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Backends are modular to support user contribution. Backend contributors are encouraged to offer custom backend abstract classes and backend implementations. A contributed backend abstract class may extend another backend to inherit the properties of the parent. In order to be compatible with DSI core middleware, backends should create an interface to Python built-in data structures or data structures from the Python ``collections`` library. Backend extensions will be accepted conditional to the extention of ``backends/tests`` to demonstrate new Backend capability. We can not accept pull requests that are not tested. + +Note that any contributed backends or extensions must include unit tests in ``backends/tests`` to demonstrate the new Backend capability. + +.. figure:: BackendClassHierarchy.png + :alt: Figure depicting the current backend class hierarchy. + :class: with-shadow + :scale: 100% + + Figure depicts the current DSI backend class hierarchy. + +.. automodule:: dsi.backends.filesystem + :members: + +.. automodule:: dsi.backends.sqlite + :members: + +.. automodule:: dsi.backends.gufi + :members: + +.. automodule:: dsi.backends.parquet + :members: diff --git a/_sources/core.rst.txt b/_sources/core.rst.txt new file mode 100644 index 00000000..c6a52394 --- /dev/null +++ b/_sources/core.rst.txt @@ -0,0 +1,87 @@ +Core +==== + +The DSI Core middleware defines the Terminal concept. An instantiated Terminal is the human/machine DSI interface. The person setting up a Core Terminal only needs to know how they want to ask questions, and what metadata they want to ask questions about. If they don’t see an option to ask questions the way they like, or they don’t see the metadata they want to ask questions about, then they should ask a Driver Contributor or a Plugin Contributor, respectively. + +A Core Terminal is a home for Plugins (Readers/Writers), and an interface for Backends. A Core Terminal is instantiated with a set of default Plugins and Backends, but they must be loaded before a user query is attempted. Here's an example of how you might work with DSI using an interactive Python interpreter for your data science workflows:: + +>>> from dsi.core import Terminal +>>> a=Terminal() +>>> a.list_available_modules('plugin') +>>> # ['Bueno', 'Hostname', 'SystemKernel'] +>>> a.load_module('plugin','Bueno','reader',filename='./data/bueno.data') +>>> # Bueno plugin reader loaded successfully. +>>> a.load_module('plugin','Hostname','writer') +>>> # Hostname plugin writer loaded successfully. +>>> a.list_loaded_modules() +>>> # {'writer': [], +>>> # 'reader': [], +>>> # 'front-end': [], +>>> # 'back-end': []} + + +At this point, you might decide that you are ready to collect data for inspection. It is possible to utilize DSI Backends to load additional metadata to supplement your Plugin metadata, but you can also sample Plugin data and search it directly. + + +The process of transforming a set of Plugin writers and readers into a querable format is called transloading. A DSI Core Terminal has a ``transload()`` method which may be called to execute all Plugins at once:: + +>>> a.transload() +>>> a.active_metadata +>>> # OrderedDict([('uid', [1000]), ('effective_gid', [1000]), ('moniker', ['qwofford'])... + +Once a Core Terminal has been transloaded, no further Plugins may be added. However, the transload method can be used to samples of each plugin as many times as you like:: + +>>> a.transload() +>>> a.transload() +>>> a.transload() +>>> a.active_metadata +>>> # OrderedDict([('uid', [1000, 1000, 1000, 1000]), ('effective_gid', [1000, 1000, 1000... + +If you perform data science tasks using Python, it is not necessary to create a DSI Core Terminal front-end because the data is already in a Python data structure. If your data science tasks can be completed in one session, it is not required to interact with DSI Backends. However, if you do want to save your work, you can load a DSI Backend with a back-end function:: + +>>> a.list_available_modules('backend') +>>> # ['Gufi', 'Sqlite', 'Parquet'] +>>> a.load_module('backend','Parquet','back-end',filename='parquet.data') +>>> # Parquet backend loaded successfully. +>>> a.list_loaded_modules() +>>> # {'writer': [], +>>> # 'reader': [], +>>> # 'front-end': [], +>>> # 'back-end': []} +>>> a.artifact_handler(interaction_type='put') + +The contents of the active DSI Core Terminal metadata storage will be saved to a Parquet object at the path you provided at module loading time. + +It is possible that you prefer to perform data science tasks using a higher level abstraction than Python itself. This is the purpose of the DSI Driver front-end functionality. Unlike Plugins, Drivers can be added after the initial ``transload()`` operation has been performed:: + +>>> a.load_module('backend','Parquet','front-end',filename='parquet.data') +>>> # Parquet backend front-end loaded successfully. +>>> a.list_loaded_modules() +>>> # {'writer': [], +>>> # 'reader': [], +>>> # 'front-end': [], +>>> # 'back-end': []} + +Any front-end may be used, but in this case the Parquet backend has a front-end implementation which builds a jupyter notebook from scratch that loads your metadata collection into a Pandas Dataframe. The Parquet front-end will then launch the Jupyter Notebook to support an interactive data science workflow:: + +>>> a.artifact_handler(interaction_type='inspect') +>>> # Writing Jupyter notebook... +>>> # Opening Jupyter notebook... + +.. image:: jupyter_frontend.png + :scale: 33% + +You can then close your Jupyter notebook, ``transload()`` additionally to increase your sample size, and use the interface to explore more data. + +Although this demonstration only used one Plugin per Plugin functionality, any number of plugins can be added to collect an arbitrary amount of queriable metadata:: + +>>> a.load_module('plugin','SystemKernel','writer') +>>> # SystemKernel plugin writer loaded successfully +>>> a.list_loaded_modules() +>>> # {'writer': [, ], +>>> # 'reader': [], +>>> # 'front-end': [], +>>> # 'back-end': []} + +.. automodule:: dsi.core + :members: diff --git a/_sources/examples.rst.txt b/_sources/examples.rst.txt new file mode 100644 index 00000000..ff0d5a3d --- /dev/null +++ b/_sources/examples.rst.txt @@ -0,0 +1,107 @@ + +DSI Examples +============ + +PENNANT mini-app +---------------- + +`PENNANT`_ is an unstructured mesh physics mini-application developed at Los Alamos National Laboratory +for advanced architecture research. +It contains mesh data structures and a few +physics algorithms from radiation hydrodynamics and serves as an example of +typical memory access patterns for an HPC simulation code. + +This DSI PENNANT example is used to show a common use case: create and query a set of metadata derived from an ensemble of simulation runs. The example GitHub directory includes 10 PENNANT runs using the PENNANT *Leblanc* test problem. + +In the first step, a python script is used to parse the slurm output files and create a CSV (comma separated value) file with the output metadata. + +.. code-block:: unixconfig + + ./parse_slurm_output.py --testname leblanc + + +.. literalinclude:: ../examples/pennant/parse_slurm_output.py + +A second python script, + +.. code-block:: unixconfig + + ./create_and_query_dsi_db.py --testname leblanc + + +reads in the CSV file and creates a database: + +.. code-block:: python + + """ + Creates the DSI db from the csv file + """ + """ + This script reads in the csv file created from parse_slurm_output.py. + Then it creates a DSI db from the csv file and performs a query. + """ + + import argparse + import sys + from dsi.backends.sqlite import Sqlite, DataType + + isVerbose = True + + def import_pennant_data(test_name): + csvpath = 'pennant_' + test_name + '.csv' + dbpath = 'pennant_' + test_name + '.db' + store = Sqlite(dbpath) + store.put_artifacts_csv(csvpath, "rundata", isVerbose=isVerbose) + store.close() + # No error implies success + + +Finally, the database is queried: + +.. code-block:: python + + """ + Performs a sample query on the DSI db + """ + def test_artifact_query(test_name): + dbpath = "pennant_" + test_name + ".db" + store = Sqlite(dbpath) + _ = store.get_artifact_list(isVerbose=isVerbose) + data_type = DataType() + data_type.name = "rundata" + query = "SELECT * FROM " + str(data_type.name) + \ + " where hydro_cycle_run_time > 0.006" + print("Running Query", query) + result = store.sqlquery(query) + store.export_csv(result, "pennant_query.csv") + store.close() + + if __name__ == "__main__": + """ The testname argument is required """ + parser = argparse.ArgumentParser() + parser.add_argument('--testname', help='the test name') + args = parser.parse_args() + test_name = args.testname + if test_name is None: + parser.print_help() + sys.exit(0) + + import_pennant_data(test_name) + test_artifact_query(test_name) + +Resulting in the output of the query: + +.. figure:: example-pennant-output.png + :alt: Screenshot of computer program output. + :class: with-shadow + + + The output of the PENNANT example. + + + +Wildfire Dataset +---------------- + + +.. _PENNANT: https://github.com/lanl/PENNANT diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 00000000..b50e5e8b --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,26 @@ +.. DSI documentation master file, created by + sphinx-quickstart on Fri Apr 14 14:04:07 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +The Data Science Infrastructure Project (DSI) +============================================= + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + introduction + installation + plugins + backends + core + tiers + examples + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/_sources/installation.rst.txt b/_sources/installation.rst.txt new file mode 100644 index 00000000..6ac2a09b --- /dev/null +++ b/_sources/installation.rst.txt @@ -0,0 +1,38 @@ +Quick Start: Installation +========================= + +#. If this is the first time using DSI, start by creating a DSI virtual environment with a name of your choice, e.g., **mydsi**: + + .. code-block:: unixconfig + + python -m venv mydsi + +#. Then activate the environment (start here if you already have a DSI virtual environment): + + .. code-block:: unixconfig + + source mydsi/bin/activate + +#. Go down into the project space root and use pip to install dsi: + + .. code-block:: unixconfig + + cd dsi + pip install . + + +#. [Optional] If you are running DSI unit tests, you may need other packages: + + .. code-block:: unixconfig + + pip install pytest gitpython coverage-badge pytest-cov . + + Plus ``pip install`` any other packages that your unit tests may need. + +#. [Optional] If you are updating the GitHub pages documentation, see `DSI Documentation README `_ for additional python packages needed. + +#. When you've completed work, deactivate the environment with: + + .. code-block:: unixconfig + + deactivate diff --git a/_sources/introduction.rst.txt b/_sources/introduction.rst.txt new file mode 100644 index 00000000..38724871 --- /dev/null +++ b/_sources/introduction.rst.txt @@ -0,0 +1,75 @@ + + + +The goal of the Data Science Infrastructure Project (DSI) is to manage data through metadata capture and curation. DSI capabilities can be used to develop workflows to support management of simulation data, AI/ML approaches, ensemble data, and other sources of data typically found in scientific computing. DSI infrastructure is designed to be flexible and with these considerations in mind: + +- Data management is subject to strict, POSIX-enforced, file security. +- DSI capabilities support a wide range of common metadata queries. +- DSI interfaces with multiple database technologies and archival storage options. +- Query-driven data movement is supported and is transparent to the user. +- The DSI API can be used to develop user-specific workflows. + +.. figure:: data_lifecycle.png + :alt: Figure depicting the data life cycle + :class: with-shadow + :scale: 50% + + A depiction of data life cycle can be seen here. The Data Science Infrastructure API supports the user to manage the life cycle aspects of their data. + +DSI system design has been driven by specific use cases, both AI/ML and more generic usage. These use cases can often be generalized to user stories and needs that can be addressed by specific features, e.g., flexible, human-readable query capabilities. DSI uses Object Oriented design principles to encourage modularity and to support contributions by the user community. The DSI API is Python-based. + +Implementation Overview +======================= + +The DSI API is broken into three main categories: + +- Plugins: these are frontend capabilities that will be commonly used by the generic DSI user. These include readers and writers. +- Backends: these are used to interact with storage devices and other ways of moving data. +- DSI Core: the *middleware* that contains the basic functionality to use the DSI API. + +Plugin Abstract Classes +----------------------- + +Plugins transform an arbitrary data source into a format that is compatible with the DSI core. The parsed and queryable attributes of the data are called *metadata* -- data about the data. Metadata share the same security profile as the source data. + +Plugins can operate as data readers or data writers. A simple data reader might parse an application's output file and place it into a core-compatible data structure such as Python built-ins and members of the popular Python ``collection`` module. A simple data writer might execute an application to supplement existing data and queryable metadata, e.g., adding locations of outputs data or plots after running an analysis workflow. + +Plugins are defined by a base abstract class, and support child abstract classes which inherit the properties of their ancestors. + +Currently, DSI has the following readers: + + - CSV file reader: reads in comma separated value (CSV) files. + - Bueno reader: can be used to capture performance data from `Bueno `_. + +.. figure:: PluginClassHierarchy.png + :alt: Figure depicting the current plugin class hierarchy. + :class: with-shadow + :scale: 100% + + Figure depicting the current DSI plugin class hierarchy. + +Backend Abstract Classes +------------------------ + +Backends are an interface between the core and a storage medium. +Backends are designed to support a user-needed functionality. Given a set of user metadata captured by a DSI frontend, a typical functionality needed by DSI users is to query that metadata by SQL query. Because the files associated with the queryable metadata may be spread across filesystems and security domains, a supporting backend is required to assemble query results and present them to the DSI core for transformation and return. + +.. figure:: user_story.png + :alt: This figure depicts a user asking a typical query on the user's metadata + :class: with-shadow + :scale: 50% + + In this typical **user story**, the user has metadata about their data stored in DSI storage of some type. The user needs to extract all files with the variable **foo** above a specific threshold. DSI backends query the DSI metadata store to locate and return all such files. + +Current DSI backends include: + +- Sqlite: Python based SQL database and backend; the default DSI API backend. +- GUFI: the Grand Unified File Index system `Grand Unified File-Index `_ ; developed at LANL, GUFI is a fast, secure metadata search across a filesystem accessible to both privileged and unprivileged users. +- Parquet: a columnar storage format for `Apache Hadoop `_. + +DSI Core +-------- + +DSI basic functionality is contained within the middleware known as the *core*. The DSI core is focused on delivering user-queries on unified metadata which can be distributed across many files and security domains. DSI currently supports Linux, and is tested on RedHat- and Debian-based distributions. The DSI core is a home for DSI Plugins and an interface for DSI Backends. + +Core Documentation diff --git a/_sources/plugins.rst.txt b/_sources/plugins.rst.txt new file mode 100644 index 00000000..83589a8e --- /dev/null +++ b/_sources/plugins.rst.txt @@ -0,0 +1,23 @@ +Plugins +======= +Plugins connect data-producing applications to DSI core functionalities. Plugins have *writers* or *readers* functions. A Plugin reader function deals with existing data files or input streams. A Plugin writer deals with generating new data. Plugins are modular to support user contribution. + +Plugin contributors are encouraged to offer custom Plugin abstract classes and Plugin implementations. A contributed Plugin abstract class may extend another plugin to inherit the properties of the parent. In order to be compatible with DSI core, Plugins should produce data in Python built-in data structures or data structures sourced from the Python ``collections`` library. + +Note that any contributed plugins or extensions must include unit tests in ``plugins/tests`` to demonstrate the new Plugin capability. + +.. figure:: PluginClassHierarchy.png + :alt: Figure depicting the current plugin class hierarchy. + :class: with-shadow + :scale: 100% + + Figure depicts the current DSI plugin class hierarchy. + +.. automodule:: dsi.plugins.plugin + :members: + +.. automodule:: dsi.plugins.metadata + :members: + +.. automodule:: dsi.plugins.env + :members: diff --git a/_sources/tiers.rst.txt b/_sources/tiers.rst.txt new file mode 100644 index 00000000..3583370f --- /dev/null +++ b/_sources/tiers.rst.txt @@ -0,0 +1,50 @@ +DSI Development Tiers +===================== + +The elements of the DSI project can be broken into Tiers. Tier 1 is the DSI Core and associated DSI data services. The DSI data services include the functionality to store and retrieve user metadata in DSI accessible storage. Tier 2 consists of DSI data workflows. These are generally customized implementations of DSI for particular science applications. Tier 2 workflows will often drive new capabilities within the DSI core. + +DSI Tier 1 +---------- + +DSI core functionalities +^^^^^^^^^^^^^^^^^^^^^^^^ + +* DSI software/API (v1.0) released via GitHub +* Can be used on its own or as part of a data service workflow +* Provides an API for users/services + +.. list-table:: Current DSI Core Functionalities + :widths: 25 25 50 + :header-rows: 1 + + * - Functionality + - DSI element + - Description + + * - Ingest data + - Plugins: Readers + - Ability to read in data; users can create data-specific readers + + * - Write data + - Plugins: Writers + - Ability to write data; users can create data-specific writers + +DSI data service +^^^^^^^^^^^^^^^^ + +* DSI software combined with storage resources +* Provides a generalized institutional data management service, e.g. readers, writers that can be used to store and update metadata on DSI storage resources +* DSI team is implementing the data service at LANL with targeted data storage; however, the storage could be set up anywhere + + +DSI Tier 2 +---------- + +* Customized implementations of DSI for particular applications/workflows +* Higher level features/capabilities +* Can be implemented locally or connected to a DSI institutional service +* Typically developed by experienced DSI users/developers in consultation with an application team +* May incorporate other tools such as customized GUIs or Cinema +* Examples + * Ensemble analysis such as the Wildfire data + * AI/ML workflows diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 00000000..81415803 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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// [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/backends.html b/backends.html new file mode 100644 index 00000000..fa05b564 --- /dev/null +++ b/backends.html @@ -0,0 +1,325 @@ + + + + + + + Backends — DSI 0.0.0 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Backends

