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 2022_ntd_annual_data_funding_sources_directly table in this repository, by referencing it like:
"datahub-transportation-gov/2022-ntd-annual-data-funding-sources-directly-yuaq-zdvc:latest"."2022_ntd_annual_data_funding_sources_directly"or in a full query, like:
SELECT
":id", -- Socrata column ID
"reporter_type", -- The type of NTD report that the agency completed this year.
"organization_type", -- Description of the agency's legal entity.
"uace_code", -- UACE Code remains consistent across census years.
"uza_name", -- The name of the agency's Urbanized Area.
"primary_uza_population", -- The population of the urbanized area primarily served by the agency.
"agency_voms", -- The number of revenue vehicles operated across the whole agency to meet the annual maximum service requirement. This is the revenue vehicle count during the peak season of the year; on the week and day that maximum service is provided. Vehicles operated in maximum service (VOMS) exclude atypical days and one-time special events.
"fares", -- All income directly earned from carrying passengers, paid either in cash or through pre-paid tickets, passes, etc. It includes donations from those passengers who donate money on the vehicle, reduced fares paid by passengers in a user-side subsidy arrangement, or payments made through an agreement to provide fare-free service for a certain group, e.g. payments from a university to provide free service to students. It also includes base fare, zone or distance premiums, express service premiums, extra cost transfers, and special transit fares.
"park_and_ride", -- Revenues earned from parking fees paid by passengers who drive to park-and-ride lots operated by the transit agency to use transit service.
"concessions", -- The revenue earned from granting operating rights to businesses (e.g., concessionaires, newsstands, candy counters) on property maintained by the transit agency.
"advertising", -- The revenue earned from displaying advertising materials on transit agency vehicles and property. The amounts should be net of any fees paid to advertising agencies that place the advertisement with the transit agency.
"advertising_questionable", -- FTA marks a data point as Questionable when there is reason to believe it is incorrect, but the reporting agency has been unable to correct the data or offer an explanation for its anomalous appearance.
"other_questionable", -- FTA marks a data point as Questionable when there is reason to believe it is incorrect, but the reporting agency has been unable to correct the data or offer an explanation for its anomalous appearance.
"purchased_transportation", -- Revenue accrued by a seller of transportation services through purchased transportation (PT) agreements, not including passenger fares for PT services from service provided under the PT agreement.
"purchased_transportation_1", -- FTA marks a data point as Questionable when there is reason to believe it is incorrect, but the reporting agency has been unable to correct the data or offer an explanation for its anomalous appearance.
"total", -- Total of the directly generated funding sources in previous columns.
"state", -- The state in which the agency is headquartered.
"concessions_questionable", -- FTA marks a data point as Questionable when there is reason to believe it is incorrect, but the reporting agency has been unable to correct the data or offer an explanation for its anomalous appearance.
"fares_questionable", -- FTA marks a data point as Questionable when there is reason to believe it is incorrect, but the reporting agency has been unable to correct the data or offer an explanation for its anomalous appearance.
"other", -- Revenue directly generated by the transit agency not fitting any of the other given categories (fares, park and ride, concessions, advertising, purchased transportation). May include revenue such as: -Investment earnings -Sales of maintenance services -Vehicle rentals -Rentals of buildings or property -Parking fees generated from parking lots not normally used as park-and-ride locations -Donations -Grants from private foundations -Development fees -School bus revenues -Charter bus revenues -Freight tariffs
"ntd_id", -- A five-digit identifying number for each agency used in the current NTD system.
"city", -- The city in which the agency is headquartered.
"park_and_ride_questionable", -- FTA marks a data point as Questionable when there is reason to believe it is incorrect, but the reporting agency has been unable to correct the data or offer an explanation for its anomalous appearance.
"total_questionable", -- FTA marks a data point as Questionable when there is reason to believe it is incorrect, but the reporting agency has been unable to correct the data or offer an explanation for its anomalous appearance.
"report_year", -- The year for which the data was reported.
"agency" -- The transit agency's name.
FROM
"datahub-transportation-gov/2022-ntd-annual-data-funding-sources-directly-yuaq-zdvc:latest"."2022_ntd_annual_data_funding_sources_directly"
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 datahub-transportation-gov/2022-ntd-annual-data-funding-sources-directly-yuaq-zdvc 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 datahub-transportation-gov/2022-ntd-annual-data-funding-sources-directly-yuaq-zdvc: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 datahub-transportation-gov/2022-ntd-annual-data-funding-sources-directly-yuaq-zdvcCheckout 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 datahub-transportation-gov/2022-ntd-annual-data-funding-sources-directly-yuaq-zdvc:latestThis will download all the objects for the latest tag of datahub-transportation-gov/2022-ntd-annual-data-funding-sources-directly-yuaq-zdvc 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 datahub-transportation-gov/2022-ntd-annual-data-funding-sources-directly-yuaq-zdvc: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 datahub-transportation-gov/2022-ntd-annual-data-funding-sources-directly-yuaq-zdvc: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, datahub-transportation-gov/2022-ntd-annual-data-funding-sources-directly-yuaq-zdvc is just another Postgres schema.