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_with_medical_assistance_ma_receiving table in this repository, by referencing it like:
"pa-gov/individuals-with-medical-assistance-ma-receiving-unzz-dvz6:latest"."individuals_with_medical_assistance_ma_receiving"or in a full query, like:
SELECT
":id", -- Socrata column ID
"location_1_zip",
"location_1", -- 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.
"county_fips_code", -- If the FIPS code is null, then the data source is unknown. 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", -- If the FIPS code is null, then the data source is unknown. 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. If the county code is null, then the county is unknown.
"county_code_number", -- Integer ranging from 1 to 67 indicating the Pennsylvania county when ordered alphabetically. If the county code is null, then the county is unknown.
"measure", -- Count of the number of individuals on MA (Medical Assistance) receiving MAT (Medication Assisted Treatment). Description also designates if the count is suppressed due to low numbers.
"count", -- Calculated total of individuals for each county/State. A null field designates the count is 10 or less, so the count is suppressed.
"date_revised", -- Date County or Statewide data was last altered. Date in MM/DD/YYYY format.
"year", -- Calendar year 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_state", -- Case of County name (includes STATE as an option) where Individuals under MA (Medical Assistance) receiving MAT (Medication Assisted Treatment)
"beginning_of_the_year", -- The beginning date of the Year of the data being reported.
"location_1_city",
"location_1_state",
"location_1_address",
":@computed_region_d3gw_znnf",
":@computed_region_nmsq_hqvv",
":@computed_region_rayf_jjgk",
":@computed_region_amqz_jbr4",
":@computed_region_r6rf_p9et",
"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
FROM
"pa-gov/individuals-with-medical-assistance-ma-receiving-unzz-dvz6:latest"."individuals_with_medical_assistance_ma_receiving"
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-with-medical-assistance-ma-receiving-unzz-dvz6 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/individuals-with-medical-assistance-ma-receiving-unzz-dvz6: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
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 (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/individuals-with-medical-assistance-ma-receiving-unzz-dvz6" \
--handler-options '{
"domain": "data.pa.gov",
"tables": {
"individuals_with_medical_assistance_ma_receiving": "unzz-dvz6"
}
}'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/individuals-with-medical-assistance-ma-receiving-unzz-dvz6 is just another Postgres schema.