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.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at datahub.transportation.gov. When you querydatahub-transportation-gov/2017-annual-metrics-national-transit-database-v6zb-d5rv:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).
We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.
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 (like this repository), 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, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Mounting Data
This repository is an external repository. It's not hosted by Splitgraph. It is hosted by datahub.transportation.gov, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.
First, install Splitgraph if you haven't already.
Mount the table with sgr mount
sgr mount socrata \
"datahub-transportation-gov/2017-annual-metrics-national-transit-database-v6zb-d5rv" \
--handler-options '{
"domain": "datahub.transportation.gov",
"tables": {
"2017_annual_metrics_national_transit_database": "v6zb-d5rv"
}
}'That's it! Now you can query the data in the mounted table like any other Postgres table.
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.