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_one_to_four_units table in this repository, by referencing it like:
"ny-gov/residential-existing-homes-one-to-four-units-jtrr-tvq4:latest"."residential_existing_homes_one_to_four_units"or in a full query, like:
SELECT
":id", -- Socrata column ID
"measure_estimated_annual_2", -- Predicted first year energy savings (MMBtu) - Category: Natural Gas. Negative numbers represent projects with predicted post-retrofit increase in consumption, typically from fuel conversions. A zero value represents no predicted energy savings for this fuel type.
"measure_estimated_annual_7", -- Predicted first year energy savings (MMBtu) - Category: Pellets. Negative numbers represent projects with predicted post-retrofit increase in consumption, typically from fuel conversions. A zero value represents no predicted energy savings for this fuel type.
"measure_estimated_annual_5", -- Predicted first year energy savings (MMBtu) - Category: Coal. Negative numbers represent projects with predicted post-retrofit increase in consumption, typically from fuel conversions. A zero value represents no predicted energy savings for this fuel type.
"measure_estimated_annual_4", -- Predicted first year energy savings (MMBtu) - Category: Propane. Negative numbers represent projects with predicted post-retrofit increase in consumption, typically from fuel conversions. A zero value represents no predicted energy savings for this fuel type.
"measure_estimated_annual_6", -- Predicted first year energy savings (MMBtu) - Category: Kerosene. Negative numbers represent projects with predicted post-retrofit increase in consumption, typically from fuel conversions. A zero value represents no predicted energy savings for this fuel type.
"project_id", -- Unique identifier for project, same as the Project ID field in the Residential Existing Homes (One to Four Units) Energy Efficiency Meter Evaluated Project Data: 2007 – 2012 (https://data.ny.gov/d/5vqm-4rpf) and the Residential Existing Homes (One to Four Units) Energy Efficiency Projects with Income-based Incentives by Customer Type: Beginning 2010 (https://data.ny.gov/d/assk-vu73) datasets
"measure_cost", -- Cost of measure in US dollars. Blank cells represent data that were not required or are not currently available
"measure_sub_category", -- Sub-Category of installed measure (ex. Air sealing, Boiler- Hot Water, etc.). Blank cells represent data that were not required or are not currently available
"measure_sir", -- Calculated savings to investment ratio (SIR) for the measure. Blank cells represent data that were not required or are not currently available
"measure_life", -- Period of measure performance used for determination of energy savings (years). Blank cells represent data that were not required or are not currently available
"measure_quantity", -- Quantity of installed measure. Blank cells represent data that were not required or are not currently available
"measure_estimated_annual_8", -- Predicted first year energy savings (MMBtu) - Category: Wood. Negative numbers represent projects with predicted post-retrofit increase in consumption, typically from fuel conversions. A zero value represents no predicted energy savings for this fuel type.
"measure_estimated_annual_3", -- Predicted first year energy savings (MMBtu) - Category: Oil. Negative numbers represent projects with predicted post-retrofit increase in consumption, typically from fuel conversions. A zero value represents no predicted energy savings for this fuel type.
"measure_estimated_annual_1", -- Predicted first year energy savings (MMBtu) - Category: All Fuels (non-electric energy sources). Negative numbers represent projects with predicted post-retrofit increase in consumption, typically from fuel conversions. A zero value represents no predicted energy savings for this fuel type.
"measure_incremental_energy", -- Calculated incremental energy savings (MMBtu) for the measure based on predicted first year energy savings
"measure_category", -- General implemented measure category; either Appliances & Lighting, Building Shell, Health & Safety, Primary Heating and Cooling, or Water Heater. Blank cells represent data that were not required or are not currently available
"measure_id", -- Unique identifier for the implemented measure
"measure_estimated_annual" -- Predicted first year energy savings (kWh) - Category: Electric. Negative numbers represent projects with predicted post-retrofit increase in consumption, typically from fuel conversions. A zero value represents no predicted energy savings for this fuel type.
FROM
"ny-gov/residential-existing-homes-one-to-four-units-jtrr-tvq4:latest"."residential_existing_homes_one_to_four_units"
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-one-to-four-units-jtrr-tvq4 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-one-to-four-units-jtrr-tvq4: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-one-to-four-units-jtrr-tvq4Checkout 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-one-to-four-units-jtrr-tvq4:latestThis will download all the objects for the latest tag of ny-gov/residential-existing-homes-one-to-four-units-jtrr-tvq4 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-one-to-four-units-jtrr-tvq4: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-one-to-four-units-jtrr-tvq4: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-one-to-four-units-jtrr-tvq4 is just another Postgres schema.