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-p8a5-sdg4:latest"."2019_kansas_city_energy_and_water_consumption"or in a full query, like:
SELECT
":id", -- Socrata column ID
"year_ending", -- The last date in the full year period in which metrics are taken
"state_province", -- State the building is located in: Missouri
"weather_normalized_source", -- Energy use per SQFT that has been normalized to account for both weather and source of energy
"direct_ghg_emissions_metric", -- Direct GHG (low amounts tend to round to 0)
"indirect_ghg_emissions_metric", -- Indirect GHG (low amounts tend to round to 0)
"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.
"postal_code", -- The zip code the building is located in.
"city", -- City that the building is located in: Kansas City Missouri.
"kansas_city_building_reporting", -- Unique (with a few exceptions) ID that identifies the building being submitted.
"weather_normalized_site_eui", -- Energy use per SQFT that has been normalized to account for weather. compared to Site, this is more reflective of what you'd find on a utility billing statement.
"street_address", -- Street Address
"property_name", -- Building name assigned by point of contact for property
"primary_property_type", -- A calculated property use type based of the use that has the most SQFT assigned to it.
"property_gfa_calculated", -- Total SQFT between the building and parking
"water_use_all_water_sources", -- Water metric from indoor, outdoor, and mixed meters
"property_gfa_calculated_2", -- Total Parking SQFT (does not include building SQFT)
"property_gfa_calculated_1", -- Total building SQFT (does not include parking)
"primary_property_type_self" -- A self-selected main property use type
FROM
"kcmo/2019-kansas-city-energy-and-water-consumption-p8a5-sdg4: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-p8a5-sdg4 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-p8a5-sdg4: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-p8a5-sdg4Checkout 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-p8a5-sdg4:latestThis will download all the objects for the latest tag of kcmo/2019-kansas-city-energy-and-water-consumption-p8a5-sdg4 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-p8a5-sdg4: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-p8a5-sdg4: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-p8a5-sdg4 is just another Postgres schema.