elasticsearch_parse 1.5

#elasticsearch_parse
=================

首先注明下本项目是由来,学习Elasticsearch本身是有些痛苦的, 我们可以熟练的编写SQL 、 Mongodb语句，但对于Elasticsearch Dsl语法总是蒙头，一个劲的翻找笔记...
简单说 Elasticsearch Parse可以让你更容易的上手ES, 他的功能主要是语句映射, 有点ORM的意思...

此项目继承于`elasticsearch-dsl-py`, 其实砍掉了大量的代码,然后中间又加了一些佐料, 只是为了DSL语句映射功能 !

安装方法:
```
pip install elasticsearch_parse
```

下面我们来体验下封装后的es语法解释器.

首先用原始的DSL语法操作,一眼望去会有些麻烦, 手写起来会更麻烦.
```
from elasticsearch import Elasticsearch
client = Elasticsearch()

response = client.search(
index="my-index",
body={
"query": {
"filtered": {
"query": {
"bool": {
"must": [{"match": {"title": "python"}}],
"must_not": [{"match": {"description": "beta"}}]
}
},
"filter": {"term": {"category": "search"}}
}
},
"aggs" : {
"per_tag": {
"terms": {"field": "tags"},
"aggs": {
"max_lines": {"max": {"field": "lines"}}
}
}
}
}
)

```

下面是使用Elasticsearch_parse的用法,要多简单就多简单

```
from elasticsearch_parse import Search, Q

s = Search(index="my-index") \
.filter("term", blog="xiaorui.cc") \
.query("match", author="ruifengyun") \
.query(~Q("match", face="good"))

s.aggs.bucket('per_tag', 'terms', field='tags')

response = s.execute()
```
我们得到的结果是:
```
{
"query": {
"filtered": {
"filter": {
"term": {
"blog": "xiaorui.cc"
}
},
"query": {
"bool": {
"must_not": [
{
"match": {
"face": "good"
}
}
],
"must": [
{
"match": {
"author": "ruifengyun"
}
}
]
}
}
}
},
"aggs": {
"per_tag": {
"terms": {
"field": "tags"
}
}
}
}
```

s.query('range', ** {'@timestamp': {'lt': 'now'}})