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 individuals_under_medical_assistance_newly table in this repository, by referencing it like:
"pa-gov/individuals-under-medical-assistance-newly-k4fb-62ju:latest"."individuals_under_medical_assistance_newly"or in a full query, like:
SELECT
":id", -- Socrata column ID
"geocoded_column", -- If the Lat/Long point is null, then the county is unknown. This is a generic latitude and longitude of the county in degrees, separated by a comma and enclosed in parentheses for each county. This is provided to create visualizations such as map layers. The latitude and longitude for Pennsylvania falls at the southeast corner of the state actually in the state of Maryland so that statewide information can be displayed on a map layer without affecting another county.
"latitude_longitude", -- If the Lat/Long point is null, then the county is unknown. This is a generic point for latitude and longitude in each county to give the ability to create visualizations such as map layers. The latitude and longitude for the state of Pennsylvanian will fall to the south east of Pennsylvania actually in Maryland so that the information for a statewide total can also be displayed on a map layer without affecting information in another county.
"county_fips_code", -- The FIPS county code is a five-digit Federal Information Processing Standard (FIPS) code (FIPS 6-4) which uniquely identifies counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. This is the 3-digit part of the 5-digit county FIPS code specifically standing for the county.
"state_fips_code", -- 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
"county_code", -- A 2-digit code indicating county within Pennsylvania, which is the same as County Code Number but with leading zeroes.
"time_measure", -- Time frame rate is based on Calendar year (January 1, 20XX to December 31, 20XX)
"number_of_records", -- Identifies number of values associated with this County/State
"measure", -- Count of the number of individuals under MA (Medical Assistance) diagnoses with OUD (Opiod Use Disorder)
"count", -- Calculated total of individuals for each county/STATE† †Non-numerical values: NR=No events reported; ND=Not displayed (for any count that is 10 or less)
"date_revised", -- Date County or Statewide data was last altered. Date in MM/DD/YYYY format.
"year", -- Timeframe for count of babies on MA (Medical Assistance) diagnosis with NAS (Neonatal Abstinence Syndrome)
"fips_county_code", -- Five-digit Federal Information Processing Standards (FIPS) code which uniquely identifies each Pennsylvania county. 42000=Pennsylvania, 42001=Adams, 42003=Allegheny,…42133=York.
"county", -- Case of County name (includes STATE as an option) where individual receiving treatment
"county_code_number", -- Integer ranging from 1 to 67 indicating the Pennsylvania county when ordered alphabetically.
"beginning_of_the_year", -- The date for the Beginning of the year of the data being reported.
":@computed_region_rayf_jjgk",
":@computed_region_r6rf_p9et",
":@computed_region_amqz_jbr4",
":@computed_region_d3gw_znnf",
":@computed_region_nmsq_hqvv"
FROM
"pa-gov/individuals-under-medical-assistance-newly-k4fb-62ju:latest"."individuals_under_medical_assistance_newly"
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/individuals-under-medical-assistance-newly-k4fb-62ju 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/individuals-under-medical-assistance-newly-k4fb-62ju: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/individuals-under-medical-assistance-newly-k4fb-62juCheckout 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/individuals-under-medical-assistance-newly-k4fb-62ju:latestThis will download all the objects for the latest tag of pa-gov/individuals-under-medical-assistance-newly-k4fb-62ju 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/individuals-under-medical-assistance-newly-k4fb-62ju: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/individuals-under-medical-assistance-newly-k4fb-62ju: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/individuals-under-medical-assistance-newly-k4fb-62ju is just another Postgres schema.