+

Backends connect users to DSI Core middleware and backends allow DSI middleware data structures to read and write to persistent external storage. Backends are modular to support user contribution. Backend contributors are encouraged to offer custom backend abstract classes and backend implementations. A contributed backend abstract class may extend another backend to inherit the properties of the parent. In order to be compatible with DSI core middleware, backends should create an interface to Python built-in data structures or data structures from the Python collections library. Backend extensions will be accepted conditional to the extention of backends/tests to demonstrate new Backend capability. We can not accept pull requests that are not tested.

+

Note that any contributed backends or extensions must include unit tests in backends/tests to demonstrate the new Backend capability.

+
+Figure depicting the current backend class hierarchy. +
+

Figure depicts the current DSI backend class hierarchy.

+
+
+
+
+class dsi.backends.sqlite.Artifact
+

Primary Artifact class that holds database schema in memory. +An Artifact is a generic construct that defines the schema for metadata that +defines the tables inside of SQL

+
+ +
+
+class dsi.backends.sqlite.Sqlite(filename)
+

Primary storage class, inherits sql class

+
+
+check_type(text)
+

Tests input text and returns a predicted compatible SQL Type +text: text string +return: string description of a SQL data type

+
+ +
+
+export_csv(rquery, tname, fname, isVerbose=False)
+

Function that outputs a csv file of a return query, not the query itself

+

rquery: return of an already called query output

+

tname: name of the table for (all) columns to export

+

fname: target filename (including path) that will output the return query as a csv file

+

return: none

+
+ +
+
+export_csv_query(query, fname, isVerbose=False)
+

Function that outputs a csv file of a return query, given an initial query.

