pa-gov/covid19-aggregate-hospitalizations-current-daily-kayn-sjhx

Query the Data Delivery Network

Query the DDN

The easiest way to query any data on Splitgraph is via the "Data Delivery Network" (DDN). The DDN is a single endpoint that speaks the PostgreSQL wire procotol. Any Splitgraph user can connect to it at data.splitgraph.com:5432 and query any version of over 40,000 datasets that are hosted or proxied by Splitgraph.

For example, you can query the covid19_aggregate_hospitalizations_current_daily table in this repository, by referencing it like:

"pa-gov/covid19-aggregate-hospitalizations-current-daily-kayn-sjhx:latest"."covid19_aggregate_hospitalizations_current_daily"

or in a full query, like:

SELECT 
    ":id", -- Socrata column ID
    "covid_icu_mean", -- The mean for COVID Patients in Intensive Care Unit (ICU), 14-day average. 
    ":@computed_region_nmsq_hqvv", -- This column was automatically created in order to record in what polygon from the dataset 'Pennsylvania County Boundaries' (nmsq-hqvv) the point in column 'georeference_latitude__longitude' is located.  This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
    "covid_vents", -- COVID-19 patients on ventilators
    "vents_mean", -- Total ventilators, 14-day average
    "icu_percent", -- Adult ICU beds, percent available
    "med_avail", -- Medical/surgical beds available
    "icu_avail_mean", -- Adult ICU beds available, 14-day average
    "covid_vents_mean", -- COVID-19 patients on ventilators, 14-day average
    "county", --  County, region, or state name
    "aii_total_mean", -- Airborne isolation beds total, 14-day average
    "vents_use", -- Total ventilators in use
    "pic_total", -- Pediatrics ICU beds total
    "aii_percent", --  Airborn isolation beds, percent available
    "icu_total_mean", -- Adult ICU beds total, 14-day average
    "latitude", -- This is a latitude generic point within the county to help create map visualizations
    "med_avail_mean", -- Medical/surgical beds available, 14-day average
    "pic_avail", -- Pediatrics ICU beds available
    "covid_icu", -- COVID Patients in Intensive Care Unit (ICU). 
    "county_fips", -- This is the 5-digit Federal Information Processing Standard (FIPS) code that designate the State association. Each State has its own 2-digit number and each County within the state has its own 3-digit number which are combined into a 5-digit number to uniquely identify every US county. For more technical details : Federal Information Processing Standards Publications (FIPS PUBS) are issued by the National Institute of Standards and Technology (NIST) after approval by the Secretary of Commerce pursuant to Section 111 (d) of the Federal Property and Administrative Services Act of 1949 as amended by the Computer Security Act of 1987, Public Law 100-235. Federal Information Processing Standard (FIPS) 6-4, Counties and Equivalent Entities of the U.S., Its Possessions, and Associated Areas -- 90 Aug 31 , provides the names and codes that represent the counties and other entities treated as equivalent legal and/or statistical subdivisions of the 50 States, the District of Columbia, and the possessions and freely associated areas of the United States. Counties are considered to be the "first-order subdivisions" of each State and statistically equivalent entity, regardless of their local designations (county, parish, borough, etc.). Information gathered from census data - https://www.census.gov/geo/reference/codes/cou.html
    "ped_total", -- Pediatrics beds total
    "covid_patients", -- COVID-19 patients hospitalized
    "ped_avail", -- Pediatrics beds available
    "med_total_mean", -- Medical/surgical beds total, 14-day average
    "longitude", -- This is a longitude generic point within the county to help create map visualizations
    "date", -- Date of data, always 12:00 UTC
    "aii_avail", -- Airborn isolation beds available
    "aii_total", -- Airborn isolation beds total
    "aii_avail_mean", -- Airborne isolation beds available, 14-day average
    "pic_avail_mean", -- Pediatric ICU beds available, 14-day average
    "med_percent", -- Medical/surgical beds, percent available
    "ped_avail_mean", -- Pediatric beds available, 14-day average
    "med_total", -- Medical/surgical beds total
    "icu_avail", -- Adult ICU beds available
    "covid_patients_mean", --  COVID-19 patients hospitalized, 14-day average
    ":@computed_region_rayf_jjgk", -- This column was automatically created in order to record in what polygon from the dataset 'Pa School Districts (2019-06)' (rayf-jjgk) the point in column 'georeference_latitude__longitude' is located.  This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
    ":@computed_region_r6rf_p9et", -- This column was automatically created in order to record in what polygon from the dataset 'Pa House Districts (2020-01)' (r6rf-p9et) the point in column 'georeference_latitude__longitude' is located.  This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
    ":@computed_region_amqz_jbr4", -- This column was automatically created in order to record in what polygon from the dataset 'Municipality Boundary' (amqz-jbr4) the point in column 'georeference_latitude__longitude' is located.  This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
    ":@computed_region_d3gw_znnf", -- This column was automatically created in order to record in what polygon from the dataset 'Pa Senatorial Districts (2020-01)' (d3gw-znnf) the point in column 'georeference_latitude__longitude' is located.  This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
    "ped_total_mean", -- Pediatric beds total, 14-day average
    "pic_total_mean", -- Pediatric ICU beds total, 14-day average
    "pic_percent", -- Pediatric ICU beds, percent available
    "vents", -- Total ventilators
    "icu_total", -- Adult ICU beds total
    "georeference_latitude__longitude", -- Georeferenced column for use in creating mapping visualizations with both a generic latitude and longitude of the county
    "ped_percent", --  Pediatric beds, percent available
    "vents_use_mean" -- Total ventilators in use, 14-day average
FROM
    "pa-gov/covid19-aggregate-hospitalizations-current-daily-kayn-sjhx:latest"."covid19_aggregate_hospitalizations_current_daily"
LIMIT 100;

