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_employees_by_mode_and table in this repository, by referencing it like:
"datahub-transportation-gov/2022-ntd-annual-data-employees-by-mode-and-uyv8-9jek:latest"."2022_ntd_annual_data_employees_by_mode_and"or in a full query, like:
SELECT
":id", -- Socrata column ID
"general_administration_hours_q", -- Indicates whether there was questionable data for the given given data point.
"total_hours_q", -- Indicates whether there was questionable data for the given given data point.
"vehicle_maintenance_hours", -- Hours worked in the Vehicle Maintenance Function. The Vehicle Maintenance function includes wages, salaries and expenses incurred during all activities related to keeping vehicles operational and in good repair, including administrative and clerical support.
"vehicle_maintenance_count", -- Count of employees (prorated across functions, modes, and types of service) working in the Vehicle Maintenance Function. The Vehicle Operations function includes wages, salaries and expenses related to all activities associated with dispatching and running vehicles to carry passengers, including management, administrative and clerical support.
"vehicle_operations_count", -- Count of employees (prorated across functions, modes, and types of service) working in the Vehicle Operations Function. The Vehicle Operations function includes wages, salaries and expenses related to all activities associated with dispatching and running vehicles to carry passengers, including management, administrative and clerical support.
"vehicle_maintenance_hours_q", -- Indicates whether there was questionable data for the given given data point.
"uza_name", -- The name of the agency's Primary Urbanized Area.
"facility_maintenance_hours", -- Hours worked in the Facility Maintenance Function. The Facility Maintenance function includes all activities related to keeping buildings, structures, roadways, track, and other non-vehicle assets operational and in good repair, including administrative and clerical support.
"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.
"uace_code", -- The UACE Code uniquely identifies an urban area and remains consistent across decennial Censuses.
"capital_labor_hours_q", -- Indicates whether there was questionable data for the given given data point.
"report_year", -- The year for which the data was reported.
"full_or_part_time", -- Indicates whether the row reported reflects counts and hours worked by Full Time vs. Part Time transit agency employees.
"city", -- The city in which the agency is headquartered.
"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.
"capital_labor_count", -- Count of employees (prorated across functions, modes, and types of service) involved in the transit agency's capital projects.
"state", -- The State in which the agency is headquartered.
"agency", -- The transit agency's legal name.
"vehicle_operations_hours_q", -- Indicates whether there was questionable data for the given given data point.
"type_of_service", -- Describes how public transportation services are provided by the transit agency: directly operated (DO) or purchased transportation (PT) services.
"general_administration_hours", -- Hours worked in the General Administration Function. The General Administration function includes wages, salaries, and expenses incurred to perform support and administrative activities.
"facility_maintenance_hours_q", -- Indicates whether there was questionable data for the given given data point.
"capital_labor_count_q", -- Indicates whether there was questionable data for the given given data point.
"mode_name", -- A system for carrying transit passengers described by specific right-of-way (ROW), technology and operational features.
"vehicle_operations_hours", -- Hours worked in the Vehicle Operations Function. The Vehicle Operations function includes wages, salaries and expenses related to all activities associated with dispatching and running vehicles to carry passengers, including management, administrative and clerical support.
"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.
"general_administration_count", -- Count of employees (prorated across functions, modes, and types of service) working in the General Administration Function. The Vehicle Operations function includes wages, salaries and expenses related to all activities associated with dispatching and running vehicles to carry passengers, including management, administrative and clerical support.
"primary_uza_population", -- The population of the urbanized area primarily served by the agency.
"mode", -- A system for carrying transit passengers described by specific right-of-way (ROW), technology and operational features.
"facility_maintenance_count", -- Count of employees (prorated across functions, modes, and types of service) working in the Facility Maintenance Function. The Vehicle Operations function includes wages, salaries and expenses related to all activities associated with dispatching and running vehicles to carry passengers, including management, administrative and clerical support.
"capital_labor_hours", -- Number of hours worked on capital projects by transit agency employees.
"vehicle_operations_count_q", -- Indicates whether there was questionable data for the given given data point.
"total_hours", -- The total number of hours worked across all functions by Full Time or Part Time employees of the transit agency during the fiscal year.
"vehicle_maintenance_count_q", -- Indicates whether there was questionable data for the given given data point.
"facility_maintenance_count_q", -- Indicates whether there was questionable data for the given given data point.
"general_administration_count_q", -- Indicates whether there was questionable data for the given given data point.
"total_employee_count", -- The number of Full Time or Part Time employees of the transit agency at the end of the fiscal year.
"total_employee_count_q" -- Indicates whether there was questionable data for the given given data point.
FROM
"datahub-transportation-gov/2022-ntd-annual-data-employees-by-mode-and-uyv8-9jek:latest"."2022_ntd_annual_data_employees_by_mode_and"
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-employees-by-mode-and-uyv8-9jek 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-employees-by-mode-and-uyv8-9jek: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-employees-by-mode-and-uyv8-9jekCheckout 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-employees-by-mode-and-uyv8-9jek:latestThis will download all the objects for the latest tag of datahub-transportation-gov/2022-ntd-annual-data-employees-by-mode-and-uyv8-9jek 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-employees-by-mode-and-uyv8-9jek: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-employees-by-mode-and-uyv8-9jek: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-employees-by-mode-and-uyv8-9jek is just another Postgres schema.