API Reference¶

`intake_elasticsearch.elasticsearch_table.ElasticSearchTableSource`(…)	Data source which executes arbitrary queries on ElasticSearch
`intake_elasticsearch.elasticsearch_seq.ElasticSearchSeqSource`(query)	Data source which executes arbitrary queries on ElasticSearch

class intake_elasticsearch.elasticsearch_table.ElasticSearchTableSource(*args, **kwargs)[source]¶

Data source which executes arbitrary queries on ElasticSearch

This is the tabular reader: will return dataframes. Nested return items will become dict-like objects in the output.

Parameters

query: str

Query to execute. Can either be in Lucene single-line format, or a JSON structured query (presented as text)

npartitions: int

Split query into this many sections. If one, will not split.

qargs: dict

Further parameters to pass to the query, such as set of indexes to consider, filtering, ordering. See http://elasticsearch-py.readthedocs.io/en/master/api.html#elasticsearch.Elasticsearch.search

es_kwargs: dict

Settings for the ES connection, e.g., a simple local connection may be {'host': 'localhost', 'port': 9200}. Other keywords to the Plugin that end up here and are material:

scroll: str: how long the query is live for, default '100m'
size: int: the paging size when downloading, default 1000.

metadata: dict

Extra information for this source.

Attributes

cache_dirs
classname
datashape
description
has_been_persisted
hvplot: Returns a hvPlot object to provide a high-level plotting API.
is_persisted
plot: Returns a hvPlot object to provide a high-level plotting API.
plots: List custom associated quick-plots

Methods

`close`(self)	Close open resources corresponding to this data source.
`discover`(self)	Open resource and populate the source attributes.
`export`(self, path, \\kwargs)	Save this data for sharing with other people
`persist`(self[, ttl])	Save data from this source to local persistent storage
`read`(self)	Read all data in one go
`read_chunked`(self)	Return iterator over container fragments of data source
`read_partition`(self, i)	Return a part of the data corresponding to i-th partition.
`to_dask`(self)	Turn into dask.dataframe
`to_spark`(self)	Provide an equivalent data object in Apache Spark
`yaml`(self[, with_plugin])	Return YAML representation of this data-source

get_persisted
set_cache_dir

to_dask(self)[source]¶: Turn into dask.dataframe

class intake_elasticsearch.elasticsearch_seq.ElasticSearchSeqSource(query, npartitions=1, qargs={}, metadata={}, **es_kwargs)[source]¶