Connecting to the DDN is easy. All you need is an existing SQL client that can connect to Postgres. As long as you have a SQL client ready, you'll be able to query pa-gov/covid19-aggregate-hospitalizations-current-daily-kayn-sjhx with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.pa.gov. When you querypa-gov/covid19-aggregate-hospitalizations-current-daily-kayn-sjhx:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).

We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.

Query Your Local Engine

Install Splitgraph Locally
bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"
 

Splitgraph Cloud is built around Splitgraph Core (GitHub), which includes a local Splitgraph Engine packaged as a Docker image. Splitgraph Cloud is basically a scaled-up version of that local Engine. When you query the Data Delivery Network or the REST API, we mount the relevant datasets in an Engine on our servers and execute your query on it.

It's possible to run this engine locally. You'll need a Mac, Windows or Linux system to install sgr, and a Docker installation to run the engine. You don't need to know how to actually use Docker; sgrcan manage the image, container and volume for you.

There are a few ways to ingest data into the local engine.

For external repositories (like this repository), the Splitgraph Engine can "mount" upstream data sources by using sgr mount. This feature is built around Postgres Foreign Data Wrappers (FDW). You can write custom "mount handlers" for any upstream data source. For an example, we blogged about making a custom mount handler for HackerNews stories.

For hosted datasets, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.

Mounting Data

This repository is an external repository. It's not hosted by Splitgraph. It is hosted by data.pa.gov, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.

First, install Splitgraph if you haven't already.

Mount the table with sgr mount

sgr mount socrata \
  "pa-gov/covid19-aggregate-hospitalizations-current-daily-kayn-sjhx" \
  --handler-options '{
    "domain": "data.pa.gov",
    "tables": {
        "covid19_aggregate_hospitalizations_current_daily": "kayn-sjhx"
    }
}'

That's it! Now you can query the data in the mounted table like any other Postgres table.

Query the data with your existing tools

Once you've loaded the data into your local Splitgraph engine, you can query it with any of your existing tools. As far as they're concerned, pa-gov/covid19-aggregate-hospitalizations-current-daily-kayn-sjhx is just another Postgres schema.

Related Documentation: