Add futures readme.

talks/FrevaFutures/README.md

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+# The freva future Data API Architecture
+
+## Introduction
+
+The [freva](https://freva-clint.github.io/freva/) Future Data API is an
+innovative architecture designed to change the way datasets are registered,
+searched for, and generated.
+Instead of pre-computing and storing large sets of data, this architecture
+allows users to register *future* datasets—data that does not yet exist but
+can be generated on-demand. This system is especially useful for calculating
+variables that are derived from existing datasets or even creating entirely
+new datasets, such as applying recipes for climate model simulations.
+
+Utilizing freva's python API, Apache Solr, and MariaDB, the system efficiently
+manages these future datasets through a sophisticated workflow that is both
+resource-efficient and highly adaptable.
+
+> ``📝`` The code of the prototype can be found in the `futures` branch of
+         the [freva GitHub repository](https://github.com/FREVA-CLINT/freva/tree/futures)
+
+### Terminology: Why Call These "Futures"?
+
+In programming, a "Future" refers to a value that is not yet available but
+will be resolved at some point in the future. The concept aligns well with
+this architecture for the following reasons:
+
+1. **Deferred Execution**: Just like a future, the actual dataset does not
+    exist until demanded.
+2. **Resource Optimization**: Computing resources are utilized only when
+    there is a need for the dataset.
+3. **Reproducibility**: The existence of the notebook as a recipe ensures
+    that the data can be recreated, similar to how a future can be resolved
+    multiple times.
+4. **Dynamic Adaptation**: The ability to generate datasets on-the-fly makes
+    the system adaptive and responsive to user needs, much like how futures
+    are used for asynchronous operations.
+
+Thus, the term "futures" aptly encapsulates the architecture's essence of
+handling data that will be materialized on demand, making it efficient and
+forward-looking.
+
+
+## Architecture Components
+
+1. **User Interface**: Allows users to register and search for datasets.
+2. **Python API**: Facilitates user registration and dataset handling.
+3. **Apache Solr**: Stores dataset facets and Jupyter Notebook string
+    representation.
+4. **MariaDB**: Keeps a copy of the Jupyter Notebook content, code hash, and
+    data file locations.
+
+### Workflow
+
+1. Users register datasets by submitting Jupyter Notebooks containing the
+    data recipe. This can be done either by:
+
+    * Using pre defined templates where the `solr-parameters` and any
+      additional variables are passed to create the specific recipe for
+      this datasets.
+    * Creating a recipe from a *freva plugin* run that has already been
+      applied.
+
+2. The Python API reads cells tagged with `solr-parameters` and extracts
+    variables as Apache Solr facets making the dataset searchable via apache
+    solr (the `freva databrowser`).
+3. The string representation of the Jupyter Notebook and the extracted facets
+    are stored in Apache Solr.
+4. The notebook content, along with a hash representing the notebook code,
+    is stored in MariaDB.
+5. MariaDB also maintains a table containing a JSON field for the location of
+    the data files that the notebook would generate.
+
+### On-Demand Data Generation
+
+1. Users search for datasets using the API.
+2. When a dataset is requested for use, the corresponding notebook is
+    executed on-demand.
+3. The generated data replaces the "future" dataset representation in
+    both Apache Solr and MariaDB.
+
+## Use Cases
+
+- **Derived Variables**: The architecture allows for easy computation of
+    variables that are derivatives of existing datasets.
+- **New Datasets**: It can also generate completely new datasets, such as
+    climate model simulations, by applying specific recipes encapsulated
+    in Jupyter Notebooks.
+
+
+## Examples
+There are two notebooks outlining the anticipated usage of the proposed
+mechanism [FuturesExample.ipynb] demonstrates the usage from python while
+[FuturesExample-Bash.ipynb] explains the same scenario for the `freva`
+command line interface.