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_stations_and_facilities_by table in this repository, by referencing it like:
"datahub-transportation-gov/2022-ntd-annual-data-stations-and-facilities-by-aqct-knjk:latest"."2022_ntd_annual_data_stations_and_facilities_by"or in a full query, like:
SELECT
":id", -- Socrata column ID
"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.
"total_facilities", -- Total of all facility types in previous columns.
"report_year", -- The year for which the data was reported.
"revenue_collection_facility", -- Number of facilities inventoried by each agency wherein where revenue collection personnel process electronic and/or cash fare payments.
"vehicle_blow_down_facility", -- Number of stand-alone buildings or structures inventoried by each agency equipment for cleaning underfloor equipment of rail rolling stock.
"vehicle_testing_facility", -- Number of facilities inventoried by each agency which are used for vehicle acceptance testing (after being received from manufacturer or overhauls or other maintenance activity).
"exclusive_grade_separated", -- Number of stations along the street or in street or highway medians that are separated from mixed traffic. May be low-level platforms (serving low-floor vehicles) or raised platforms (serving high-floor vehicles).
"primary_uza_population", -- The population of the urbanized area primarily served by the agency.
"surface_parking_lot", -- Number of lots inventoried by each agency which are paved with asphalt, concrete, or permeable materials with parking spaces outlined by paint and other materials for demarcation. Typically includes lanes for vehicle circulation and is usually uncovered.
"organization_type", -- Description of the agency's legal entity.
"reporter_type", -- The type of NTD report that the agency completed this year.
"city", -- The city in which the agency is headquartered.
"passenger_stations_and_terminals", -- Total of all station and terminal subcategories.
"ntd_id", -- A five-digit identifying number for each agency used in the current NTD system.
"general_purpose_maintenance", -- Number of facilities inventoried by each agency wherein mechanics and other maintenance department personnel, provide basic service readiness inspection (e.g. tire pressure, oil/fluid levels) and light repair (e.g. mirror replacement) or service (e.g. sweeping) on revenue.
"state", -- The state in which the agency is headquartered.
"parking_structure", -- Number of inventoried single or multi-level parking structures inventoried by each agency that are built either underground (typically underneath a building or station), above grade, or both. Characterized by a street-level entrance with ramps to access parking spaces below the surface.
"vehicle_washing_facility", -- Number of stand-alone buildings or structures inventoried by each agency containing vehicle washer equipment.
"bus_transfer_center", -- Number of inventoried terminal stations for several routes or a large mid-route transfer facility where passengers may connect between two or more fixed-route bus services.
"other_passenger_or_parking", -- Number of inventoried passenger or parking facilities that do not fit into one of the nine categories described.
"administrative_and_other_non_passenger_facilities", -- Total of all administrative/non-passenger facility subcategories.
"maintenance_facility_service", -- Number of facilities inventoried by each agency wherein mechanics, machinists and other maintenance personnel perform preventive maintenance, daily service and inspection, and/or corrective maintenance activities on revenue vehicles to keep them in-service.
"underground_fixed_guideway", -- Number of passenger stations inventoried by each agency which typically consist of a concrete structure built below grade, constructed by cut and cover, drill-and-blast, excavated, bored tunnel, or sunken underwater tube.
"ferryboat_terminal", -- Number of terminals inventoried by each agency wherein passengers may board or alight from the ferryboat. Terminals may include canopies or shelters, lighting, and signage.
"at_grade_fixed_guideway", -- Number of inventoried passenger stations located at street grade along a transit exclusive right-of-way. May include pedestrian overpasses to allow passengers to reach station.
"elevated_fixed_guideway", -- Number of inventoried passenger stations located above grade built on a viaduct, a steel or concrete structure, or on retained fill.
"heavy_maintenance_overhaul", -- Number of facilities inventoried by each agency wherein mechanics, machinists and other maintenance personnel perform heavy overhaul and other related rebuilding activities to help revenue vehicles reach their targeted service life.
"parking_and_other_passenger_facilities", -- Total of all parking/other passenger facility subcategories.
"simple_at_grade_platform", -- Number of stations on-street or in street or highway medians. May be low-level platforms (serving low-floor vehicles) or raised platforms (serving high-floor vehicles). Typically includes shelters, canopies, lighting, signage, and/or ticket vending machines.
"agency", -- The transit agency's name.
"other_administrative", -- Number of inventoried administrative or maintenance facilities that do not fit into one of the ten categories described.
"combined_administrative_and", -- Number of facilities inventoried by each agency wherein facility with combined functions of at least one of the administrative facilities and one of the maintenance facilities.
"uace_code", -- UACE Code remains consistent across census years.
"administrative_office_sales", -- Number of facilities inventoried by each agency which house the executive management and supporting activities for transit operations, with the exception of vehicle maintenance, that could include accounting, finance, engineering, legal, safety, security, customer services, scheduling and planning. These buildings may include customer information or ticket sale offices, which are owned by the transit agency but not part of passenger stations.
"maintenance_facilities", -- Total of all maintenance facility subcategories.
"uza_name", -- The name of the agency's Urbanized Area.
"vehicle_fueling_facility" -- Number of stand-alone buildings or structures inventoried by each agency containing vehicle fuel dispensing equipment.
FROM
"datahub-transportation-gov/2022-ntd-annual-data-stations-and-facilities-by-aqct-knjk:latest"."2022_ntd_annual_data_stations_and_facilities_by"
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-stations-and-facilities-by-aqct-knjk 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-stations-and-facilities-by-aqct-knjk: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-stations-and-facilities-by-aqct-knjkCheckout 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-stations-and-facilities-by-aqct-knjk:latestThis will download all the objects for the latest tag of datahub-transportation-gov/2022-ntd-annual-data-stations-and-facilities-by-aqct-knjk 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-stations-and-facilities-by-aqct-knjk: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-stations-and-facilities-by-aqct-knjk: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-stations-and-facilities-by-aqct-knjk is just another Postgres schema.