+

query: raw SQL query to be executed on current table

+

fname: target filename (including path) that will output the return query as a csv file

+

return: none

+
+ +
+
+get_artifact_list(isVerbose=False)
+

Function that returns a list of all of the Artifact names (represented as sql tables)

+

return: list of Artifact names

+
+ +
+
+put_artifact_type(types, isVerbose=False)
+

Primary class for defining metadata Artifact schema.

+
+
types: DataType derived class that defines the string name, properties

(named SQL type), and units for each column in the schema.

+
+
+

return: none

+
+ +
+
+put_artifacts(collection, isVerbose=False)
+

Primary class for insertion of Artifact metadata into a defined schema

+
+
Artifacts: DataType derived class that has a regular structure of a defined schema,

filled with rows to insert.

+
+
+

return: none

+
+ +
+
+put_artifacts_csv(fname, tname, isVerbose=False)
+

Function for insertion of Artifact metadata into a defined schema by using a CSV file, +where the first row of the CSV contains the column names of the schema. Any rows +thereafter contain data to be inserted. Data types are automatically assigned based on +typecasting and default to a string type if none can be found.

+

fname: filepath to the .csv file to be read and inserted into the database

+

tname: String name of the table to be inserted

+

return: none

+
+ +
+
+query_fctime(operator, ctime, isVerbose=False)
+

Function that queries file creation times within the filesystem metadata store

+

operator: operator input GT, LT, EQ as a modifier for a creation time search

+

ctime: creation time in POSIX format, see the utils dateToPosix conversion function

+

return: query list of filenames matching the creation time criteria with modifiers

+
+ +
+
+query_fname(name, isVerbose=False)
+

Function that queries filenames within the filesystem metadata store

+

name: string name of a subsection of a filename to be searched

+

return: query list of filenames matching name string

+
+ +
+
+query_fsize(operator, size, isVerbose=False)
+

Function that queries ranges of file sizes within the filesystem metadata store

+

operator: operator input GT, LT, EQ as a modifier for a filesize search

+

size: size in bytes

+

return: query list of filenames matching filesize criteria with modifiers

+
+ +
+
+sqlquery(query, isVerbose=False)
+

Function that provides a direct sql query passthrough to the database.

+

query: raw SQL query to be executed on current table

+

return: raw sql query list that contains result of the original query

+
+ +
+ +
+
+class dsi.backends.gufi.Gufi(prefix, index, dbfile, table, column, verbose=False)
+

GUFI Datastore

+
+
+get_artifacts(query)
+

Retrieves GUFI’s metadata joined with a dsi database +query: an sql query into the dsi_entries table

+
+ +
+
+isVerbose = False
+

prefix: prefix to GUFI commands +index: directory with GUFI indexes +dbfile: sqlite db file from DSI +table: table name from the DSI db we want to join on +column: column name from the DSI db to join on

+
+ +
+ +
+
+class dsi.backends.parquet.Parquet(filename, **kwargs)
+

Support for a Parquet back-end.

+

Parquet is a convenient format when metadata are larger than SQLite supports.

+
+
+get_artifacts()
+

Get Parquet data from filename.

+
+ +
+
+static get_cmd_output(cmd: list) → str
+

Runs a given command and returns the stdout if successful.

+

If stderr is not empty, an exception is raised with the stderr text.

+
+ +
+
+inspect_artifacts(collection, interactive=False)
+

Populate a Jupyter notebook with tools required to look at Parquet data.

+
+ +
+
+put_artifacts(collection)
+

Put artifacts into file at filename path.

+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/core.html b/core.html new file mode 100644 index 00000000..4ce80b5b --- /dev/null +++ b/core.html @@ -0,0 +1,279 @@ + + + + + + + Core — DSI 0.0.0 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Core

+

The DSI Core middleware defines the Terminal concept. An instantiated Terminal is the human/machine DSI interface. The person setting up a Core Terminal only needs to know how they want to ask questions, and what metadata they want to ask questions about. If they don’t see an option to ask questions the way they like, or they don’t see the metadata they want to ask questions about, then they should ask a Driver Contributor or a Plugin Contributor, respectively.

+

A Core Terminal is a home for Plugins (Readers/Writers), and an interface for Backends. A Core Terminal is instantiated with a set of default Plugins and Backends, but they must be loaded before a user query is attempted. Here’s an example of how you might work with DSI using an interactive Python interpreter for your data science workflows:

+
>>> from dsi.core import Terminal
+>>> a=Terminal()
+>>> a.list_available_modules('plugin')
+>>> # ['Bueno', 'Hostname', 'SystemKernel']
+>>> a.load_module('plugin','Bueno','reader',filename='./data/bueno.data')
+>>> # Bueno plugin reader loaded successfully.
+>>> a.load_module('plugin','Hostname','writer')
+>>> # Hostname plugin writer loaded successfully.
+>>> a.list_loaded_modules()
+>>> # {'writer': [<dsi.plugins.env.Hostname object at 0x7f21232474d0>],
+>>> #  'reader': [<dsi.plugins.env.Bueno object at 0x7f2123247410>],
+>>> #  'front-end': [],
+>>> #   'back-end': []}
+
+
+

At this point, you might decide that you are ready to collect data for inspection. It is possible to utilize DSI Backends to load additional metadata to supplement your Plugin metadata, but you can also sample Plugin data and search it directly.

