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Java Web && PHP 对比——嵌入篇

Java Web学习笔记——同时与PHP对比 博主在在学习Java Web，因为之前有学习过PHP，感觉二者有相似之处也有不同之处。所以在学习Java Web过程中，总结一下，希望能有所收获。 1、JSP的java代码嵌入和PHP文件中的PHP代码嵌入。（二者必须是Jsp文件或PHP文件才能实现嵌入html元素） 1.1JSP： //for循环输出表单 <% for ($i=0; $i < 5 ; $i++) { //for循环 %> <tr> <td>张三</td> <td>29</td> </tr> <% } //for循环结束 %> 1.2PHP： //for循环输出表单 <?php for ($i=0; $i < 5 ; $i++) { //for循环 ?> <tr> <td>张三</td> <td>29</td> </tr> <?php } //for循环结束 ?> 输出
2018-03-15
优秀的个人博客,低调大师

Java Web &amp;&amp; PHP 对比——Cookie篇

Java Web学习笔记——同时与PHP对比 博主在在学习Java Web，因为之前有学习过PHP，感觉二者有相似之处也有不同之处。所以在学习Java Web过程中，总结一下，希望能有所收获。 2、JSP和PHP的Cookie设置和获取 2.1JSP： <% //设置cookie Cookie name = new Cookie("name","JSP"); // 设置cookie过期时间为1小时。 name.setMaxAge(60*60); // 在响应头部添加cookie response.addCookie( name ); //获取Cookie Cookie[] cookies =request.getCookies(); if( cookies != null ){ for (int i = 0; i < cookies.length; i++){ cookie = cookies[i]; out.print(cookie.getName());+":"+cookie.getValue()+"</br>"); } %> 2.2PHP： <?php setcookie("user", "PHP", time()+3600); //设置cookie echo $_COOKIE["user"]; // 输出 cookie 值 ?>
2018-03-15
优秀的个人博客,低调大师

Android LruCache &amp;amp; DiskLruCache cooperate working in ListView

package zhangphil.cache; import android.app.ListActivity; import android.graphics.Bitmap; import android.graphics.BitmapFactory; import android.support.annotation.NonNull; import android.support.annotation.Nullable; import android.os.Bundle; import android.util.Log; import android.util.LruCache; import android.view.View; import android.view.ViewGroup; import android.widget.ArrayAdapter; import android.widget.ImageView; import java.io.ByteArrayOutputStream; import java.io.InputStream; import java.io.OutputStream; import java.security.MessageDigest; public class MainActivity extends ListActivity { private LruCache<String, Bitmap> mLruCache; private DiskLruCache mDiskLruCache; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); int CACHE_SIZE = 8 * 1024 * 1024; mLruCache = new LruCache(CACHE_SIZE); try { mDiskLruCache = DiskLruCache.open(this.getCacheDir(), 1, 1, CACHE_SIZE * 10); } catch (Exception e) { e.printStackTrace(); } ArrayAdapter mAdapter = new ArrayAdapter(this, 0) { @NonNull @Override public View getView(int position, @Nullable View convertView, @NonNull ViewGroup parent) { ImageView image = new ImageView(getContext()); load(R.mipmap.ic_launcher, image); return image; } @Override public int getCount() { return 20; } }; setListAdapter(mAdapter); } private void load(int id, ImageView image) { String key = null; if (id == R.mipmap.ic_launcher) { key = getMD5("R.mipmap.ic_launcher"); } //首先查找LruCache中的缓存 Bitmap bmp = mLruCache.get(key); if (bmp == null) { Log.d("LruCache缓存", "没有，继续深入到DiskLruCache读取。"); bmp = readFromDiskLruCache(key); if (bmp == null) { Log.d("LruCache与DiskLruCache", "没有缓存，开始创建新数据资源并缓存之。"); bmp = BitmapFactory.decodeResource(getResources(), id); //初次创建新资源，更新到DiskLruCache writeToDiskLruCache(key, bmp); } else { image.setImageBitmap(bmp); } //更新到LruCache缓存中，以待下一次使用。 mLruCache.put(key, bmp); Log.d("LruCache缓存", "写入完成。"); } else { //命中缓存 Log.d("LruCache缓存", "已有，复用。"); image.setImageBitmap(bmp); } } private void writeToDiskLruCache(String key, Bitmap bmp) { try { DiskLruCache.Editor editor = mDiskLruCache.edit(key); //把Bitmap转化为byte数组存储 ByteArrayOutputStream baos = new ByteArrayOutputStream(); bmp.compress(Bitmap.CompressFormat.PNG, 100, baos); byte[] bytes = baos.toByteArray(); //再次把Bitmap数组写进为DiskLruCache准备的输出流。 