开发app和网站的公司/竞价广告代运营

作者：David Pilato

对于 Elasticsearch®，我们知道联接应该在 “索引时” 而不是查询时完成。 本博文是一系列三篇博文的开始，因为我们可以在 Elastic® 生态系统中采取多种方法。 我们将介绍如何在 Elasticsearch 中做到这一点。 下一篇博文将介绍如何使用集中式组件 Logstash 来实现这一点，上一篇博文将展示如何使用 Elastic Agent/Beats 在边缘实现这一点。

举一个简单的例子，假设我们是一个电子商务网站，在 kibana_sample_data_logs 中收集日志：

{"agent": "Mozilla/5.0 (X11; Linux i686) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.696.50 Safari/534.24","bytes": 1831,"clientip": "30.156.16.164","extension": "","geo": {"srcdest": "US:IN","src": "US","dest": "IN","coordinates": {"lat": 55.53741389,"lon": -132.3975144}},"host": "elastic-elastic-elastic.org","index": "kibana_sample_data_logs","ip": "30.156.16.163","machine": {"ram": 9663676416,"os": "win xp"},"memory": 73240,"message": "30.156.16.163 - - [2018-09-01T12:43:49.756Z] \"GET /wp-login.php HTTP/1.1\" 404 1831 \"-\" \"Mozilla/5.0 (X11; Linux i686) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.696.50 Safari/534.24\"","phpmemory": 73240,"referer": "http://www.elastic-elastic-elastic.com/success/timothy-l-kopra","request": "/wp-login.php","response": 404,"tags": ["success","info"],"timestamp": "2023-03-18T12:43:49.756Z","url": "https://elastic-elastic-elastic.org/wp-login.php","utc_time": "2023-03-18T12:43:49.756Z","event": {"dataset": "sample_web_logs"}
}

请注意，你可以通过单击 “Sample web blogs” 框中的 “Add data”按钮，使用 Kibana® 示例数据集轻松导入此数据集：

我们还有一个 VIP 索引，其中包含有关我们客户的信息：

{ "ip" : "30.156.16.164", "vip": true, "name": "David P" 
}

要导入此示例数据集，我们只需运行：

DELETE /vip
PUT /vip
{"mappings": {"properties": {"ip": { "type": "keyword" },"name": { "type": "text" },"vip": { "type": "boolean" }}}
}
POST /vip/_bulk
{ "index" : { } }
{ "ip" : "30.156.16.164", "vip": true, "name": "David P" }
{ "index" : { } }
{ "ip" : "164.85.94.243", "vip": true, "name": "Philipp K" }
{ "index" : { } }
{ "ip" : "50.184.59.162", "vip": true, "name": "Adrienne V" }
{ "index" : { } }
{ "ip" : "236.212.255.77", "vip": true, "name": "Carly R" }
{ "index" : { } }
{ "ip" : "16.241.165.21", "vip": true, "name": "Naoise R" }
{ "index" : { } }
{ "ip" : "246.106.125.113", "vip": true, "name": "Iulia F" }
{ "index" : { } }
{ "ip" : "81.194.200.150", "vip": true, "name": "Jelena Z" }
{ "index" : { } }
{ "ip" : "111.237.144.54", "vip": true, "name": "Matt R" }

要执行 “joins at index time”，我们需要丰富我们的数据集以获得如下所示的最终日志：

{"agent": "Mozilla/5.0 (X11; Linux i686) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.696.50 Safari/534.24","bytes": 1831,"clientip": "30.156.16.164","extension": "","geo": {"srcdest": "US:IN","src": "US","dest": "IN","coordinates": {"lat": 55.53741389,"lon": -132.3975144}},"host": "elastic-elastic-elastic.org","index": "kibana_sample_data_logs","ip": "30.156.16.163","machine": {"ram": 9663676416,"os": "win xp"},"memory": 73240,"message": "30.156.16.163 - - [2018-09-01T12:43:49.756Z] \"GET /wp-login.php HTTP/1.1\" 404 1831 \"-\" \"Mozilla/5.0 (X11; Linux i686) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.696.50 Safari/534.24\"","phpmemory": 73240,"referer": "http://www.elastic-elastic-elastic.com/success/timothy-l-kopra","request": "/wp-login.php","response": 404,"tags": ["success","info"],"timestamp": "2023-03-18T12:43:49.756Z","url": "https://elastic-elastic-elastic.org/wp-login.php","utc_time": "2023-03-18T12:43:49.756Z","event": {"dataset": "sample_web_logs"},"vip": true, "name": "David P" 
}

