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 delaware_air_emissions_inventory table in this repository, by referencing it like:
"delaware-gov/delaware-air-emissions-inventory-r3u2-4uk7:latest"."delaware_air_emissions_inventory"or in a full query, like:
SELECT
":id", -- Socrata column ID
"lead_2018_lbs", -- Pounds of lead emitted by the facility in 2018
"co_2018_tons", -- Tons of carbon monoxide emitted by the facility in 2018
"voc_2017_tons", -- Tons of volatile organic compounds emitted by the facility in 2017
"so2_2017_tons", -- Tons of sulfur dioxide emitted by the facility in 2017
"pm2_5_2017_tons", -- Tons of fine particulate matter emitted by the facility in 2017
"pm10_2017_tons", -- Tons of coarse particulate matter emitted by the facility in 2017
"nox_2017_tons", -- Tons of nitrogen oxides emitted by the facility in 2017
"nh3_2017_tons", -- Tons of ammonia emitted by the facility in 2017
"lead_2017_lbs", -- Pounds of lead emitted by the facility in 2017
"co_2017_tons", -- Tons of carbon monoxide emitted by the facility in 2017
"voc_2016_tons", -- Tons of volitile organic compounds emitted by the facility in 2016
"so2_2016_tons", -- Tons of sulfur dioxide emitted by the facility in 2016
"pm2_5_2016_tons", -- Tons of fine particulate matter emitted by the facility in 2016
"voc_2021_tons", -- Tons of volatile organic compounds emitted by the facility in 2021
"lead_2021_lbs", -- Pounds of lead emitted by the facility in 2021
"co_2021_tons", -- Tons of carbon monoxide emitted by the facility in 2021
"voc_2020_tons", -- Tons of volatile organic compounds emitted by the facility in 2020
"pm10_2020_tons", -- Tons of coarse particulate matter emitted by the facility in 2020
"nh3_2020_tons", -- Tons of ammonia emitted by the facility in 2020
"lead_2020_lbs", -- Pounds of lead emitted by the facility in 2020
"address", -- Street address of facility location
"facility_description", -- Description of facility
"facility_name", -- Facility name
"facility_id", -- Unique identifier for the facility
"pm2_5_2019_tons", -- Tons of fine particulate matter emitted by the facility in 2019
"so2_2021_tons", -- Tons of sulfur dioxide emitted by the facility in 2021
"pm2_5_2021_tons", -- Tons of fine particulate matter emitted by the facility in 2021
"pm10_2021_tons", -- Tons of coarse particulate matter emitted by the facility in 2021
"nox_2021_tons", -- Tons of nitrogen oxides emitted by the facility in 2021
"co_2020_tons", -- Tons of carbon monoxide emitted by the facility in 2020
"voc_2019_tons", -- Tons of volatile organic compounds emitted by the facility in 2019
"so2_2019_tons", -- Tons of sulfur dioxide emitted by the facility in 2019
"pm10_2019_tons", -- Tons of coarse particulate matter emitted by the facility in 2019
"nox_2019_tons", -- Tons of nitrogen oxides emitted by the facility in 2019
"nh3_2021_tons", -- Tons of ammonia emitted by the facility in 2021
"notes", -- Additional information relating to the facility and its emissions
"nh3_2019_tons", -- Tons of ammonia emitted by the facility in 2019
"lead_2019_lbs", -- Pounds of lead emitted by the facility in 2019
"co_2019_tons", -- Tons of carbon monoxide emitted by the facility in 2019
"voc_2018_tons", -- Tons of volatile organic compounds emitted by the facility in 2018
"so2_2018_tons", -- Tons of sulfur dioxide emitted by the facility in 2018
"pm2_5_2018_tons", -- Tons of fine particulate matter emitted by the facility in 2018
"pm10_2018_tons", -- Tons of coarse particulate matter emitted by the facility in 2018
"nox_2018_tons", -- Tons of nitrogen oxides emitted by the facility in 2018
"nh3_2018_tons", -- Tons of ammonia emitted by the facility in 2018
"so2_2020_tons", -- Tons of sulfur dioxide emitted by the facility in 2020
"pm2_5_2020_tons", -- Tons of fine particulate matter emitted by the facility in 2020
"pm10_2016_tons", -- Tons of coarse particulate matter emitted by the facility in 2016
"nox_2016_tons", -- Tons of nitrogen oxides emitted by the facility in 2016
"nh3_2016_tons", -- Tons of ammonia emitted by the facility in 2016
"lead_2016_lbs", -- Pounds of lead emitted by the facility in 2016
"co_2016_tons", -- Tons of carbon monoxide emitted by the facility in 2016
"zip", -- Zipcode in which the facility is located
"city", -- City in which the facility is located
"nox_2020_tons" -- Tons of nitrogen oxides emitted by the facility in 2020
FROM
"delaware-gov/delaware-air-emissions-inventory-r3u2-4uk7:latest"."delaware_air_emissions_inventory"
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 delaware-gov/delaware-air-emissions-inventory-r3u2-4uk7 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 delaware-gov/delaware-air-emissions-inventory-r3u2-4uk7: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 delaware-gov/delaware-air-emissions-inventory-r3u2-4uk7Checkout 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 delaware-gov/delaware-air-emissions-inventory-r3u2-4uk7:latestThis will download all the objects for the latest tag of delaware-gov/delaware-air-emissions-inventory-r3u2-4uk7 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 delaware-gov/delaware-air-emissions-inventory-r3u2-4uk7: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 delaware-gov/delaware-air-emissions-inventory-r3u2-4uk7: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, delaware-gov/delaware-air-emissions-inventory-r3u2-4uk7 is just another Postgres schema.