OutputStream os = editor.newOutputStream(0); os.write(bytes); os.flush(); //正式写入DiskLruCache editor.commit(); Log.d("DiskLruCache缓存", "写入缓存资源完毕。"); } catch (Exception e) { e.printStackTrace(); } } private Bitmap readFromDiskLruCache(String key) { DiskLruCache.Snapshot snapShot = null; try { snapShot = mDiskLruCache.get(key); } catch (Exception e) { e.printStackTrace(); } Bitmap bmp = null; if (snapShot != null) { Log.d("DiskLruCache缓存", "发现资源，复用。"); InputStream is = snapShot.getInputStream(0); bmp = BitmapFactory.decodeStream(is); } else { Log.d("DiskLruCache缓存", "没有发现缓存资源。"); } return bmp; } private String getMD5(String msg) { MessageDigest md = null; try { md = MessageDigest.getInstance("MD5"); } catch (Exception e) { e.printStackTrace(); } md.reset(); md.update(msg.getBytes()); byte[] bytes = md.digest(); String result = ""; for (byte b : bytes) { result += String.format("%02x", b); } return result; } }
2017-10-02
优秀的个人博客,低调大师

让 ESS 更灵活的新特性：UserData &amp; KeyPair &amp; RamRole &amp; Tags

弹性伸缩（Elastic Scaling Service, ESS）是一种根据业务需求和策略，自动调整其弹性计算资源的管理服务，在满足业务需求高峰增长时无缝地增加 ECS 实例，并在业务需求下降时自动减少 ECS 实例以节约成本。 为了提供更加弹性、灵活的伸缩服务，ESS 弹性伸缩配置中新增了 UserData、KeyPair、RamRole、Tags 四个特性。使用 UserData，您可以快速安全的完成自动化的配置过程，在 ECS 实例数量随着业务需求弹性变化的同时，您还能够安全、快速地完成应用级别的扩容和缩容。您还可以通过配置 KeyPair、Tags 等参数，实现更加高效、智能的 ECS 实例管理服务。 本文将详细介绍 ESS 新增的四个特性，并结合具体场景，向您阐述这些特性在 ESS 中的使用方式。您可以根据自己的业务场景，
2017-11-11
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依赖注入 in 前端 &amp;&amp; Typescript 实现依赖注入

背景 最近因为工作需要，研究了vscode的代码，看文档时发现其中提到了Dependency Injection，并且在类的构造函数中看到了这样的写法。 constructor( id: string, @IMessageService messageService: IMessageService, @IStorageService storageService: IStorageService, @ITelemetryService telemetryService: ITelemetryService, @IContextMenuService contextMenuService: IContextMenuService, @IPa
2018-02-26
优秀的个人博客,低调大师

Elasticsearch Index API &amp; Aggregations API &amp; Query DSL

这篇小菜给大家演示和讲解一些Elasticsearch的API，如在工作中用到时，方便查阅。 一、Index API 创建索引库 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 curl-XPUT 'http://127.0.0.1:9200/test_index/' -d'{ "settings" :{ "index" :{ "number_of_shards" :3, "number_of_replicas" :1 } }, "mappings" :{ "type_test_01" :{ "properties" :{ "field1" :{ "type" : "string" }, "field2" :{ "type" : "string" } } }, "type_test_02" :{ "properties" :{ "field1" :{ "type" : "string" }, "field2" :{ "type" : "string" } } } } }' 验证索引库是否存在 1 curl–XHEAD-i 'http://127.0.0.1:9200/test_index?pretty' 注: 这里加上的?pretty参数，是为了让输出的格式更好看。 查看索引库的mapping信息 1 curl–XGET-i 'http://127.0.0.1:9200/test_index/_mapping?pretty' 验证当前库type为article是否存在 1 curl-XHEAD-i 'http://127.0.0.1:9200/test_index/article' 查看test_index索引库type为type_test_01的mapping信息 1 curl–XGET-i 'http://127.0.0.1:9200/test_index/_mapping/type_test_01/?pretty' 测试索引分词器 1 2 3 4 5 curl-XGET 'http://127.0.0.1:9200/_analyze?pretty' -d' { "analyzer" : "standard" , "text" : "thisisatest" }' 输出索引库的状态信息 1 curl 'http://127.0.0.1:9200/test_index/_stats?pretty' 输出索引库的分片相关信息 1 curl-XGET 'http://127.0.0.1:9200/test_index/_segments?pretty' 删除索引库 1 curl-XDELETEhttp: //127 .0.0.1:9200 /logstash-nginxacclog-2016 .09.20/ 二、Count API 简易语法 curl -XGET 'http://elasticsearch_server:port/索引库名称/_type(当前索引类型,没有可以不写)/_count 用例: 1、统计 logstash-nginxacclog-2016.10.09 索引库有多少条记录 1 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_count' 2、统计 logstash-nginxacclog-2016.10.09 索引库status为200的有多少条记录 1 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_count?q=status:200' DSL 写法 1 2 3 4 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_count' -d' { "query" : { "term" :{ "status" : "200" }} }' 三、Aggregations API （数据分析和统计） 注: 聚合相关的API只能对数值、日期 类型的字段做计算。 1、求平均数 业务场景: 统计访问日志中的平均响应时长 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "match_all" :{}}, "aggs" :{ "avg_num" :{ "avg" :{ "field" : "responsetime" }} }, "size" :0 #这里的size:0表示不输出匹配到数据，只输出聚合结果。 }' { "took" :598, "timed_out" : false , "_shards" :{ "total" :5, "successful" :5, "failed" :0 }, "hits" :{ "total" :32523067, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "avg_num" :{ "value" :0.0472613558675975 } } } #得到平均响应时长为0.0472613558675975秒 2、求最大值 业务场景:获取访问日志中最长的响应时间 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "match_all" :{}}, "aggs" :{ "max_num" :{ "max" :{ "field" : "responsetime" }} }, "size" :0 }' { "took" :29813, "timed_out" : false , "_shards" :{ "total" :431, "successful" :431, "failed" :0 }, "hits" :{ "total" :476952009, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "max_num" :{ "value" :65.576 } } } #得到最大响应时长为65.576秒 3、求最小值 业务场景: 获取访问日志中最快的响应时间 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "match_all" :{}}, "aggs" :{ "min_num" :{ "min" :{ "field" : "responsetime" }} }, "size" :0 }' { "took" :2145, "timed_out" : false , "_shards" :{ "total" :431, "successful" :431, "failed" :0 }, "hits" :{ "total" :477156773, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "min_num" :{ "value" :0.0 } } } #看来最快的响应时间竟然是0，笔者通过查询日志发现，原来这些响应时间为0的请求是被nginx拒绝掉的。 4、数值求和 业务场景: 统计一天的访问日志中为响应请求总共输出了多少流量。 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "match_all" :{}}, "aggs" :{ "sim_num" :{ "sum" :{ "field" : "size" }} }, "size" :0 }' { "took" :1226, "timed_out" : false , "_shards" :{ "total" :5, "successful" :5, "failed" :0 }, "hits" :{ "total" :32523067, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "sim_num" :{ "value" :6.9285945505E10 } } } #这个数有点大，后面的E10表示6.9285945505X10^10，笔者算了下，大概70GB流量。 5、获取常用的数据统计指标 其中包括（ 最大值、最小值、平均值、求和、个数 ） 业务场景: 求访问日志中的 responsetime （ 最大值、最小值、平均值、求和、个数 ） 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "match_all" :{}}, "aggs" :{ "like_stats" :{ "stats" :{ "field" : "responsetime" }} }, "size" :0 }' { "took" :2868, "timed_out" : false , "_shards" :{ "total" :431, "successful" :431, "failed" :0 }, "hits" :{ "total" :477797577, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "like_stats" :{ "count" :469345191, "min" :0.0, "max" :65.576, "avg" :0.06088492952649428, "sum" :2.8576048877634E7 } } } 这个是上面统计方式的增强版，新增了几个统计数据 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "match_all" :{}}, "aggs" :{ "like_stats" :{ "extended_stats" :{ "field" : "responsetime" }} }, "size" :0 }' { "took" :2830, "timed_out" : false , "_shards" :{ "total" :431, "successful" :431, "failed" :0 }, "hits" :{ "total" :478145456, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "like_stats" :{ "count" :469687072, "min" :0.0, "max" :65.576, "avg" :0.06087745173159307, "sum" :2.859335205463328E7, "sum_of_squares" :1.3162790273264633E7, "variance" :0.02431853151732958, "std_deviation" :0.1559440012226491, "std_deviation_bounds" :{ "upper" :0.3727654541768913, "lower" :-0.2510105507137051 } } } } #其中新增的三个返回结果分别是: #sum_of_squares平方和 #variance方差 #std_deviation标准差 6、统计数据在某个区间所占的百分比 业务场景: 求出访问日志中响应时间的各个区间，所占的百分比 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "match_all" :{}}, "aggs" :{ "outlier" :{ "percentiles" :{ "field" : "responsetime" }} }, "size" :0 }' { "took" :60737, "timed_out" : false , "_shards" :{ "total" :431, "successful" :431, "failed" :0 }, "hits" :{ "total" :478287997, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "outlier" :{ "values" :{ "1.0" :0.0, "5.0" :0.0, "25.0" :0.02, "50.0" :0.038999979789136247, "75.0" :0.06247223731250421, "95.0" :0.16479760590682113, "99.0" :0.520510492464275 } } } } #values对应的列为所占的百分比，右边则是对应的数据值。表示: #响应时间小于或等于0的请求占1% #响应时间小于或等于0的请求占5% #响应时间小于或等于0.02的请求占25% #响应时间小于或等于0.038999979789136247的请求占50% #响应时间小于或等于0.06247223731250421的请求占75% #响应时间小于或等于0.16479760590682113的请求占95% #响应时间小于或等于0.520510492464275的请求占99% #还可以通过percents参数，自定义一些百分比区间，如10%,30%,60%,90%等。 #注:经笔者测试，这个方法只能对数值类型的字段进行统计，无法操作字符串类型的字段。 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "match_all" :{}}, "aggs" :{ "outlier" :{ "percentiles" :{ "field" : "status" , "percents" :[5,10,20,50,99.9] } } }, "size" :0 }' 7、求指定字段数值在各个区间所占的百分比 业务场景：求响应时间 0, 0.01, 0.1, 0.2 在整个日志文件中，分别所占的百分比。 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "match_all" :{}}, "aggs" :{ "outlier" :{ "percentile_ranks" :{ "field" : "responsetime" , "values" :[0,0.01,0.1,0.2] } } }, "size" :0 }' { "took" :6950, "timed_out" : false , "_shards" :{ "total" :5, "successful" :5, "failed" :0 }, "hits" :{ "total" :32523067, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "outlier" :{ "values" :{ "0.0" :8.79897648675993, "0.01" :17.90331319256336, "0.1" :91.18297638776373, "0.2" :98.22564774611764 } } } } #响应时间小于或等于0的请求占8.7% #响应时间小于或等于0.01的请求占17.9% #响应时间小于或等于0.1的请求占91.1% #响应时间小于或等于0.2的请求占98.2% 8、求该数值范围内有多少文档匹配 业务场景: 求访问日志中的响应时间为，0 ~ 0.02、0.02 ~ 0.1 、大于 0.1 这三个数值区间内，各有多少文档匹配。 "ranges":[{"to": 0.02}, {"from":0.02,"to":0.1},{"from":0.1}] {"to": 0.02} 求响应时间 0 ~ 0.02 区间内的匹配文档数 {"from":0.02,"to":0.1} 求响应时间 0.02 ~ 0.1 区间内匹配的文档数 {"from":0.1} 求响应时间大于 0.1 匹配的文档数 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "match_all" :{}}, "aggs" :{ "range_info" :{ "range" :{ "field" : "responsetime" , "ranges" :[{ "to" :0.02},{ "from" :0.02, "to" :0.1},{ "from" :0.1}] } } }, "size" :0 }' { "took" :474, "timed_out" : false , "_shards" :{ "total" :5, "successful" :5, "failed" :0 }, "hits" :{ "total" :32523067, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "range_info" :{ "buckets" :[{ "key" : "*-0.02" , "to" :0.02, "to_as_string" : "0.02" , "doc_count" :9093600 },{ "key" : "0.02-0.1" , "from" :0.02, "from_as_string" : "0.02" , "to" :0.1, "to_as_string" : "0.1" , "doc_count" :20547128 },{ "key" : "0.1-*" , "from" :0.1, "from_as_string" : "0.1" , "doc_count" :2879418 }] } } } "aggregations" :{ "range_info" :{ "buckets" :[{ "key" : "*-0.02" , "to" :0.02, "to_as_string" : "0.02" , "doc_count" :9093600 } #响应时间在0~0.02的文档数是9093600 ,{ "key" : "0.02-0.1" , "from" :0.02, "from_as_string" : "0.02" , "to" :0.1, "to_as_string" : "0.1" , "doc_count" :20547128 } #响应时间在0.02~0.1的文档数是20547128 ,{ "key" : "0.1-*" , "from" :0.1, "from_as_string" : "0.1" , "doc_count" :2879418 } #响应时间在大于0.1的文档数是2879418 ] } } 9、求时间范围内有多少文档匹配 业务场景：求访问日志中，在 2016-10-09T01:00:00 之前的文档有多少。 