你可以使用摄取管道中的 Elasticsearch Enrich Processor 开箱即用地执行此操作。 让我们看看如何做到这一点。

在 Elasticsearch 中丰富 Elasticsearch 数据

摄取管道 - ingest pipeline

让我们首先使用摄取管道。

我们可以从一个空的开始，我们将用它来模拟我们想要的行为。 我们不需要原始数据集的完整字段集，因此我们对其进行了简化：

POST /_ingest/pipeline/_simulate
{"docs": [{"_source": {"clientip": "30.156.16.164"}}],"pipeline": {"processors": []}
}

我们现在需要向我们的管道添加一个 enrich processor。 但为此，我们需要首先创建一个丰富的策略 (enrich policy)：

PUT /_enrich/policy/vip-policy
{"match": {"indices": "vip","match_field": "ip","enrich_fields": ["name", "vip"]}
}

创建丰富策略后，我们可以使用执行丰富策略 API 来执行它：

PUT /_enrich/policy/vip-policy/_execute

我们现在可以模拟它：

POST /_ingest/pipeline/_simulate
{"docs": [{"_source": {"clientip": "30.156.16.164"}}],"pipeline": {"processors": [{"enrich": {"policy_name": "vip-policy","field": "clientip","target_field": "enriched"}}]}
}

这给出如下的响应：

{"docs": [{"doc": {"_index": "_index","_id": "_id","_version": "-3","_source": {"enriched": {"name": "David P","vip": true,"ip": "30.156.16.164"},"clientip": "30.156.16.164"},"_ingest": {"timestamp": "2023-04-06T17:14:29.127569953Z"}}}]
}

我们只需清理一下数据即可获得我们期望的结构：

POST /_ingest/pipeline/_simulate
{"docs": [{"_source": {"clientip": "30.156.16.164"}}],"pipeline": {"processors": [{"enrich": {"policy_name": "vip-policy","field": "clientip","target_field": "enriched"}},{"rename": {"field": "enriched.name","target_field": "name"}},{"rename": {"field": "enriched.vip","target_field": "vip"}},{"remove": {"field": "enriched"}}]}
}

现在给出了预期的结果：

{"docs": [{"doc": {"_index": "_index","_id": "_id","_version": "-3","_source": {"name": "David P","vip": true,"clientip": "30.156.16.164"},"_ingest": {"timestamp": "2023-04-06T17:16:08.175186282Z"}}}]
}

我们现在可以存储最终的管道：

PUT /_ingest/pipeline/vip
{"processors": [{"enrich": {"policy_name": "vip-policy","field": "clientip","target_field": "enriched"}},{"rename": {"field": "enriched.name","target_field": "name","ignore_failure": true}},{"rename": {"field": "enriched.vip","target_field": "vip","ignore_failure": true}},{"remove": {"field": "enriched","ignore_failure": true}}]
}

请注意，我们通过添加一些 ignore_failure 指令对其进行了一些更改，因为我们可能在 vip 索引中找不到任何相关数据。

我们可以使用与源索引相同的映射来创建目标索引：

# Get the source mapping
GET /kibana_sample_data_logs/_mapping# Create the destination index
PUT /kibana_sample_data_logs_new
{// Paste the source mappings structure"mappings": {"properties": {// And add the properties we are adding"name": {"type": "keyword"},"vip": {"type": "boolean"}}}
}

并调用重建索引 API：

POST _reindex
{"source": {"index": "kibana_sample_data_logs"},"dest": {"index": "kibana_sample_data_logs_new","pipeline": "vip"}
}

让我们检查一下工作是否已完成：

GET /kibana_sample_data_logs_new/_search?filter_path=aggregations.by_name.buckets
{"size": 0,"aggs": {"by_name": {"terms": {"field": "name"}}}
}

上述命令给出如下类似的响应：

{"aggregations": {"by_name": {"buckets": [{"key": "David P","doc_count": 100},{"key": "Philipp K","doc_count": 29},{"key": "Adrienne V","doc_count": 26},{"key": "Carly R","doc_count": 26},{"key": "Iulia F","doc_count": 25},{"key": "Naoise R","doc_count": 25},{"key": "Jelena Z","doc_count": 24},{"key": "Matt R","doc_count": 24}]}}
}

