PyPI 官网下载 | dask_elk-0.1.0.tar.gz

[](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)