和在 2016-10-09T02:00:00 之后的文档有多少。 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "match_all" :{}}, "aggs" :{ "range_info" :{ "date_range" :{ "field" : "@timestamp" , "ranges" :[{ "to" : "2016-10-09T01:00:00" },{ "from" : "2016-10-09T02:00:00" }] } } }, "size" :0 }' { "took" :432, "timed_out" : false , "_shards" :{ "total" :5, "successful" :5, "failed" :0 }, "hits" :{ "total" :32523067, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "range_info" :{ "buckets" :[{ "key" : "*-2016-10-09T01:00:00.000Z" , "to" :1.4759748E12, "to_as_string" : "2016-10-09T01:00:00.000Z" , "doc_count" :613460 }, #在2016-10-09T01:00:00之前的文档数有613460 { "key" : "2016-10-09T02:00:00.000Z-*" , "from" :1.4759784E12, "from_as_string" : "2016-10-09T02:00:00.000Z" , "doc_count" :31264881 } #在2016-10-09T02:00:00之后的文档数有31264881 ] } } } 10、聚合结果不依赖于查询结果集 "global":{} 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "term" :{ "status" : "200" }}, "aggs" :{ "all_articles" :{ "global" :{}, "aggs" :{ "sum_like" :{ "sum" :{ "field" : "responsetime" }} } } }, "size" :0 }' { "took" :1519, "timed_out" : false , "_shards" :{ "total" :5, "successful" :5, "failed" :0 }, "hits" :{ "total" :26686196, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "all_articles" :{ "doc_count" :32523067, "sum_like" :{ "value" :1536946.1929722272 } } } } #可以看到查询结果集hitstotal部分才匹配到26686196条记录。而聚合的文档数则是32523067多于查询结果匹配到的文档。 #聚合结果为1536946.1929722272 #我们再看看没有引用"global":{}参数的方式 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "query" : { "term" :{ "status" : "200" }}, "aggs" :{ "sum_like" :{ "sum" :{ "field" : "responsetime" }} }, "size" :0 }' { "took" :1326, "timed_out" : false , "_shards" :{ "total" :5, "successful" :5, "failed" :0 }, "hits" :{ "total" :26686196, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "sum_like" :{ "value" :1526710.3929916811 } } } #聚合结果小于上诉的结果。表示这次的聚合的值，是依赖于检索匹配到的文档。 11、分组聚合 用于统计指定字段在自定义的固定增长区间下，每个增长后的值，所匹配的文档数量。 1 2 3 4 5 6 7 8 9 10 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "aggs" :{ "like_histogram" :{ "histogram" :{ "field" : "status" , "interval" :200, "min_doc_count" :1} } }, "size" :0 }' #对status字段操作，增长区间为200，为了避免有的区间匹配为0所导致空数据，所以这里指定最小文档数为1"histogram":{"field":"status","interval":200,"min_doc_count":1} 12、分组聚合-基于时间做分组 "date_histogram":{"field": "@timestamp", "interval": "1d","format": "yyyy-MM-dd",} "field": "@timestamp" 指定记录时间的字段 "interval": "1d" 分组区间为每天. 1M 每月、1H 每小时、1m 每分钟 "format": "yyyy-MM-dd" 指定时间的输出格式 统计每天产生的日志数量 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-*/_search?pretty' -d'{ "aggs" :{ "date_histogram_info" :{ "date_histogram" :{ "field" : "@timestamp" , "interval" : "1d" , "format" : "yyyy-MM-dd" , "min_doc_count" :1} } } }' "aggregations" :{ "date_histogram_info" :{ "buckets" :[{ "key_as_string" : "2016-09-27" , "key" :1474934400000, "doc_count" :6895375 },{ "key_as_string" : "2016-09-28" , "key" :1475020800000, "doc_count" :1255775 },{ "key_as_string" : "2016-09-29" , "key" :1475107200000, "doc_count" :38512862 },{ "key_as_string" : "2016-09-30" , "key" :1475193600000, "doc_count" :35314225 },{ "key_as_string" : "2016-10-01" , "key" :1475280000000, "doc_count" :45358162 },{ "key_as_string" : "2016-10-02" , "key" :1475366400000, "doc_count" :42058056 },{ "key_as_string" : "2016-10-03" , "key" :1475452800000, "doc_count" :39945587 },{ "key_as_string" : "2016-10-04" , "key" :1475539200000, "doc_count" :39509128 },{ "key_as_string" : "2016-10-05" , "key" :1475625600000, "doc_count" :40506342 },{ "key_as_string" : "2016-10-06" , "key" :1475712000000, "doc_count" :43303499 },{ "key_as_string" : "2016-10-07" , "key" :1475798400000, "doc_count" :44234780 },{ "key_as_string" : "2016-10-08" , "key" :1475884800000, "doc_count" :32880600 },{ "key_as_string" : "2016-10-09" , "key" :1475971200000, "doc_count" :32523067 },{ "key_as_string" : "2016-10-10" , "key" :1476057600000, "doc_count" :31454044 },{ "key_as_string" : "2016-10-11" , "key" :1476144000000, "doc_count" :2018401 }] } } } #基于小时做分组 #统计当天每小时产生的日志数量 curl-XGET 'http://127.0.0.1:9200/logstash-nginxacclog-2016.10.09/_search?pretty' -d'{ "aggs" :{ "date_histogram_info" :{ "date_histogram" :{ "field" : "@timestamp" , "interval" : "1H" , "format" : "yyyy-MM-dd-H" , "min_doc_count" :1} } }, "size" :0 }' { "took" :530, "timed_out" : false , "_shards" :{ "total" :5, "successful" :5, "failed" :0 }, "hits" :{ "total" :32523067, "max_score" :0.0, "hits" :[] }, "aggregations" :{ "date_histogram_info" :{ "buckets" :[{ "key_as_string" : "2016-10-09-0" , "key" :1475971200000, "doc_count" :613460 },{ "key_as_string" : "2016-10-09-1" , "key" :1475974800000, "doc_count" :644726 },{ "key_as_string" : "2016-10-09-2" , "key" :1475978400000, "doc_count" :687196 },{ "key_as_string" : "2016-10-09-3" , "key" :1475982000000, "doc_count" :730831 },{ "key_as_string" : "2016-10-09-4" , "key" :1475985600000, "doc_count" :1460320 },{ "key_as_string" : "2016-10-09-5" , "key" :1475989200000, "doc_count" :1469098 },{ "key_as_string" : "2016-10-09-6" , "key" :1475992800000, "doc_count" :1004399 },{ "key_as_string" : "2016-10-09-7" , "key" :1475996400000, "doc_count" :962843 },{ "key_as_string" : "2016-10-09-8" , "key" :1476000000000, "doc_count" :1232560 },{ "key_as_string" : "2016-10-09-9" , "key" :1476003600000, "doc_count" :1809741 },{ "key_as_string" : "2016-10-09-10" , "key" :1476007200000, "doc_count" :2802804 },{ "key_as_string" : "2016-10-09-11" , "key" :1476010800000, "doc_count" :3941192 },{ "key_as_string" : "2016-10-09-12" , "key" :1476014400000, "doc_count" :4631032 },{ "key_as_string" : "2016-10-09-13" , "key" :1476018000000, "doc_count" :3651968 },{ "key_as_string" : "2016-10-09-14" , "key" :1476021600000, "doc_count" :2079933 },{ "key_as_string" : "2016-10-09-15" , "key" :1476025200000, "doc_count" :973578 },{ "key_as_string" : "2016-10-09-16" , "key" :1476028800000, "doc_count" :517435 },{ "key_as_string" : "2016-10-09-17" , "key" :1476032400000, "doc_count" :388382 },{ "key_as_string" : "2016-10-09-18" , "key" :1476036000000, "doc_count" :361296 },{ "key_as_string" : "2016-10-09-19" , "key" :1476039600000, "doc_count" :345926 },{ "key_as_string" : "2016-10-09-20" , "key" :1476043200000, "doc_count" :342214 },{ "key_as_string" :
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