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Adding contributing section based on Elasticsearch/CONTRIBUTING.md TODO - add testing docs (based on CI)1
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62 lines
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.. _implementation/details:
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======================
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Implementation Details
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======================
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The goal of an ``eland.DataFrame`` is to enable users who are familiar with ``pandas.DataFrame``
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to access, explore and manipulate data that resides in Elasticsearch.
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Ideally, all data should reside in Elasticsearch and not to reside in memory.
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This restricts the API, but allows access to huge data sets that do not fit into memory, and allows
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use of powerful Elasticsearch features such as aggregations.
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Pandas and 3rd Party Storage Systems
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------------------------------------
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Generally, integrations with [3rd party storage systems](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html)
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(SQL, Google Big Query etc.) involve accessing these systems and reading all external data into an
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in-core pandas data structure. This also applies to [Apache Arrow](https://arrow.apache.org/docs/python/pandas.html)
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structures.
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Whilst this provides access to data in these systems, for large datasets this can require significant
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in-core memory, and for systems such as Elasticsearch, bulk export of data can be an inefficient way
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of exploring the data.
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An alternative option is to create an API that proxies ``pandas.DataFrame``-like calls to Elasticsearch
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queries and operations. This could allow the Elasticsearch cluster to perform operations such as
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aggregations rather than exporting all the data and performing this operation in-core.
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Implementation Options
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----------------------
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An option would be to replace the ``pandas.DataFrame`` backend in-core memory structures with Elasticsearch
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accessors. This would allow full access to the ``pandas.DataFrame`` APIs. However, this has issues:
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* If a ``pandas.DataFrame`` instance maps to an index, typical manipulation of a ``pandas.DataFrame``
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may involve creating many derived ``pandas.DataFrame`` instances. Constructing an index per
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``pandas.DataFrame`` may result in many Elasticsearch indexes and a significant load on Elasticsearch.
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For example, ``df_a = df['a']`` should not require Elasticsearch indices ``df`` and ``df_a``
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* Not all ``pandas.DataFrame`` APIs map to things we may want to do in Elasticsearch. In particular,
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API calls that involve exporting all data from Elasticsearch into memory e.g. ``df.to_dict()``.
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* The backend ``pandas.DataFrame`` structures are not easily abstractable and are deeply embedded in
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the implementation.
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Another option is to create a ``eland.DataFrame`` API that mimics appropriate aspects of
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the ``pandas.DataFrame`` API. This resolves some of the issues above as:
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* ``df_a = df['a']`` could be implemented as a change to the Elasticsearch query used, rather
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than a new index
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* Instead of supporting the enitre ``pandas.DataFrame`` API we can support a subset appropriate for
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Elasticsearch. If addition calls are required, we could to create a ``eland.DataFrame._to_pandas()``
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method which would explicitly export all data to a ``pandas.DataFrame``
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* Creating a new ``eland.DataFrame`` API gives us full flexibility in terms of implementation. However,
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it does create a large amount of work which may duplicate a lot of the ``pandas`` code - for example,
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printing objects etc. - this creates maintenance issues etc.
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