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_federal_funding_allocation table in this repository, by referencing it like:
"datahub-transportation-gov/2022-ntd-annual-data-federal-funding-allocation-5x22-djnv:latest"."2022_ntd_annual_data_federal_funding_allocation"or in a full query, like:
SELECT
":id", -- Socrata column ID
"non_fixed_guideway_operating_expenses", -- Agency allocation of mode and type of service non-fixed guideway operating expense to a particular UZA (see Vehicle Revenue Miles and Fixed Guideway).
"mode_name", -- A system for carrying transit passengers described by specific right-of-way (ROW), technology and operational features.
"year", -- The report year for which the given record is reported. Reflects transit agency Fiscal Year that begins in the year given.
"urbanized_area_square_miles", -- The square miles of the Urban Area served by the agency.
"urbanized_area_population", -- The population of the Urban Area served by the agency.
"urbanized_area_name", -- The name of the Urban Area served by the agency.
"urbanized_area_code", -- The Census Code that uniquely identifies an urban area.
"type_of_service", -- Describes how public transportation services are provided by the transit agency: directly operated (DO) or purchased transportation (PT) services.
"non_fixed_guideway_vehicle_revenue_miles", -- Agency allocation of mode and type of service non-fixed guideway vehicle revenue miles to a particular UZA (see Vehicle Revenue Miles and Fixed Guideway).
"non_fixed_guideway_passenger_miles", -- Agency allocation of mode and type of service non-fixed guideway passenger miles traveled to a particular UZA (see Passenger Miles Traveled and Fixed Guideway).
"fg_nfg_reporting_method", -- Indicates what data point the agency used to allocate fixed guideway data for each data point specific to each urbanized area.
"state_ntd_id",
"fg_vrm_questionable",
"fg_oe_questionable",
"drm_questionable",
"nfg_pmt_questionable",
"nfg_vrm_questionable",
"total_oe_questionable",
"total_pmt_questionable",
"total_actual_vrm_questionable",
"total_upt_questionable",
"total_actual_vrh_questionable",
"primary_uza", -- Indicates whether the UZA is the primary operating area of the given Agency.
"sgr_hib_vehicle_revenue_miles", -- Agency allocation of mode and type of service high intensity bus vehicle revenue miles greater than or equal to 7 year for a particular UZA (see Vehicle Revenue Miles and Fixed Guideway).
"sgr_hib_directional_route_miles", -- Agency allocation of mode and type of service high intensity bus directional route miles greater than or equal to 7 year for a particular UZA (see Directional Route Miles).
"sgr_fg_vehicle_revenue_miles", -- Agency allocation of mode and type of service vehicle revenue miles greater than or equal to 7 year old's for a particular UZA (see Vehicle Revenue Miles and Fixed Guideway).
"sgr_fg_directional_route_miles", -- Agency allocation of mode and type of service directional route miles greater than or equal to 7 years old for a particular UZA (see Directional Route Miles).
"directional_route_miles", -- Agency allocation of mode and type of service fixed guideway directional route miles to a particular UZA (see Directional Route Miles). See NTD Annual Service dataset for definition of underlying data point.
"fixed_guideway_operating_expenses", -- Agency allocation of mode and type of service fixed guideway operating expense to a particular UZA (see Operating Expenses and Fixed Guideway).
"fixed_guideway_passenger_miles", -- Agency allocation of mode and type of service fixed-guideway passenger miles traveled to a particular UZA (see Passenger Miles Traveled and Fixed Guideway).
"fixed_guideway_vehicle_revenue_miles", -- Agency allocation of mode and type of service fixed guideway vehicle revenue miles to a particular UZA (see Vehicle Revenue Miles and Fixed Guideway).
"total_operating_expenses", -- Agency allocation of mode and type of service operating expenses to the respective UZA (see Operating Expenses).
"total_passenger_miles_traveled", -- Agency allocation of mode and type of service passenger miles traveled to the respective UZA (see Passenger Miles Traveled).
"total_actual_vrm", -- Agency allocation of vehicle revenue miles to the respective UZA (see Vehicle Revenue Hours)
"total_unlinked_passenger_trips", -- Agency allocation of mode and type of service unlinked passenger trips to the respective UZA (see Unlinked Passenger Trips).
"total_actual_vehicle_revenue_hours", -- Agency allocation of vehicle revenue hours to the respective UZA (see Vehicle Revenue Hours)
"uza_reporting_method", -- Indicates what data point the agency used to allocate data for each data point specific to each urbanized area.
"mode", -- A system for carrying transit passengers described by specific right-of-way (ROW), technology and operational features.
"reporter_type", -- The type of NTD report that the agency completed this year.
"_5_digit_ntd_id", -- A five-digit identifying number for each agency used in the current NTD system.
"agency" -- The transit agency's legal name.
FROM
"datahub-transportation-gov/2022-ntd-annual-data-federal-funding-allocation-5x22-djnv:latest"."2022_ntd_annual_data_federal_funding_allocation"
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-federal-funding-allocation-5x22-djnv 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-federal-funding-allocation-5x22-djnv: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-federal-funding-allocation-5x22-djnvCheckout 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-federal-funding-allocation-5x22-djnv:latestThis will download all the objects for the latest tag of datahub-transportation-gov/2022-ntd-annual-data-federal-funding-allocation-5x22-djnv 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-federal-funding-allocation-5x22-djnv: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-federal-funding-allocation-5x22-djnv: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-federal-funding-allocation-5x22-djnv is just another Postgres schema.