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 2017_annual_metrics_national_transit_database table in this repository, by referencing it like:
"datahub-transportation-gov/2017-annual-metrics-national-transit-database-v6zb-d5rv:latest"."2017_annual_metrics_national_transit_database"or in a full query, like:
SELECT
":id", -- Socrata column ID
"mode", -- A system for carrying transit passengers described by specific right-of-way (ROW), technology and operational features.
"passengers_per_hour", -- The average number of passengers to board a vehicle/passenger car in one hour of service.
"organization_type", -- Description of the agency's legal entity.
"fare_revenues_per_total", -- The proportion of operating expenses that are paid for by fare revenues.
"ratios",
"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.
"legacy_ntd_id", -- A four-digit identifying number for each agency used in the legacy NTD system.
"fare_revenues_earned", -- Fares earned by the given mode/type of service.
"unlinked_passenger_trips_1",
"source_data",
"cost_per_passenger_mile_1",
"cost_per_passenger_mile", -- The average cost to transport one passenger one mile.
"state", -- The state in which the agency is headquartered.
"fare_revenues_earned_1",
"cost_per_passenger", -- The average cost to transport one passenger from the beginning of her trip to the end.
"passengers_per_hour_1",
"vehicle_revenue_hours_1",
"cost_per_hour_questionable",
"reporter_type", -- The type of NTD report that the agency completed this year.
"tos", -- Describes how public transportation services are provided by the transit agency: directly operated (DO) or purchased transportation (PT) services.
"cost_per_passenger_1",
"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.
"cost_per_hour", -- The average cost to operate one vehicle/passenger car for one hour of passenger service.
"primary_uza_population", -- The population of the urbanized area primarily served by the agency.
"ntd_id", -- A five-digit identifying number for each agency used in the current NTD system.
"vehicle_revenue_miles", -- The miles that vehicles (or passenger cars, for rail service) travel while in revenue service. Vehicle revenue miles exclude deadhead, operator training, maintenance testing, and school bus and charter services.
"passenger_miles_questionable",
"passenger_miles", -- The sum of the distances ridden by all passengers during the entire Fiscal Year.
"city", -- The city in which the agency is headquartered.
"vehicle_revenue_hours", -- Total number of hours that vehicles/passenger cars traveled while in revenue service during the report year. Includes both typical and atypical service. Excludes deadhead.
"unlinked_passenger_trips", -- The number of passengers who boarded public transportation vehicles. Passengers are counted each time they board a vehicle no matter how many vehicles they use to travel from their origin to their destination.
"name", -- The transit agency's name.
"total_operating_expenses_1",
"fare_revenues_per_unlinked_1",
"fare_revenues_per_total_1",
"total_operating_expenses", -- Total of all operating expenses for the mode/type of service.
"fare_revenues_per_unlinked" -- The average fare collected per passenger.
FROM
"datahub-transportation-gov/2017-annual-metrics-national-transit-database-v6zb-d5rv:latest"."2017_annual_metrics_national_transit_database"
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/2017-annual-metrics-national-transit-database-v6zb-d5rv 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/2017-annual-metrics-national-transit-database-v6zb-d5rv: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/2017-annual-metrics-national-transit-database-v6zb-d5rvCheckout 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/2017-annual-metrics-national-transit-database-v6zb-d5rv:latestThis will download all the objects for the latest tag of datahub-transportation-gov/2017-annual-metrics-national-transit-database-v6zb-d5rv 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/2017-annual-metrics-national-transit-database-v6zb-d5rv: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/2017-annual-metrics-national-transit-database-v6zb-d5rv: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/2017-annual-metrics-national-transit-database-v6zb-d5rv is just another Postgres schema.