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 medical_assistance_enrollment_july_2003_current table in this repository, by referencing it like:
"pa-gov/medical-assistance-enrollment-july-2003-current-2ght-hfn9:latest"."medical_assistance_enrollment_july_2003_current"or in a full query, like:
SELECT
":id", -- Socrata column ID
"fiscal_year", -- State Fiscal Year (SFY) starts in July and ends in June. This column gives what the year is of the State Fiscal Year for the data in this row.
"ma_individuals", -- MA (Medical Assistance) A person who has been determined eligible for MA and is receiving benefits for a given month. These numbers include adults & children. These numbers include people that are on TANF (Temporary Assistance for Needy Families) & GA (General Assistance), Disabled, & Waiver categories. In July 2014, duplication of persons moving from county to county was eliminated.
"ma_children", -- MA (Medical Assistance) Children enrolled in MA for a given month based on their age which is under 21 years old. In July 2014, duplication of persons moving from county to county was eliminated. In July 2014, the EC (Eligible Child) designation for determining MA children was changed to using age, which is under 21. These numbers include people that are on TANF (Temporary Assistance for Needy Families) & GA (General Assistance), Disabled, & Waiver categories.
"geocoded_column", -- Georeferenced column as a point used for creating visuals such as maps. A generic point for reach county is supplied so a map can be created.
"latitude", -- Latitude generic point for each county.
"county_fips", -- FIPS code. The Federal Information Processing Standard (FIPS) code, used by the United States government to uniquely identify counties, is provided with each entry. FIPS codes are five-digit numbers; for Pennsylvania the codes start with 42 and are completed with the three-digit county code. The County FIPS is the last three digits of the five digit FIPS and the code 000 is for statewide.
"county_cd", -- There are 67 counties in Pennsylvania. They're numbered 01 thru 67, and 00 identifies the statewide total.
"month", -- Calendar Month Number
"state_code", -- The Federal Information Processing Standard (FIPS) code, used by the United States government to uniquely identify states and counties. FIPS codes are five-digit numbers; for Pennsylvania the codes start with 42 and are completed with the three-digit county code. The state code is the first two digits of the five digit FIPS code.
"state_name", -- PA is the abbreviation for Pennsylvania and provides the state name for this dataset.
"date", -- Combination of calendar month and year, formatted as MM/YYYY.
"year", -- Calendar Year Number
"county_name", -- The name of the county in Pennsylvania or "Statewide" for all of Pennsylvania.
"month_name", -- Name of the Calendar Month
"longitude", -- Longitude generic point for each county.
":@computed_region_rayf_jjgk",
":@computed_region_r6rf_p9et",
":@computed_region_amqz_jbr4",
":@computed_region_d3gw_znnf",
":@computed_region_nmsq_hqvv"
FROM
"pa-gov/medical-assistance-enrollment-july-2003-current-2ght-hfn9:latest"."medical_assistance_enrollment_july_2003_current"
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/medical-assistance-enrollment-july-2003-current-2ght-hfn9 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/medical-assistance-enrollment-july-2003-current-2ght-hfn9: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/medical-assistance-enrollment-july-2003-current-2ght-hfn9Checkout 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/medical-assistance-enrollment-july-2003-current-2ght-hfn9:latestThis will download all the objects for the latest tag of pa-gov/medical-assistance-enrollment-july-2003-current-2ght-hfn9 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/medical-assistance-enrollment-july-2003-current-2ght-hfn9: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/medical-assistance-enrollment-july-2003-current-2ght-hfn9: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/medical-assistance-enrollment-july-2003-current-2ght-hfn9 is just another Postgres schema.