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 2019_kansas_city_energy_and_water_consumption table in this repository, by referencing it like:
"kcmo/2019-kansas-city-energy-and-water-consumption-iy94-ug87:latest"."2019_kansas_city_energy_and_water_consumption"or in a full query, like:
SELECT
":id", -- Socrata column ID
"direct_ghg_emissions_metric", -- Direct GHG emissions
"city", -- City that the building is located in: Kansas City, Missouri.
"street_address", -- Street Address
"national_median_source_energy", -- The median Source Energy use across building with the same use type and size
"direct_ghg_emissions_intensity", -- Direct GHG emissions per SQFT
"district_hot_water_use_kbtu", -- District Hot Water usage
"electricity_use_generated", -- Renewable Electricity usage
"cooling_degree_days_cdd_f", -- Number of days you would have to cool your building by 1 degree to accommodate the heating requirement.
"source_energy_use_kbtu", -- Total Source Energy use among all meter source types
"source_eui_kbtu_ft", -- Source Energy Use per SQFT
"weather_normalized_site_eui", -- Site Energy use per square foot (normalized for weather)
"weather_normalized_source_1", -- Site Energy use per square foot (normalized for weather)
"district_steam_use_kbtu", -- District steam usage
"total_ghg_emissions_metric", -- Total GHG emissions
"state_province", -- State the building is located in: Missouri
"property_name", -- Building name assigned by point of contact for property
"year_ending", -- The last date in the full year period in which metrics are taken
"postal_code", -- The zip code the building is located in.
"weather_station_name", -- Name of weather station that weather data is being taken from
"heating_degree_days_hdd_f", -- Number of days you would have to heat your building by 1 degree to accommodate the heating requirement.
"electricity_use_grid_purchase", -- Electricity grid usage
"total_ghg_emissions_intensity", -- Total GHG emissions per SQFT
"district_chilled_water_use", -- District Chilled Water usage
"indirect_ghg_emissions_metric", -- Indirect GHG emissions
"water_use_intensity_all_water", -- Water usage per square foot
"water_use_all_water_sources", -- Water usage
"energy_cost_intensity_ft", -- Amount spent on energy per square foot
"energy_cost", -- Amount spent on Energy
"weather_normalized_source", -- Total Source Energy use among all meter source types (normalized for weather)
"weather_normalized_site_energy", -- Total Site Energy use among all meter source types (normalized for weather)
"difference_from_national_1", -- % Difference between median and actual
"national_median_source_eui", -- The median Source Energy use per SQFT across building with the same use type and size
"difference_from_national", -- % Difference between median and actual
"property_gfa_calculated", -- Total SQFT between the building and parking
"property_gfa_calculated_1", -- Total building SQFT (does not include parking)
"property_gfa_calculated_2", -- Total Parking SQFT (does not include building SQFT)
"primary_property_type_self", -- A self-selected main property use type
"list_of_all_property_use", -- All property uses identified for the building
"energy_star_score", -- A score generated by the national Energy Star Program used to compare the building to other similar building types. The score is out of 1-100 where 50 is the median and 75 is at the upper quartile. Not all buildings are eligible for a score.
"site_energy_use_kbtu", -- Total Site Energy use among all meter source types
"site_eui_kbtu_ft", -- Site Energy use per square foot
"national_median_site_eui", -- The median Site Energy use per SQFT across building with the same use type and size
"national_median_site_energy", -- The median Site Energy use across building with the same use type and size
"indirect_ghg_emissions", -- Indirect GHG emissions per SQFT
"egrid_subregion", -- Subregion of eGRID which is the source of data on the environmental characteristics of almost all electric power generated in the United States.
"egrid_output_emissions_rate", -- This is the emissions taken from the eGRID subregion
"kansas_city_building_reporting", -- Unique building ID for identification
"natural_gas_use_kbtu" -- Natural gas usage
FROM
"kcmo/2019-kansas-city-energy-and-water-consumption-iy94-ug87:latest"."2019_kansas_city_energy_and_water_consumption"
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 kcmo/2019-kansas-city-energy-and-water-consumption-iy94-ug87 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 kcmo/2019-kansas-city-energy-and-water-consumption-iy94-ug87: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 kcmo/2019-kansas-city-energy-and-water-consumption-iy94-ug87Checkout 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 kcmo/2019-kansas-city-energy-and-water-consumption-iy94-ug87:latestThis will download all the objects for the latest tag of kcmo/2019-kansas-city-energy-and-water-consumption-iy94-ug87 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 kcmo/2019-kansas-city-energy-and-water-consumption-iy94-ug87: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 kcmo/2019-kansas-city-energy-and-water-consumption-iy94-ug87: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, kcmo/2019-kansas-city-energy-and-water-consumption-iy94-ug87 is just another Postgres schema.