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 residential_existing_homes_onetofour_units_energy table in this repository, by referencing it like:
"ny-gov/residential-existing-homes-onetofour-units-energy-4a2x-yp8g:latest"."residential_existing_homes_onetofour_units_energy"or in a full query, like:
SELECT
":id", -- Socrata column ID
"number_of_units", -- Number of units served by the Program. Data may include exceptions to the One-to-Four units, which were approved by NYSERDA on a case-by-case basis
"location_1_zip",
"location_1_city",
"first_year_modeled_project_energy_savings_estimate", -- Estimated post-retrofit first year dollar savings (USD). Negative numbers represent projects with estimated post-retrofit first year dollar expenses, typically occurring when non-energy work was completed such as health and safety improvements, or when work was done in conjunction with another, net positive energy savings project. Projects with zero energy savings dollars represent projects with only health and safety measures, and customer efficiency education
"estimated_annual_mmbtu_savings", -- Annual post-retrofit modeled MMBtu savings based on primary fuel type. Negative numbers represent projects with post-retrofit increase in MMBtu consumption, typically from fuel conversions or ancillary savings. Projects with zero MMBtu represent projects with only health and safety measures, and customer efficiency education
"estimated_annual_kwh_savings", -- Annual post-retrofit modeled electric savings estimate in kWh. Negative numbers represent projects with post-retrofit increase in electric consumption, typically from fuel conversions or ancillary savings. Projects with zero kWh represent projects with only health and safety measures, and customer efficiency education
"measure_type", -- Measure classification describing primary project improvement defined as Combination-Home Performance, Combination-Electric Reduction, Heating Repair/Replacement, Refrigerator/Freezer Replacement, CFL/LED Lighting, Shell, Shower Head Replacement, or Other
"year_home_built", -- Home construction date. Blank cells indicate data not reported by the contractor
"pre_retrofit_home_heating_fuel_type", -- Indicates the pre-retrofit primary heating fuel type. Either coal, electricity, kerosene, natural gas, oil, other, pellets, propane, or wood
"total_project_cost", -- Cost of project (USD). NYSERDA incentive currently at 100% of the total project cost. Total Project Costs less than $100 often reflects mileage-only billing for projects with minor work scopes
"project_completion_date", -- Date final project completion paperwork was reviewed and approved by Program
"electric_utility", -- Name of electric utility for project location
"gas_utility", -- Name of gas utility for project location. If blank, then utility was not reported, or project location is not served by a gas utility
"project_zip", -- ZIP code for project location
"project_county", -- Name of county for project location
"reporting_period", -- The time period covered by the dataset
"job_type", -- Indicates whether the project includes only electric reduction measures (Electric Reduction) or is a comprehensive (Home Performance) project including both electric and heating efficiency improvements
"project_id", -- Unique identifier for project
"location_1", -- Open Data/Socrata-generated geocoding information
"size_of_home", -- Square footage of home. Blank cells indicate data not reported by the contractor
"project_city", -- Name of city for project location
"location_1_address",
"type_of_dwelling", -- General home category describing the dwelling as Single Family, 2-4 Family, Multi Family, or Manufactured/Mobile Home
":@computed_region_kjdx_g34t",
"location_1_state",
":@computed_region_yamh_8v7k",
":@computed_region_wbg7_3whc"
FROM
"ny-gov/residential-existing-homes-onetofour-units-energy-4a2x-yp8g:latest"."residential_existing_homes_onetofour_units_energy"
LIMIT 100;Connecting to the DDN is easy. All you need is an existing SQL client that can connect to Postgres. As long as you have a SQL client ready, you'll be able to query ny-gov/residential-existing-homes-onetofour-units-energy-4a2x-yp8g with SQL in under 60 seconds.
Query Your Local Engine
bash -c "$(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 ny-gov/residential-existing-homes-onetofour-units-energy-4a2x-yp8g: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 ny-gov/residential-existing-homes-onetofour-units-energy-4a2x-yp8gCheckout 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 ny-gov/residential-existing-homes-onetofour-units-energy-4a2x-yp8g:latestThis will download all the objects for the latest tag of ny-gov/residential-existing-homes-onetofour-units-energy-4a2x-yp8g 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 ny-gov/residential-existing-homes-onetofour-units-energy-4a2x-yp8g: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 ny-gov/residential-existing-homes-onetofour-units-energy-4a2x-yp8g: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, ny-gov/residential-existing-homes-onetofour-units-energy-4a2x-yp8g is just another Postgres schema.