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_track_roadway_by_mode table in this repository, by referencing it like:
"datahub-transportation-gov/2022-ntd-annual-data-track-roadway-by-mode-fzbb-f6kc:latest"."2022_ntd_annual_data_track_roadway_by_mode"or in a full query, like:
SELECT
":id", -- Socrata column ID
"slip_switch", -- Number of slip switches inventoried by the transit agency.
"lapped_turnout_q", -- 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.
"lapped_turnout", -- Number of lapped turnouts inventoried by the transit agency.
"double_crossover_q", -- 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.
"single_crossover_q", -- 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.
"single_crossover", -- Number of single crossovers inventoried by the transit agency.
"grade_crossings", -- Number of grade crossings inventoried by the transit agency.
"single_turnout", -- Number of single turnouts inventoried by the transit agency.
"exclusive_fixed_guideway", -- Lane miles over which public transit operates that are exclusive to other traffic at all times, 24 hours per day, seven days per week.
"below_grade_submerged_tube_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.
"below_grade_submerged_tube", -- Number of this guideway element type inventoried by the transit agency.
"elevated_steel_viaduct_or", -- Number of this guideway element type inventoried by the transit agency.
"elevated_concrete", -- Number of this guideway element type inventoried by the transit agency.
"elevated_retained_fill_q", -- 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.
"elevated_retained_fill", -- Number of this guideway element type inventoried by the transit agency.
"total_track_miles", -- Total track miles (sum of previous track mile-related columns).
"rail_crossings_q", -- 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.
"rail_crossings", -- Number of rail crossings inventoried by the transit agency.
"controlled_access_high_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.
"slip_switch_q", -- 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.
"grade_crossings_q", -- 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.
"single_turnout_q", -- 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_track_miles_q", -- 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.
"below_grade_bored_or_blasted_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.
"below_grade_bored_or_blasted", -- Number of this guideway element type inventoried by the transit agency.
"below_grade_cut_and_cover_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.
"below_grade_cut_and_cover", -- Number of this guideway element type inventoried by the transit agency.
"below_grade_retained_cut_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.
"below_grade_retained_cut", -- Number of this guideway element type inventoried by the transit agency.
"elevated_steel_viaduct_or_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.
"elevated_concrete_q", -- 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.
"at_grade_ballast_including_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.
"report_year", -- The year for which the data was reported.
"total_miles", -- Total roadway miles.
"controlled_access_high", -- Lane miles over which transit operates that may be exclusive to transit or function as HOV for a certain number of hours but are open to general traffic for some part of the week.
"exclusive_high_intensity_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.
"exclusive_high_intensity", -- Lane miles over which transit operates that are HOV lanes at all times, 24 hours per day, seven days per week or alternatively may be HOV lanes for a portion of the week and exclusive to transit for the remainder of the week.
"exclusive_fixed_guideway_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.
"at_grade_in_street_embedded_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.
"at_grade_in_street_embedded", -- Number of this guideway element type inventoried by the transit agency.
"at_grade_ballast_including", -- Number of this guideway element type inventoried by the transit agency.
"mode_voms", -- The number of revenue vehicles operated by the given mode and type of service 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.
"type_of_service", -- Describes how public transportation services are provided by the transit agency: directly operated (DO) or purchased transportation (PT) services.
"mode_name", -- A system for carrying transit passengers described by specific right-of-way (ROW), technology and operational features.
"mode", -- A system for carrying transit passengers described by specific right-of-way (ROW), technology and operational features.
"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.
"primary_uza_population", -- The population of the urbanized area primarily served by the agency.
"uza_name", -- The name of the agency's Urbanized Area.
"uace_code", -- UACE Code remains consistent across census years.
"reporter_type", -- The type of NTD report that the agency completed this year.
"organization_type", -- Description of the agency's legal entity.
"ntd_id", -- A five-digit identifying number for each agency used in the current NTD system.
"state", -- The state in which the agency is headquartered.
"city", -- The city in which the agency is headquartered.
"agency", -- The transit agency's name.
"double_crossover" -- Number of double crossovers for inventoried by the transit agency.
FROM
"datahub-transportation-gov/2022-ntd-annual-data-track-roadway-by-mode-fzbb-f6kc:latest"."2022_ntd_annual_data_track_roadway_by_mode"
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-track-roadway-by-mode-fzbb-f6kc 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-track-roadway-by-mode-fzbb-f6kc: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-track-roadway-by-mode-fzbb-f6kcCheckout 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-track-roadway-by-mode-fzbb-f6kc:latestThis will download all the objects for the latest tag of datahub-transportation-gov/2022-ntd-annual-data-track-roadway-by-mode-fzbb-f6kc 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-track-roadway-by-mode-fzbb-f6kc: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-track-roadway-by-mode-fzbb-f6kc: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-track-roadway-by-mode-fzbb-f6kc is just another Postgres schema.