• 简化_cat使用，可以直接输入 cat 命令 ，可以滚动查看历史结果
• 支持字体放大缩小
• 支持命令历史记录（通过上下方向键来切换 ）
• 支持鼠标划取的复制粘贴（暂不复制到剪贴板）
• 安装后在 http://127.0.0.1:9200/_console  使用，也可本地使用：直接访问html文件

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Vega： https://vega.github.io/vega/examples/   Vega是什么？ 相比其他第三方可视化库，Vega的目的是让你更快将数据进行展现，vega通过声明的方式可以快速将你的数据进行各种格式化，而不用纠结于具体的调用细节，和SQL这种通用式交互语言类似，数据的输入是JSON。   Vega的Kibana插件下载地址： https://github.com/nyurik/kibana-vega-vis/releases   安装之后，就能在可视化类型里面选择Vega了，使用起来很简单，输入相应的描叙语言，如：

{
  "$schema": "https://vega.github.io/schema/ ... ot%3B,
  "description": "A simple bar chart with embedded data.",
  "width": 300, "height": 200, "padding": 5,
  "data": {
    "values": [
      {"a": "A","b": 28}, {"a": "B","b": 55}, {"a": "C","b": 43},
      {"a": "D","b": 91}, {"a": "E","b": 81}, {"a": "F","b": 53},
      {"a": "G","b": 19}, {"a": "H","b": 87}, {"a": "I","b": 52}
    ]
  },
  "mark": "bar",
  "encoding": {
    "x": {"field": "a", "type": "ordinal"},
    "y": {"field": "b", "type": "quantitative"}
  }
}

Vega支持各种可视化类型，更多详情请参照文档和例子： https://vega.github.io/vega-li ... k-def https://vega.github.io/vega/examples/ https://github.com/nyurik/kiba ... -demo

版本分别支持到最新的 es v2.4.1和 es v5.0.0-rc1 新增若干特性，支持多种选项配置，支持 pinyin 的切分，比之前需要结合 ngram 的方式更加准确， 如：liudehuaalibaba13zhuanghan->liu,de,hua,a,li,ba,ba,13,zhuang,han， 具体配置参加文档： https://github.com/medcl/elast ... inyin   下载： https://github.com/medcl/elast ... eases   欢迎测试：

curl -XPUT http://localhost:9200/medcl/ -d'
{
    "index" : {
        "analysis" : {
            "analyzer" : {
                "pinyin_analyzer" : {
                    "tokenizer" : "my_pinyin"
                    }
            },
            "tokenizer" : {
                "my_pinyin" : {
                    "type" : "pinyin",
                    "keep_separate_first_letter" : false,
                    "keep_full_pinyin" : true,
                    "keep_original" : false,
                    "limit_first_letter_length" : 16,
                    "lowercase" : true
                }
            }
        }
    }
}'

curl http://localhost:9200/medcl/_a ... lyzer
{
  "tokens" : [ {
    "token" : "liu",
    "start_offset" : 0,
    "end_offset" : 1,
    "type" : "word",
    "position" : 0
  }, {
    "token" : "de",
    "start_offset" : 1,
    "end_offset" : 2,
    "type" : "word",
    "position" : 1
  }, {
    "token" : "hua",
    "start_offset" : 2,
    "end_offset" : 3,
    "type" : "word",
    "position" : 2
  }, {
    "token" : "a",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 3
  }, {
    "token" : "b",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 4
  }, {
    "token" : "c",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 5
  }, {
    "token" : "d",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 6
  }, {
    "token" : "liu",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 7
  }, {
    "token" : "de",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 8
  }, {
    "token" : "hua",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 9
  }, {
    "token" : "wo",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 10
  }, {
    "token" : "bu",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 11
  }, {
    "token" : "zhi",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 12
  }, {
    "token" : "dao",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 13
  }, {
    "token" : "shi",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 14
  }, {
    "token" : "shui",
    "start_offset" : 2,
    "end_offset" : 31,
    "type" : "word",
    "position" : 15
  }, {
    "token" : "ldhabcdliudehuaw",
    "start_offset" : 0,
    "end_offset" : 16,
    "type" : "word",
    "position" : 16
  } ]
}