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 better_buildings_challenge_properties table in this repository, by referencing it like:
"norfolk-gov/better-buildings-challenge-properties-ykcq-76im:latest"."better_buildings_challenge_properties"or in a full query, like:
SELECT
":id", -- Socrata column ID
"energy_cost_per_square_foot", -- The Energy Cost divided by the Gross Floor Area.
"longitude", -- The longitude coordinate of the property.
"national_median_site_energy_1", -- The national median site energy use units.
"number_of_buildings", -- Indicates the total number of buildings that are located on the property.
"national_median_site_energy", -- The national median site energy use of similar properties in Energy Star Portfolio Manager. 50% of properties nationwide perform below the median, and 50% perform above the median. For more information, see this link: https://portfoliomanager.energystar.gov/pm/glossary#NationalMedian
"site_energy_use_units", -- The Site Energy Use divided by the Gross Floor Area.
"site_energy_use", -- The annual amount of all the energy the property consumes on site, regardless of source (electrical, natural gas, etc.). It includes energy purchased from the grid or in bulk, as well as renewable energy generated and consumed on-site. Expressed in kBTUs (one-thousand British thermal units).
"rolling_year_end_date", -- The last day of a 12-month period on which a set of metrics are based. This date starts on the first day of one month and ends 12 months later on the last day of the last month (e.g., 6/1/2013 – 5/31/2014). All metrics in this dataset are calculated based on 12 full calendar months of data.
":@computed_region_25t2_rbz7", -- This column was automatically created in order to record in what polygon from the dataset 'US Counties' (25t2-rbz7) the point in column 'geographic_point' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_x6fk_ihs5", -- This column was automatically created in order to record in what polygon from the dataset 'Civic Leagues' (x6fk-ihs5) the point in column 'geographic_point' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"geographic_point", -- For mapping purposes, showing the geographic point of the property.
"property_id", -- A unique identifier number assigned by the Environmental Protection Agency (EPA) to the property.
"property_name", -- The name of the property as defined by the City of Norfolk.
"address", -- The address location of the property.
"latitude", -- The latitude coordinate of the property.
"gross_floor_area", -- The total property square footage, including if there is more than one building on the property.
"source_energy_use", -- The total amount of raw fuel that is required to operate the property. In addition to what the property consumes on-site, source energy includes losses that take place during generation, transmission, and distribution of the energy, thereby enabling a complete assessment of energy consumption resulting from building operations. Expressed in kBTUs (one-thousand British thermal units). For more information, see this link: https://portfoliomanager.energystar.gov/pm/glossary#SourceEnergy
"source_energy_use_units", -- The Source Energy Use divided by the Gross Floor Area. Source Energy Use Units is the best way to quantify the energy performance of properties.
"year_built", -- The year in which the property was constructed.
"property_type", -- Indicates the primary use of the property, such as “Courthouse”, “Stadium (Open)”, “Office”, “Fire Station”, among others.
"national_median_source_energy", -- The national median source energy use.
"national_median_source_energy_1", -- The national median source energy use units.
"percent_difference_from", -- The percentage difference for between the property’s source energy use units and the national median.
"energy_cost" -- The total cost, in dollars of the property’s energy use for the 12-month time period.
FROM
"norfolk-gov/better-buildings-challenge-properties-ykcq-76im:latest"."better_buildings_challenge_properties"
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 norfolk-gov/better-buildings-challenge-properties-ykcq-76im 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 norfolk-gov/better-buildings-challenge-properties-ykcq-76im: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 norfolk-gov/better-buildings-challenge-properties-ykcq-76imCheckout 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 norfolk-gov/better-buildings-challenge-properties-ykcq-76im:latestThis will download all the objects for the latest tag of norfolk-gov/better-buildings-challenge-properties-ykcq-76im 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 norfolk-gov/better-buildings-challenge-properties-ykcq-76im: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 norfolk-gov/better-buildings-challenge-properties-ykcq-76im: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, norfolk-gov/better-buildings-challenge-properties-ykcq-76im is just another Postgres schema.