镜像而是真正的下载我的拷贝。可以右击资源管理器中的icloud标识选择“Always keep on this device”，他就会自动下载所有文件。
nas玩了好多年，现在稳定使用的事赛扬的cpu和六块大硬盘。系统使用的是windows server 2019，为啥没用黑群辉，为啥没用linux。作为母系统来说win绝对是最好的，个人nas经常要解决的就是长期运行、不可间断的，比如百度网盘，长时间跑脚本的数据，这些都要求系统跟pc有很大的兼容，所以应用上win胜出。win server还提供了block级别的重复数据，所以省心的给你节省空间。这块其实专门写个专题都有必要。
。
