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 electric_vehicle_population_data table in this repository, by referencing it like:
"wa-gov/electric-vehicle-population-data-f6w7-q2d2:latest"."electric_vehicle_population_data"or in a full query, like:
SELECT
":id", -- Socrata column ID
"geocoded_column", -- The center of the ZIP Code for the registered vehicle.
"dol_vehicle_id", -- Unique number assigned to each vehicle by Department of Licensing for identification purposes.
"electric_range", -- Describes how far a vehicle can travel purely on its electric charge.
"vin_1_10", -- The 1st 10 characters of each vehicle's Vehicle Identification Number (VIN).
"model_year", -- The model year of the vehicle, determined by decoding the Vehicle Identification Number (VIN).
"county", -- This is the geographic region of a state that a vehicle's owner is listed to reside within. Vehicles registered in Washington state may be located in other states.
"ev_type", -- This distinguishes the vehicle as all electric or a plug-in hybrid.
"base_msrp", -- This is the lowest Manufacturer's Suggested Retail Price (MSRP) for any trim level of the model in question.
"cafv_type", -- This categorizes vehicle as Clean Alternative Fuel Vehicles (CAFVs) based on the fuel requirement and electric-only range requirement in House Bill 2042 as passed in the 2019 legislative session.
":@computed_region_fny7_vc3j",
"city", -- The city in which the registered owner resides.
":@computed_region_8ddd_yn5v",
"model", -- The model of the vehicle, determined by decoding the Vehicle Identification Number (VIN).
":@computed_region_x4ys_rtnd",
"_2020_census_tract", -- The census tract identifier is a combination of the state, county, and census tract codes as assigned by the United States Census Bureau in the 2020 census, also known as Geographic Identifier (GEOID). More information can be found here: https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_13 https://www.census.gov/programs-surveys/geography/guidance/geo-identifiers.html
"state", -- This is the geographic region of the country associated with the record. These addresses may be located in other states.
"electric_utility", -- This is the electric power retail service territories serving the address of the registered vehicle. All ownership types for areas in Washington are included: federal, investor owned, municipal, political subdivision, and cooperative. If the address for the registered vehicle falls into an area with overlapping electric power retail service territories then a single pipe | delimits utilities of same TYPE and a double pipe || delimits utilities of different types. We combined vehicle address and Homeland Infrastructure Foundation Level Database (HIFLD) (https://gii.dhs.gov/HIFLD) Retail_Service_Territories feature layer using a geographic information system to assign values for this field. Blanks occur for vehicles with addresses outside of Washington or for addresses falling into areas in Washington not containing a mapped electric power retail service territory in the source data.
"legislative_district", -- The specific section of Washington State that the vehicle's owner resides in, as represented in the state legislature.
"zip_code", -- The 5 digit zip code in which the registered owner resides.
"make" -- The manufacturer of the vehicle, determined by decoding the Vehicle Identification Number (VIN).
FROM
"wa-gov/electric-vehicle-population-data-f6w7-q2d2:latest"."electric_vehicle_population_data"
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 wa-gov/electric-vehicle-population-data-f6w7-q2d2 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 wa-gov/electric-vehicle-population-data-f6w7-q2d2: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 wa-gov/electric-vehicle-population-data-f6w7-q2d2Checkout 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 wa-gov/electric-vehicle-population-data-f6w7-q2d2:latestThis will download all the objects for the latest tag of wa-gov/electric-vehicle-population-data-f6w7-q2d2 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 wa-gov/electric-vehicle-population-data-f6w7-q2d2: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 wa-gov/electric-vehicle-population-data-f6w7-q2d2: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, wa-gov/electric-vehicle-population-data-f6w7-q2d2 is just another Postgres schema.