Query the Data Delivery Network
Query the DDNThe 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 protocol. 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 rate_of_hospitalizations_for_opioid_overdose_per table in this repository, by referencing it like:
"pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-kdjs-fpiw:latest"."rate_of_hospitalizations_for_opioid_overdose_per"or in a full query, like:
SELECT
":id", -- Socrata column ID
"state_fips_code", -- Federal Information Processing Standard code for the state (Always “42” for Commonwealth). These are the first 2 digits of 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/library/reference/code-lists/ansi.html
"type_of_rate", -- Describes the Type of Rate displayed. Values: “Rate of hospitalizations for heroin overdose per 100,000 residents” indicates the number of hospitalizations for heroin overdose for Pennsylvania residents age 15 and above divided by the total population age 15 and above for that demographic; that percentage is then multiplied by 100,000 and rounded to the nearest tenth. “Rate of hospitalizations for pain medication overdose per 100,000 residents” indicates the number of hospitalizations for opioid pain medication overdose for Pennsylvania residents age 15 and above divided by the total population age 15 and above for that demographic; that percentage is then multiplied by 100,000 and rounded to the nearest tenth. “Not Reported due to Low Volume” – indicates that the rate of hospitalizations for opioid overdose has not been displayed (is blank) due to low volume of hospitalizations for opioid overdose.
"demographic", -- Specifies the characteristic of the population for which the rate of hospitalizations for heroin or opioid pain medication overdose are calculated. Values: “Statewide” = Hospitalization rate across all characteristics. “Age 15 to 34” = Hospitalization rate based on patient age in years between 15 and 34 inclusive. “Age 35 to 54” = Hospitalization rate based on patient age in years between 35 and 54 inclusive. “Age 55 and over” = Hospitalization rate based on patient age in years greater than or equal to 55. “Black (non-Hispanic)” = Hospitalization rate based on reported Black race and no reported Hispanic ethnicity. “Hispanic” = Hospitalization rate based on reported Hispanic ethnicity. “White (non-Hispanic)” = Hospitalization rate based on reported White race and no reported Hispanic ethnicity. “$0 to <$30,000 Household Income” = Hospitalization rate based on population and median household income estimates for patient zip code of residency less than $30,000. “$30,000 to <$60,000 Household Income” = Hospitalization rate based on population and median household income estimates for patient zip code of residency greater than or equal to $30,000 but less than $60,000. “$60,000 to <$90,000 Household Income” = Hospitalization rate based on population and median household income estimates for patient zip code of residency greater than or equal to $60,000 but less than $90,000. “$90,000+ Household Income” = Hospitalization rate based on population and median household income for patient zip code of residency greater than or equal to $90,000. “Rural” and “Urban” = Hospitalization rate based on rural or urban designation of patient county of residency. “Female” and “Male” = Hospitalization rate based on patient sex reported as female or male.
"category", -- Specifies whether the measurement is for hospitalizations for heroin overdose or for opioid pain medication overdose. Values: 1) “Heroin” = Rate of hospitalizations with a principal diagnosis of heroin overdose, and 2) “Pain Medication” = Rate of hospitalizations with a principal diagnosis of opioid pain medication overdose.
"demographic_sort_key", -- Specifies group of values from the Demographic column. Values: “Statewide” = Hospitalization rate across all characteristics. “Age Group” = Hospitalization rate based on categorization of reported patient age. “Race/Ethnicity” = Hospitalization rate based on reported race and ethnicity. “Household Income” = Hospitalization rate based on population and median household income estimates for patient zip code of residency. “Rural/Urban” = Hospitalization rate based on rural or urban designation of patient county of residency. “Female/Male” = Hospitalization rate based on reported patient sex.
"time_period", -- Reporting period that the measurement is based upon; "CY-" prefix is shorthand for "calendar year", which begins on January 1st of the stated year and ends on December 31st of the stated year.
"state", -- State Name (Always “COMMONWEALTH”) for the Commonwealth of Pennsylvania
"time_period_date_start", -- Beginning of the reporting period that covers the hospitalization's discharge date.
"time_period_date_end", -- End of the reporting period that covers the hospitalization's discharge date.
"rate_of_hospitalizations" -- Calculated rate of hospitalizations for heroin overdose or for opioid pain medication overdose per 100,000 residents age 15 and above in a particular demographic. Type of Rate column provides additional details.
FROM
"pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-kdjs-fpiw:latest"."rate_of_hospitalizations_for_opioid_overdose_per"
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/rate-of-hospitalizations-for-opioid-overdose-per-kdjs-fpiw with SQL in under 60 seconds.
Query Your Local Engine
bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"Read the installation docs.
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, 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 (like this repository), where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Cloning Data
Because pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-kdjs-fpiw:latest is a Splitgraph Image, you can clone the data from Spltgraph Cloud to your local engine, where you can query it like any other Postgres database, using any of your existing tools.
First, install Splitgraph if you haven't already.
Clone the metadata with sgr clone
This will be quick, and does not download the actual data.
sgr clone pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-kdjs-fpiwCheckout the data
Once you've cloned the data, you need to "checkout" the tag that you want. For example, to checkout the latest tag:
sgr checkout pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-kdjs-fpiw:latestThis will download all the objects for the latest tag of pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-kdjs-fpiw and load them into the Splitgraph Engine. Depending on your connection speed and the size of the data, you will need to wait for the checkout to complete. Once it's complete, you will be able to query the data like you would any other Postgres database.
Alternatively, use "layered checkout" to avoid downloading all the data
The data in pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-kdjs-fpiw:latest is 0 bytes. If this is too big to download all at once, or perhaps you only need to query a subset of it, you can use a layered checkout.:
sgr checkout --layered pa-gov/rate-of-hospitalizations-for-opioid-overdose-per-kdjs-fpiw:latestThis will not download all the data, but it will create a schema comprised of foreign tables, that you can query as you would any other data. Splitgraph will lazily download the required objects as you query the data. In some cases, this might be faster or more efficient than a regular checkout.
Read the layered querying documentation to learn about when and why you might want to use layered queries.
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/rate-of-hospitalizations-for-opioid-overdose-per-kdjs-fpiw is just another Postgres schema.