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This post is the last in an ongoing three-part series, and is a write-up of the talk we gave at RailsConf 2018. You can find the slides , or read Part I and Dorothy Perkins Womens Broderie Lace Shell Blouse Wholesale Price Cheap Online Cheap Sale Hot Sale WUlNVi
of the series to catch up.

In Part II of the Skylight for Open Source Series, we explored how to spot problematic indexing using Skylight. In this installment, we'll dig into some hidden performance issues in another open source app: Junk Food Janis Joplin Down On Me Tee Outlet Newest UnRvZSg
.

Code Triage is an open source app built to help other open source projects. Popular open source projects receive a lot of bug reports, feature requests, and pull requests every day, and just reading through all of them can be a huge time sink for their maintainers. Here at Skylight, we are involved in a number of popular open source projects ourselves, so we understand this problem pretty well!

Code Triage lets you help out your favorite project maintainers by sending contributors a random ticket to their inboxes on a daily basis, which allow them to help triage GitHub issues and pull requests. That way, contributors can help split the workload, so everyone only has to do a little bit of work each day. Today, there are tens of thousands of developers who signed up to help thousands of open source projects on the site.

Running an app at this scale creates some pretty unique challenges. A quick glance at Code Triage's Vero Moda Frill Skirt Women Blue Clearance Pictures Cheap Sale 2018 New 33NYNR
reveals some of the most popular projects and shows exactly how many open issues need to be triaged.

This can be a lot of information to render, and it can get a little slow at times. However, this is also by far one of the most popular pages in the app, since it's what everyone sees first when they visit the site.

Skylight marks Code Triage's homepage as an endpoint with "high agony", which means that we'll probably get good bang for our buck if we choose to optimize it.

Notably, even though there's a lot of information on the home page, most of these things don’t actually change all that often, which makes this page a prime candidate for caching. But, as it turns out, a lot of performance-minded people have already done extensive work on Code Triage, so most things that should be cached actually already are !

However, when we took a closer look at this page on Skylight, we noticed that, in order to populate the <meta> tag, we needed to run two queries to fetch the counts of users and projects on the site.

It's likely that these numbers didn't need to be super up-to-date, so we implemented some caching here. We submitted a pull request to cache this information for up to an hour.

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Curriculum

Overview - All Tracks

The MS Analytics curriculum is structured to be completed in a single year (fall, spring, and summer), with a total of 36 credit-hours required for each student. Trained by world-class faculty, students will learn identification and framing of problems; acquisition, management, and utilization of large and fast-moving streams of data; creation, analysis, solution, and interpretation of mathematical models using appropriate methodology; and the integration of these interdisciplinary skills to enable graduates to successfully develop and execute analytics projects.

The interdisciplinary core includes 15 hours of coursework across business, computing, statistics, and operations research. On top of this integrated breadth of study covering the core areas of analytics, each student has 15 hours of electives to satisfy one of the specialized tracks to give them depth in an analytics area of specialization: Analytical Tools, Business Analytics, and Computational Data Analytics. Each student's elective choices can be personalized to support their individual career goals. The final piece of the curriculum is an applied analytics practicum, in which students will work with companies and organizations on real analytics problems.

To see the specific list of topics covered in the interdisciplinary core and electives, see the Topics Covered page.

Base Curriculum - All Tracks

To see a list of all courses offered, see the Course Listing page.

MS Analytics Track Options

Analytical Tools Track

The Analytical Tools track provides students with a greater knowledge and understanding of the quantitative methodology of descriptive, predictive, and prescriptive analytics: how to select, build, solve, and analyze models using methodology such as parametric and non-parametric statistics, regression, forecasting, data mining, machine learning, optimization, stochastics , and simulation.

View Curriculum

Business Analytics Track

The Business Analytics track provides students with a deeper understanding of the practice of using analytics in business and industry: how to understand, frame, and solve problems in marketing, operations, finance, management of information technology, human resources, and accounting in order to develop and execute analytics projects within businesses.

View Curriculum

Computational Data Analytics

The Computational Data Analytics track provides students with a deeper understanding of the practice of dealing with so-called “big data”: how to acquire, preprocess, store, manage, analyze, and visualize data arriving at high volume, velocity, and variety.

CoverageJSON

WORK-IN-PROGRESS

The following items are (major) outstanding issues to be resolved for the first version:

Contents

CoverageJSON is a format for encoding coverage data like grids, time series, and vertical profiles, distinguished by the geometry of their spatiotemporal domain. A CoverageJSON object represents a domain, a range, a coverage, or a collection of coverages. A range in CoverageJSON represents coverage values. A coverage in CoverageJSON is the combination of a domain, parameters, ranges, and additional metadata. A coverage collection represents a list of coverages.

A complete CoverageJSON data structure is always an object (in JSON terms). In CoverageJSON, an object consists of a collection of name/value pairs – also called members. For each member, the name is always a string. Member values are either a string, number, object, array or one of the literals: true, false, and null. An array consists of elements where each element is a value as described above.

A CoverageJSON grid coverage of global air temperature:

where "http://example.com/coverages/123/TEMP" points to the following document:

Range data can also be directly embedded into the main CoverageJSON document, making it stand-alone.

The candidate OGC standard Broadway Womens 10156258 3/4 Sleeve Cardigan Off 16 Prices nmLtIntfz
(short CIS) defines a coverage model targeted towards OGC service types like Web Coverage Service (WCS) and is the successor of the “GML 3.2.1 Application Schema – Coverages” version 1.0 (short GMLCOV).

The model of CoverageJSON can be seen as a mix of CIS and the data cube-based ASOS Scuba Prom Skirt with Paperbag Waist Sale Discounts Top Quality For Sale Free Shipping Shop For Buy Cheap 100% Guaranteed Cheap Price Factory Outlet qZw098HnW
.

The following lists some areas where the model used by CoverageJSON departs from CIS:

An i18n object represents a string in multiple languages where each key is a language tag as defined in Buy Cheap High Quality High Quality Boohoo Micro Mini Cord Skirt Sale View umCiNMu
, and the value is the string in that language. The special language tag "und" can be used to identify a value whose language is unknown or undetermined.

Example:

Parameter objects represent metadata about the values of the coverage in terms of the observed property (like water temperature), the units, and others.

Example for a continuous-data parameter:

Example for a categorical-data parameter:

Parameter group objects represent logical groups of parameters, for example vector quantities.

Example of a group describing a vector quantity:

where "WIND_SPEED" and "WIND_DIR" reference existing parameters in a CoverageJSON coverage or collection object by their short identifiers.

Example of a group describing uncertainty of a parameter:

where "SST_mean" references the following parameter: