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 multifamily_residential_existing_and_new table in this repository, by referencing it like:
"ny-gov/multifamily-residential-existing-and-new-xt6e-eyna:latest"."multifamily_residential_existing_and_new"or in a full query, like:
SELECT
":id", -- Socrata column ID
"measure_category", -- Category of upgrade - from dropdown list of options BEDES Term: Technology Category
"program_type", -- Indicates if the project was part of NYSERDA’s Existing Buildings or New Construction program BEDES Term: Program Name Identifier
"georeference", -- Open Data/Socrata-generated geocoding information from supplied address components.
"est_annual_energy_sav_propane", -- Estimated annual propane savings or consumption. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings.
"total_est_electric_demand_reduc", -- Estimated annual measure electricity demand resource savings. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings. BEDES Term: Projected Annual Electricity Summer Demand Resource Savings
"project_name", -- Unique project name BEDES Term: Project Name
":@computed_region_kjdx_g34t", -- This column was automatically created in order to record in what polygon from the dataset 'Counties' (kjdx-g34t) the point in column 'georeference' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_wbg7_3whc", -- This column was automatically created in order to record in what polygon from the dataset 'New York Zip Codes' (wbg7-3whc) the point in column 'georeference' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"est_annual_energy_sav_oil", -- Estimated annual #2/distillate oil savings or consumption. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings. BEDES Term: Projected Annual Fuel Oil Resource Value
"building_zip", -- ZIP Code where project is located. Blank cells represent data that were not required or are not currently available
"proposed_install_unit_cost", -- Total proposed cost of installed measures in U.S dollars. A value of 0 indicate that the measure was installed in house, with no cost incurred. Blank cells represent data that were not required or are not currently available BEDES* Term: Proposed Measure Total Cost
"building_city", -- City where project is located. Blank cells represent data that were not required or are not currently available
"building_address", -- Street Address of building where project is located. Blank cells represent data that were not required or are not currently available BEDES* Term: Premise Address Line 1
"measure", -- Name of upgrade - from dropdown list of options based on "Measure Category" field selection BEDES Term: Measure Name Identifier
"est_annual_energy_sav_other", -- Pre-construction estimated annual savings or consumption from other energy types. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings.
":@computed_region_yamh_8v7k", -- This column was automatically created in order to record in what polygon from the dataset 'NYS Municipal Boundaries' (yamh-8v7k) the point in column 'georeference' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"percent_installed", -- Estimated percent of installation that is complete. Value will equal 100 when project is complete BEDES Term: Completed Percent of Total
"total_est_electric_savings", -- Estimated annual measure electricity Resource Savings. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings. BEDES Term: Projected Annual Electricity Resource Savings
"est_annual_energy_sav_natgas", -- Pre-construction estimated annual natural gas savings or consumption. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings. BEDES Term: Projected Annual Non Electric Energy Resource Savings
"est_annual_energy_sav_steam", -- Pre-construction estimated annual steam savings or consumption. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings. BEDES Term: Projected Annual District Steam Resource Value
"total_estimated_annual_energy_savings_mmbtu_" -- Estimated annual fuel savings or consumption. A positive number indicates savings and a negative number indicates new consumption. Zero or blank cells indicate no savings BEDES* Term: Projected Annual Non Electric Energy Resource Savings
FROM
"ny-gov/multifamily-residential-existing-and-new-xt6e-eyna:latest"."multifamily_residential_existing_and_new"
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/multifamily-residential-existing-and-new-xt6e-eyna 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/multifamily-residential-existing-and-new-xt6e-eyna: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/multifamily-residential-existing-and-new-xt6e-eynaCheckout 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/multifamily-residential-existing-and-new-xt6e-eyna:latestThis will download all the objects for the latest tag of ny-gov/multifamily-residential-existing-and-new-xt6e-eyna 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/multifamily-residential-existing-and-new-xt6e-eyna: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/multifamily-residential-existing-and-new-xt6e-eyna: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/multifamily-residential-existing-and-new-xt6e-eyna is just another Postgres schema.