运行时字段丰富

丰富数据的另一种方法是在搜索时而不是索引时执行此操作。 这与本文的第一句话相悖，但有时，你需要进行一些权衡。 在这里，我们想用搜索速度来交换灵活性。

运行时字段功能 (runtime field feature) 允许丰富搜索响应对象，但不能用于查询或聚合数据。 此功能的一个简单示例：

GET kibana_sample_data_logs/_search?filter_path=hits.hits.fields
{"size": 1,"query": {"match": {"clientip": "30.156.16.164"}}, "runtime_mappings": {"enriched": {"type": "lookup", "target_index": "vip", "input_field": "clientip", "target_field": "ip", "fetch_fields": ["name", "vip"] }},"fields": ["clientip","enriched"],"_source": false
}

上述命令给出如下的响应：

{"hits": {"hits": [{"fields": {"enriched": [{"name": ["David P"],"vip": [true]}],"clientip": ["30.156.16.164"]}}]}
}

请注意，这也可以添加为映射的一部分：

PUT kibana_sample_data_logs/_mappings
{"runtime": {"enriched": {"type": "lookup", "target_index": "vip", "input_field": "clientip", "target_field": "ip", "fetch_fields": ["name", "vip"] }}
}GET kibana_sample_data_logs/_search
{"size": 1,"query": {"match": {"clientip": "30.156.16.164"}}, "fields": ["clientip","enriched"]
}

但是，如果你希望能够搜索或聚合这些字段，则需要在搜索时实际发出 (emit) 一些内容。

请注意，我们不能使用此方法在另一个索引中进行查找。 因此，因为且仅仅因为列表的长度很小，我们可以使用脚本来动态进行 “丰富”：

PUT kibana_sample_data_logs/_mappings
{"runtime": {"name": {"type": "keyword","script": {"source": """def name=params.name;for (int i=0; i< params.lookup.length; i++) {if (params.lookup[i].ip == doc['clientip'].value) {emit(params.lookup[i].name);break;}}""","lang": "painless","params": {"name": "David P","lookup": [{ "ip" : "30.156.16.164", "vip": true, "name": "David P" },{ "ip" : "164.85.94.243", "vip": true, "name": "Philipp K" },{ "ip" : "50.184.59.162", "vip": true, "name": "Adrienne V" },{ "ip" : "236.212.255.77", "vip": true, "name": "Carly R" },{ "ip" : "16.241.165.21", "vip": true, "name": "Naoise R" },{ "ip" : "246.106.125.113", "vip": true, "name": "Iulia F" },{ "ip" : "81.194.200.150", "vip": true, "name": "Jelena Z" },{ "ip" : "111.237.144.54", "vip": true, "name": "Matt R" }]}}},"vip": {"type": "boolean","script": {"source": """def name=params.name;for (int i=0; i< params.lookup.length; i++) {if (params.lookup[i].ip == doc['clientip'].value) {emit(params.lookup[i].vip);break;}}""","lang": "painless","params": {"name": "David P","lookup": [{ "ip" : "30.156.16.164", "vip": true, "name": "David P" },{ "ip" : "164.85.94.243", "vip": true, "name": "Philipp K" },{ "ip" : "50.184.59.162", "vip": true, "name": "Adrienne V" },{ "ip" : "236.212.255.77", "vip": true, "name": "Carly R" },{ "ip" : "16.241.165.21", "vip": true, "name": "Naoise R" },{ "ip" : "246.106.125.113", "vip": true, "name": "Iulia F" },{ "ip" : "81.194.200.150", "vip": true, "name": "Jelena Z" },{ "ip" : "111.237.144.54", "vip": true, "name": "Matt R" }]}}}}
}

我们可以再次聚合这些运行时字段：

GET /kibana_sample_data_logs/_search?filter_path=aggregations.by_name.buckets
{"size": 0,"aggs": {"by_name": {"terms": {"field": "name"}}}
}

这给出了与我们之前看到的相同的结果，但当然有点慢：

{"aggregations": {"by_name": {"buckets": [{"key": "David P","doc_count": 100},{"key": "Philipp K","doc_count": 29},{"key": "Adrienne V","doc_count": 26},{"key": "Carly R","doc_count": 26},{"key": "Iulia F","doc_count": 25},{"key": "Naoise R","doc_count": 25},{"key": "Jelena Z","doc_count": 24},{"key": "Matt R","doc_count": 24}]}}
}

同样，此方法不适用于大索引，因此如我们在第一部分中看到的那样重新索引数据将是首选方法。

本文中描述的任何特性或功能的发布和时间安排均由 Elastic 自行决定。 当前不可用的任何特性或功能可能无法按时交付或根本无法交付。