[](https://circleci.com/gh/avlahop/dask-elk/tree/master)
# dask-elk
Use dask to fetch data from Elasticsearch in parallel by sending the request to each shard separatelly.
# Table of Contents
1. [Introduction](#introduction)
1. [Usage](#usage)
## Introduction <a name='introduction' />
The library tries to imitate the functionality of the ES Hadoop plugin for spark. `dask-elk` performs a parallel read across all the target indices shards.
In order to achieve that it uses Elasticsearch scrolling mechanism.
## Usage <a name="usage" />
To use the library and read from an index:
```python
from dask_elk.client import DaskElasticClient
# First create a client
client = DaskElasticClient() # localhost Elasticsearch
index = 'my-index'
df = client.read(index=index, doc_type='_doc')
```
You can even pass a query to push down to elasticsearch, so that any filtering can be done on the Elasticsearch side. Because `dask-elk` uses scroll mechanism aggregations are not supported
```python
from dask_elk.client import DaskElasticClient
# First create a client
client = DaskElasticClient() # localhost Elasticsearch
query = {
"query" : {
"term" : { "user" : "kimchy" }
}
}
index = 'my-index'
df = client.read(query=query, index=index, doc_type='_doc')
```
Read documentation [here](https://dask-elk.readthedocs.io)
dask_elk-0.1.0.tar.gz （19个子文件） 
dask_elk-0.1.0 dask_elk.egg-info 
dependency_links.txt 1B
PKG-INFO 2KB SOURCES.txt 431B top_level.txt 9B requires.txt 49B setup.cfg 38B README.md 1KB PKG-INFO 2KB dask_elk parsers.py 2KB __init__.py 0B reader.py 4KB helpers.py 106B delayed_methods.py 1KB client.py 8KB elk_entities shards.py 573B index.py 6KB node.py 2KB __init__.py 0B setup.py 1KB资源评论