+

The process of transforming a set of Plugin writers and readers into a querable format is called transloading. A DSI Core Terminal has a transload() method which may be called to execute all Plugins at once:

+
>>> a.transload()
+>>> a.active_metadata
+>>> # OrderedDict([('uid', [1000]), ('effective_gid', [1000]), ('moniker', ['qwofford'])...
+
+
+

Once a Core Terminal has been transloaded, no further Plugins may be added. However, the transload method can be used to samples of each plugin as many times as you like:

+
>>> a.transload()
+>>> a.transload()
+>>> a.transload()
+>>> a.active_metadata
+>>> # OrderedDict([('uid', [1000, 1000, 1000, 1000]), ('effective_gid', [1000, 1000, 1000...
+
+
+

If you perform data science tasks using Python, it is not necessary to create a DSI Core Terminal front-end because the data is already in a Python data structure. If your data science tasks can be completed in one session, it is not required to interact with DSI Backends. However, if you do want to save your work, you can load a DSI Backend with a back-end function:

+
>>> a.list_available_modules('backend')
+>>> # ['Gufi', 'Sqlite', 'Parquet']
+>>> a.load_module('backend','Parquet','back-end',filename='parquet.data')
+>>> # Parquet backend loaded successfully.
+>>> a.list_loaded_modules()
+>>> # {'writer': [<dsi.plugins.env.Hostname object at 0x7f21232474d0>],
+>>> #  'reader': [<dsi.plugins.env.Bueno object at 0x7f2123247410>],
+>>> #  'front-end': [],
+>>> #   'back-end': [<dsi.backends.parquet.Parquet object at 0x7f212325a110>]}
+>>> a.artifact_handler(interaction_type='put')
+
+
+

The contents of the active DSI Core Terminal metadata storage will be saved to a Parquet object at the path you provided at module loading time.

+

It is possible that you prefer to perform data science tasks using a higher level abstraction than Python itself. This is the purpose of the DSI Driver front-end functionality. Unlike Plugins, Drivers can be added after the initial transload() operation has been performed:

+
>>> a.load_module('backend','Parquet','front-end',filename='parquet.data')
+>>> # Parquet backend front-end loaded successfully.
+>>> a.list_loaded_modules()
+>>> # {'writer': [<dsi.plugins.env.Hostname object at 0x7fce3c612b50>],
+>>> # 'reader': [<dsi.plugins.env.Bueno object at 0x7fce3c622110>],
+>>> # 'front-end': [<dsi.backends.parquet.Parquet object at 0x7fce3c622290>],
+>>> # 'back-end': [<dsi.backends.parquet.Parquet object at 0x7fce3c622650>]}
+
+
+

Any front-end may be used, but in this case the Parquet backend has a front-end implementation which builds a jupyter notebook from scratch that loads your metadata collection into a Pandas Dataframe. The Parquet front-end will then launch the Jupyter Notebook to support an interactive data science workflow:

+
>>> a.artifact_handler(interaction_type='inspect')
+>>> # Writing Jupyter notebook...
+>>> # Opening Jupyter notebook...
+
+
+_images/jupyter_frontend.png +

You can then close your Jupyter notebook, transload() additionally to increase your sample size, and use the interface to explore more data.

+

Although this demonstration only used one Plugin per Plugin functionality, any number of plugins can be added to collect an arbitrary amount of queriable metadata:

+
>>> a.load_module('plugin','SystemKernel','writer')
+>>> # SystemKernel plugin writer loaded successfully
+>>> a.list_loaded_modules()
+>>> # {'writer': [<dsi.plugins.env.Hostname object at 0x7fce3c612b50>, <dsi.plugins.env.SystemKernel object at 0x7fce68519250>],
+>>> # 'reader': [<dsi.plugins.env.Bueno object at 0x7fce3c622110>],
+>>> # 'front-end': [<dsi.backends.parquet.Parquet object at 0x7fce3c622290>],
+>>> # 'back-end': [<dsi.backends.parquet.Parquet object at 0x7fce3c622650>]}
+
+
+
+
+class dsi.core.Terminal
+

An instantiated Terminal is the DSI human/machine interface.

+

Terminals are a home for Plugins and an interface for Backends. Backends may be +front-ends or back-ends. Plugins may be Writers or readers. See documentation +for more information.

+
+
+add_external_python_module(mod_type, mod_name, mod_path)
+

Adds an external, meaning not from the DSI repo, Python module to the module_collection.

+

Afterwards, load_module can be used to load a DSI module from the added Python module. +Note: mod_type is needed because each Python module only implements plugins or backends.

+

For example,

+

term = Terminal() +term.add_external_python_module(‘plugin’, ‘my_python_file’,

+
+

‘/the/path/to/my_python_file.py’)

+
+

term.load_module(‘plugin’, ‘MyPlugin’, ‘reader’)

+

term.list_loaded_modules() # includes MyPlugin

+
+ +
+
+artifact_handler(interaction_type, **kwargs)
+

Store or retrieve using all loaded DSI Backends with storage functionality.

+

A DSI Core Terminal may load zero or more Backends with storage functionality. +Calling artifact_handler will execute all back-end functionality currently loaded, given +the provided interaction_type.

+
+ +
+
+list_available_modules(mod_type)
+

List available DSI modules of an arbitrary module type.

+

This method is useful for Core Terminal setup. Plugin and Backend type DSI modules +are supported, but this getter can be extended to support any new DSI module +types which are added. Note: self.VALID_MODULES refers to _DSI_ Modules +however, DSI Modules are classes, hence the naming idiosynchrocies below.

+
+ +
+
+list_loaded_modules()
+

List DSI modules which have already been loaded.

+

These Plugins and Backends are active or ready to execute a post-processing task.

+
+ +
+
+load_module(mod_type, mod_name, mod_function, **kwargs)
+

Load a DSI module from the available Plugin and Backend module collection.

+

DSI modules may be loaded which are not explicitly listed by the list_available_modules. +This flexibility ensures that advanced users can access higher level abstractions. +We expect most users will work with module implementations rather than templates, but +but all high level class abstractions are accessible with this method.

+
+ +
+
+transload(**kwargs)
+

Transloading signals to the DSI Core Terminal that Plugin set up is complete.

+

A DSI Core Terminal must be transloaded before queries, metadata collection, or metadata +storage is possible. Transloading is the process of merging Plugin metadata from many +data sources to a single DSI Core Middleware data structure.

+
+ +
+
+unload_module(mod_type, mod_name, mod_function)
+

Unloads a DSI module from the active_modules collection

+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/examples.html b/examples.html new file mode 100644 index 00000000..0c08c454 --- /dev/null +++ b/examples.html @@ -0,0 +1,282 @@ + + + + + + + DSI Examples — DSI 0.0.0 documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

DSI Examples

+
+

PENNANT mini-app

+

PENNANT is an unstructured mesh physics mini-application developed at Los Alamos National Laboratory +for advanced architecture research. +It contains mesh data structures and a few +physics algorithms from radiation hydrodynamics and serves as an example of +typical memory access patterns for an HPC simulation code.

