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 building_energy_benchmarking_city_of_calgary table in this repository, by referencing it like:
"calgary-ca/building-energy-benchmarking-city-of-calgary-8twd-upbv:latest"."building_energy_benchmarking_city_of_calgary"or in a full query, like:
SELECT
":id", -- Socrata column ID
"avoided_emissions_onsite_and_offsite_green_power_metric_tons_co",
"green_power_onsite_and_offsite_kwh_",
"electricity_use_generated", -- The total amount of energy produced by onsite solar/wind).
"direct_ghg_emissions_metric", -- Direct Emissions are emissions associated with onsite fuel combustion (e.g. combustion of natural gas or fuel oil).
"site_energy_use_gj", -- The annual amount of all the energy a property consumes on-site, regardless of the source.
"district_hot_water_use_gj", -- Total annual energy consumed using district hot water.
"electricity_use_grid_purchase", -- Total annual electricity purchased from the grid.
"direct_ghg_emissions_intensity", -- Direct GHG Emissions divided by the property square meters.
"total_ghg_emissions_metric", -- Total Emissions is the sum of Direct Emissions and Indirect Emissions.
"weather_normalized_source_1", -- The Weather Normalized Source Energy Use divided by the property square meters.
"source_eui_gj_m", -- The Source Energy Use divided by the property square meters.
"weather_normalized_source", -- The source energy use your property would have consumed during 30-year average weather conditions.
"source_energy_use_gj", -- The total amount of all the raw fuel required to operate a property, including losses that take place during generation, transmission, and distribution of the energy.
"weather_normalized_site_eui", -- The Weather Normalized Site Energy Use divided by the property square meters.
"weather_normalized_site_energy", -- The energy use a property would have consumed during 30-year average weather conditions.
"number_of_buildings", -- Indicates the total number of buildings on the property
"property_name",
"natural_gas_use_gj", -- Total annual energy consumed using natural gas.
"total_ghg_emissions_intensity", -- Total GHG Emissions divided by the property square meters.
"property_gfa_self_reported", -- The total property gross floor area.
"postal_code",
"address_1",
"property_id", -- This is a unique ID assigned by ENERGY STAR Portfolio Manager to each property.
"unique_id", -- year-property_id
"year_ending", -- The last day of the 12-month reporting period
"site_eui_gj_m", -- The Site Energy Use divided by the property square meters.
"energy_star_score", -- A measure of how well a property is performing relative to similar properties, when normalized for climate and operational characteristics.
"year_built", -- This is the year the property was constructed or the year of the most recent major renovation including a complete interior redesign.
"primary_property_type_self", -- The property type that has been selected to most closely match the actual property type.
"province",
"city"
FROM
"calgary-ca/building-energy-benchmarking-city-of-calgary-8twd-upbv:latest"."building_energy_benchmarking_city_of_calgary"
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 calgary-ca/building-energy-benchmarking-city-of-calgary-8twd-upbv 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 calgary-ca/building-energy-benchmarking-city-of-calgary-8twd-upbv: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 calgary-ca/building-energy-benchmarking-city-of-calgary-8twd-upbvCheckout 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 calgary-ca/building-energy-benchmarking-city-of-calgary-8twd-upbv:latestThis will download all the objects for the latest tag of calgary-ca/building-energy-benchmarking-city-of-calgary-8twd-upbv 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 calgary-ca/building-energy-benchmarking-city-of-calgary-8twd-upbv: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 calgary-ca/building-energy-benchmarking-city-of-calgary-8twd-upbv: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, calgary-ca/building-energy-benchmarking-city-of-calgary-8twd-upbv is just another Postgres schema.