+

This DSI PENNANT example is used to show a common use case: create and query a set of metadata derived from an ensemble of simulation runs. The example GitHub directory includes 10 PENNANT runs using the PENNANT Leblanc test problem.

+

In the first step, a python script is used to parse the slurm output files and create a CSV (comma separated value) file with the output metadata.

+
./parse_slurm_output.py --testname leblanc
+
+
+
#!/usr/bin/env python3
+
+"""
+Parses the slurm output from PENNANT runs and creates a csv file
+"""
+
+import argparse
+import sys
+import re
+import glob
+
+def main():
+    """ A testname argument is required """
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--testname', help='the test name')
+    args = parser.parse_args()
+    testname = args.testname
+    if testname is None:
+        parser.print_help()
+        sys.exit(0)
+
+
+    """ The data is parsed from all of the .out files in the current directory """
+    for slurmoutput in glob.glob('*.out'):
+        with open(slurmoutput, 'r') as slurmout:
+            data = {}
+            data['testname'] = testname
+            for line in slurmout:
+                if "Running PENNANT" in line:
+                    match = re.match(r'Running PENNANT (.*)', line)
+                    version = match.group(1)
+                    data['version'] = version
+                elif "MPI PE(s)" in line:
+                    match = re.match(r'Running on (\d+) MPI PE\(s\)', line)
+                    pes = match.group(1)
+                    data['pes'] = int(pes)
+                elif "thread(s)" in line:
+                    match = re.match(r'Running on (\d+) thread\(s\).*', line)
+                    threads = match.group(1)
+                    data['threads'] = int(threads)
+                elif "total energy  =" in line:
+                    match = re.match(r'.*total energy  =\s+([^\s]+)', line)
+                    energy = match.group(1)
+                    data['total_energy'] = float(energy)
+                elif "internal =" in line and "kinetic =" in line:
+                    match = re.match(r'.*internal =\s+([^\s]+), kinetic =\s+([^\s\)]+).*', line)
+                    internal_energy = match.group(1)
+                    kinetic_energy = match.group(2)
+                    data['internal_energy'] = float(internal_energy)
+                    data['kinetic_energy'] = float(kinetic_energy)
+                elif "time  =" in line:
+                    match = re.match(r'.*time  =\s+([^\s,]+),.*', line)
+                    time = match.group(1)
+                    data['time'] = float(time)
+                elif "hydro cycle run time=" in line:
+                    match = re.match(r'.*hydro cycle run time=\s+([^\s]+).*', line)
+                    hydro_cycle_run_time = match.group(1)
+                    data['hydro_cycle_run_time'] = float(hydro_cycle_run_time)
+
+        """ The csv file is created and written to disk """
+        with open('pennant_' + testname + '.csv', 'a+') as pennant_out:
+            header = ""
+            row = ""
+            for key, val in data.items():
+                header += key + ","
+                row += str(val) + ","
+
+            header = header.rstrip(',')
+            row = row.rstrip(',')
+
+            if pennant_out.tell() == 0:
+                pennant_out.write(header + "\n")
+
+            pennant_out.write(row + "\n")
+
+if __name__ == '__main__':
+    main()
+
+
+
+
+

A second python script,

+
./create_and_query_dsi_db.py --testname leblanc
+
+
+

reads in the CSV file and creates a database:

+
"""
+Creates the DSI db from the csv file
+"""
+"""
+This script reads in the csv file created from parse_slurm_output.py.
+Then it creates a DSI db from the csv file and performs a query.
+"""
+
+import argparse
+import sys
+from dsi.backends.sqlite import Sqlite, DataType
+
+isVerbose = True
+
+def import_pennant_data(test_name):
+   csvpath = 'pennant_' + test_name + '.csv'
+   dbpath = 'pennant_' + test_name + '.db'
+   store = Sqlite(dbpath)
+   store.put_artifacts_csv(csvpath, "rundata", isVerbose=isVerbose)
+   store.close()
+   # No error implies success
+
+
+

Finally, the database is queried:

+
 """
+ Performs a sample query on the DSI db
+ """
+ def test_artifact_query(test_name):
+    dbpath = "pennant_" + test_name + ".db"
+    store = Sqlite(dbpath)
+    _ = store.get_artifact_list(isVerbose=isVerbose)
+    data_type = DataType()
+    data_type.name = "rundata"
+    query = "SELECT * FROM " + str(data_type.name) + \
+      " where hydro_cycle_run_time > 0.006"
+    print("Running Query", query)
+    result = store.sqlquery(query)
+    store.export_csv(result, "pennant_query.csv")
+    store.close()
+
+if __name__ == "__main__":
+    """ The testname argument is required """
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--testname', help='the test name')
+    args = parser.parse_args()
+    test_name = args.testname
+    if test_name is None:
+        parser.print_help()
+        sys.exit(0)
+
+    import_pennant_data(test_name)
+    test_artifact_query(test_name)
+
+
+

Resulting in the output of the query:

+
+Screenshot of computer program output. +
+

The output of the PENNANT example.

+
+
+
+
+

Wildfire Dataset

+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Triad National Security, LLC. All rights reserved..

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 00000000..9204d236 --- /dev/null +++ b/genindex.html @@ -0,0 +1,424 @@ + + + + + + Index — DSI 0.0.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

+ +
+ A + | C + | D + | E + | G + | H + | I + | L + | M + | P + | Q + | S + | T + | U + +
+

A

+ + + +
+ +

C

+ + +
+ +

D

+ + + +
    +
  • + dsi.backends.filesystem + +
    • +
  • + dsi.backends.gufi + +
    • +
  • + dsi.backends.parquet + +
    • +
  • + dsi.backends.sqlite + +
    • +
    +
  • + dsi.core + +
    • +
  • + dsi.plugins.env + +
    • +
  • + dsi.plugins.metadata + +
    • +
  • + dsi.plugins.plugin + +
    • +
+ +

E

+ + + +
+ +

G

+ + + +
+ +

H

+ + +
+ +

I

+ + + +
+ +

L

+ + + +
+ +

M

+ + +
+ +

P

+ + + +
+ +

Q

+ + + +
+ +

S

+ + + +
+ +

T

+ + + +
+ +

U

+ + +
+ + + +
+
+
+ +
+ +
+

© Copyright 2023, Triad National Security, LLC. All rights reserved..

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..d398b8db --- /dev/null +++ b/index.html @@ -0,0 +1,165 @@ + + + + + + + The Data Science Infrastructure Project (DSI) — DSI 0.0.0 documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+
+ +
+ +
+

© Copyright 2023, Triad National Security, LLC. All rights reserved..

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/installation.html b/installation.html new file mode 100644 index 00000000..9ffa10e9 --- /dev/null +++ b/installation.html @@ -0,0 +1,147 @@ + + + + + + + Quick Start: Installation — DSI 0.0.0 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Quick Start: Installation

+
    +

  1. If this is the first time using DSI, start by creating a DSI virtual environment with a name of your choice, e.g., mydsi:

    +
    python -m venv mydsi
+
    +
    +
    2. +

  2. Then activate the environment (start here if you already have a DSI virtual environment):

    +
    source mydsi/bin/activate
+
    +
    +
    3. +

  3. Go down into the project space root and use pip to install dsi:

    +
    cd dsi
+pip install .
+
    +
    +
    4. +

  4. [Optional] If you are running DSI unit tests, you may need other packages:

    +
    pip install pytest gitpython coverage-badge pytest-cov .
+
    +
    +

    Plus pip install any other packages that your unit tests may need.

    +
    5. +

  5. [Optional] If you are updating the GitHub pages documentation, see DSI Documentation README for additional python packages needed.

    6. +

  6. When you’ve completed work, deactivate the environment with:

    +
    deactivate
+
    +
    +
    7. +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/introduction.html b/introduction.html new file mode 100644 index 00000000..90a29ade --- /dev/null +++ b/introduction.html @@ -0,0 +1,184 @@ + + + + + + + Implementation Overview — DSI 0.0.0 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

The goal of the Data Science Infrastructure Project (DSI) is to manage data through metadata capture and curation. DSI capabilities can be used to develop workflows to support management of simulation data, AI/ML approaches, ensemble data, and other sources of data typically found in scientific computing. DSI infrastructure is designed to be flexible and with these considerations in mind:

+
    +

  • Data management is subject to strict, POSIX-enforced, file security.

    • +

  • DSI capabilities support a wide range of common metadata queries.

    • +

  • DSI interfaces with multiple database technologies and archival storage options.

    • +

  • Query-driven data movement is supported and is transparent to the user.

    • +

  • The DSI API can be used to develop user-specific workflows.

    • +
+
+Figure depicting the data life cycle +
+

A depiction of data life cycle can be seen here. The Data Science Infrastructure API supports the user to manage the life cycle aspects of their data.

+
+
+

DSI system design has been driven by specific use cases, both AI/ML and more generic usage. These use cases can often be generalized to user stories and needs that can be addressed by specific features, e.g., flexible, human-readable query capabilities. DSI uses Object Oriented design principles to encourage modularity and to support contributions by the user community. The DSI API is Python-based.

+
+

Implementation Overview

+

The DSI API is broken into three main categories:

+
    +

  • Plugins: these are frontend capabilities that will be commonly used by the generic DSI user. These include readers and writers.

    • +

  • Backends: these are used to interact with storage devices and other ways of moving data.

    • +

  • DSI Core: the middleware that contains the basic functionality to use the DSI API.

    • +
+
+

Plugin Abstract Classes

+

Plugins transform an arbitrary data source into a format that is compatible with the DSI core. The parsed and queryable attributes of the data are called metadata – data about the data. Metadata share the same security profile as the source data.

+

Plugins can operate as data readers or data writers. A simple data reader might parse an application’s output file and place it into a core-compatible data structure such as Python built-ins and members of the popular Python collection module. A simple data writer might execute an application to supplement existing data and queryable metadata, e.g., adding locations of outputs data or plots after running an analysis workflow.

+

Plugins are defined by a base abstract class, and support child abstract classes which inherit the properties of their ancestors.

+

Currently, DSI has the following readers:

+
+
    +

  • CSV file reader: reads in comma separated value (CSV) files.

    • +

  • Bueno reader: can be used to capture performance data from Bueno.

    • +
+
+
+Figure depicting the current plugin class hierarchy. +
+

Figure depicting the current DSI plugin class hierarchy.

+
+
+
+
+

Backend Abstract Classes

+

Backends are an interface between the core and a storage medium. +Backends are designed to support a user-needed functionality. Given a set of user metadata captured by a DSI frontend, a typical functionality needed by DSI users is to query that metadata by SQL query. Because the files associated with the queryable metadata may be spread across filesystems and security domains, a supporting backend is required to assemble query results and present them to the DSI core for transformation and return.

+
+This figure depicts a user asking a typical query on the user's metadata +
+

In this typical user story, the user has metadata about their data stored in DSI storage of some type. The user needs to extract all files with the variable foo above a specific threshold. DSI backends query the DSI metadata store to locate and return all such files.

+
+
+

Current DSI backends include:

+
    +

  • Sqlite: Python based SQL database and backend; the default DSI API backend.

    • +

  • GUFI: the Grand Unified File Index system Grand Unified File-Index ; developed at LANL, GUFI is a fast, secure metadata search across a filesystem accessible to both privileged and unprivileged users.

    • +

  • Parquet: a columnar storage format for Apache Hadoop.

    • +
+
+
+

DSI Core

+

DSI basic functionality is contained within the middleware known as the core. The DSI core is focused on delivering user-queries on unified metadata which can be distributed across many files and security domains. DSI currently supports Linux, and is tested on RedHat- and Debian-based distributions. The DSI core is a home for DSI Plugins and an interface for DSI Backends.

+

Core Documentation

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 00000000..f1b32eb8 Binary files /dev/null and b/objects.inv differ diff --git a/plugins.html b/plugins.html new file mode 100644 index 00000000..5dec0207 --- /dev/null +++ b/plugins.html @@ -0,0 +1,325 @@ + + + + + + + Plugins — DSI 0.0.0 documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Plugins

+

Plugins connect data-producing applications to DSI core functionalities. Plugins have writers or readers functions. A Plugin reader function deals with existing data files or input streams. A Plugin writer deals with generating new data. Plugins are modular to support user contribution.

+

Plugin contributors are encouraged to offer custom Plugin abstract classes and Plugin implementations. A contributed Plugin abstract class may extend another plugin to inherit the properties of the parent. In order to be compatible with DSI core, Plugins should produce data in Python built-in data structures or data structures sourced from the Python collections library.

+

Note that any contributed plugins or extensions must include unit tests in plugins/tests to demonstrate the new Plugin capability.

+
+Figure depicting the current plugin class hierarchy. +
+

Figure depicts the current DSI plugin class hierarchy.

+
+
+
+
+class dsi.plugins.plugin.Plugin(path)
+

Plugin abstract class for DSI core product.

+

A Plugin connects a data reader or writer to a compatible middleware +data structure.

+
+
+abstract add_to_output(path)
+

Initialize Plugin setup.

+

Read a Plugin file. Return a Plugin object.

+
+ +
+ +
+
+class dsi.plugins.metadata.StructuredMetadata(**kwargs)
+

plugin superclass that provides handy methods for structured data

+
+
+add_to_output(row: list) → None
+

Adds a row of data to the output_collector and guarantees good structure. +Useful in a plugin’s add_rows method.

+
+ +
+
+schema_is_set() → bool
+

Helper method to see if the schema has been set

+
+ +
+
+set_schema(column_names: list, validation_model=None) → None
+

Initializes columns in the output_collector and column_cnt. +Useful in a plugin’s pack_header method.

+
+ +
+ +
+
+class dsi.plugins.env.Environment
+

Environment Plugins inspect the calling process’ context.

+

Environments assume a POSIX-compliant filesystem and always collect UID/GID +information.

+
+ +
+
+class dsi.plugins.env.GitInfo(git_repo_path='./')
+

A Plugin to capture Git information.

+

Adds the current git remote and git commit to metadata.

+
+
+add_rows() → None
+

Adds a row to the output with POSIX info, git remote, and git commit

+
+ +
+
+pack_header() → None
+

Set schema with POSIX and Git columns

+
+ +
+ +
+
+class dsi.plugins.env.Hostname(**kwargs)
+

An example Environment implementation.

+

This plugin collects the hostname of the machine, and couples this with the POSIX +information gathered by the Environment base class.

+
+
+add_rows() → None
+

Parses environment provenance data and adds the row.

+
+ +
+
+pack_header() → None
+

Set schema with keys of prov_info.

+
+ +
+ +
+
+class dsi.plugins.env.SystemKernel
+

Plugin for reading environment provenance data.

+

An environment provenance plugin which does the following: +1. System Kernel Version +2. Kernel compile-time config +3. Kernel boot config +4. Kernel runtime config +5. Kernel modules and module config +6. Container information, if containerized

+
+
+add_rows() → None
+

Parses environment provenance data and adds the row.

+
+ +
+
+static get_cmd_output(cmd: list, ignore_stderr=False) → str
+

Runs a given command and returns the stdout if successful.

+

If stderr is not empty, an exception is raised with the stderr text.

+
+ +
+
+get_kernel_bt_config() → dict
+

Kernel boot-time configuration is collected by looking at /proc/cmdline.

+

The output of this command is one string of boot-time parameters. This string is +returned in a dict.

+
+ +
+
+get_kernel_ct_config() → dict
+

Kernel compile-time configuration is collected by looking at /boot/config-(kernel version) +and removing comments and empty lines.

+

The output of said command is newline-delimited option=value pairs.

+
+ +
+
+get_kernel_mod_config() → dict
+

Kernel module configuration is collected with the “lsmod” and “modinfo” commands.

+

Each module and modinfo are stored as a key-value pair in the returned dict.

+
+ +
+
+get_kernel_rt_config() → dict
+

Kernel run-time configuration is collected with the “sysctl -a” command.

+

The output of this command is lines consisting of two possibilities: +option = value (note the spaces), and sysctl: permission denied … +The option = value pairs are added to the output dict.

+
+ +
+
+get_kernel_version() → dict
+

Kernel version is obtained by the “uname -r” command, returns it in a dict.

+
+ +
+
+get_prov_info() → str
+

Collect and return the different categories of provenance info.

+
+ +
+
+pack_header() → None
+

Set schema with keys of prov_info.

+
+ +
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/py-modindex.html b/py-modindex.html new file mode 100644 index 00000000..a2860e5c --- /dev/null +++ b/py-modindex.html @@ -0,0 +1,168 @@ + + + + + + Python Module Index — DSI 0.0.0 documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Python Module Index
    • +
  • +
    • +
+
+
+
+
+ + +

Python Module Index

+ +
+ d +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 
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"dsi.plugins.metadata.StructuredMetadata.schema_is_set"]], "set_schema() (dsi.plugins.metadata.structuredmetadata method)": [[6, "dsi.plugins.metadata.StructuredMetadata.set_schema"]]}}) \ No newline at end of file diff --git a/tiers.html b/tiers.html new file mode 100644 index 00000000..0385262c --- /dev/null +++ b/tiers.html @@ -0,0 +1,182 @@ + + + + + + + DSI Development Tiers — DSI 0.0.0 documentation + + + + + + + + + + + + + + + + + + + +
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DSI Development Tiers

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The elements of the DSI project can be broken into Tiers. Tier 1 is the DSI Core and associated DSI data services. The DSI data services include the functionality to store and retrieve user metadata in DSI accessible storage. Tier 2 consists of DSI data workflows. These are generally customized implementations of DSI for particular science applications. Tier 2 workflows will often drive new capabilities within the DSI core.

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DSI Tier 1

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DSI core functionalities

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  • DSI software/API (v1.0) released via GitHub

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  • Can be used on its own or as part of a data service workflow

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  • Provides an API for users/services

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Current DSI Core Functionalities

Functionality

DSI element

Description

Ingest data

Plugins: Readers

Ability to read in data; users can create data-specific readers

Write data

Plugins: Writers

Ability to write data; users can create data-specific writers

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DSI data service

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  • DSI software combined with storage resources

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  • Provides a generalized institutional data management service, e.g. readers, writers that can be used to store and update metadata on DSI storage resources

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  • DSI team is implementing the data service at LANL with targeted data storage; however, the storage could be set up anywhere

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DSI Tier 2

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  • Customized implementations of DSI for particular applications/workflows

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  • Higher level features/capabilities

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  • Can be implemented locally or connected to a DSI institutional service

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  • Typically developed by experienced DSI users/developers in consultation with an application team

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  • May incorporate other tools such as customized GUIs or Cinema

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  • Examples +* Ensemble analysis such as the Wildfire data +* AI/ML workflows

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