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 civic_census_by_ward_and_dwelling_structure table in this repository, by referencing it like:
"calgary-ca/civic-census-by-ward-and-dwelling-structure-yr3w-mcsu:latest"."civic_census_by_ward_and_dwelling_structure"or in a full query, like:
SELECT
":id", -- Socrata column ID
"ocpd_ownership_cnt", -- Count of dwellings occupied by home owners
"vacant_dwelling_cnt", -- Count of dwelling units uninhabited at the time of the census, but suitable and available for occupancy
"ocpd_dwelling_cnt", -- Count of dwellings with confirmed residents
"resident_cnt", -- Count of residents. A person who maintains residence in the city is considered a resident. Residents include newly arrived persons, persons absent for educational purposes, persons temporarily at remote work sites, etc.
"other_purpose_cnt", -- Count of structures or units originally built as a dwelling and which could be used as a dwelling again, but which is presently used for a non-residential purpose (e.g. show home)
"renovation_dwelling_cnt", -- Count of dwelling units vacant because of renovations
"inactive_cnt", -- Count of inactive dwelling units. A dwelling unit that is part of a multi-dwelling unit structure and is being used as part of another dwelling unit in the same physical structure (e.g. both halves of a duplex used by one family); or a vacant pad in a mobile home park; or a converted structure unit like a basement suite, which is not occupied as a dwelling unit, has not been removed, is not being renovated but is not available for occupancy; or a dwelling unit that has been boarded-up, scheduled for demolition, fenced or taped off, condemned, or otherwise uninhabitable.
"under_const_dwelling_cnt", -- Count of dwelling units under construction. A dwelling unit is considered under construction as as soon as the foundation is poured, and until it is ready for occupancy.
"dwelling_cnt", -- Count of dwellings
"dwelling_type_description", -- Description of the dwelling type.
"dwelling_type", -- Refers to the physical building or structure in which the dwelling unit is located. It is not a reference to the use or ownership of the dwelling.
"ward", -- Municipal electoral ward
"census_year", -- Year the data was collected
"dwelling_type_code" -- Numerical code of the dwelling type.
FROM
"calgary-ca/civic-census-by-ward-and-dwelling-structure-yr3w-mcsu:latest"."civic_census_by_ward_and_dwelling_structure"
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 calgary-ca/civic-census-by-ward-and-dwelling-structure-yr3w-mcsu 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 calgary-ca/civic-census-by-ward-and-dwelling-structure-yr3w-mcsu: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 calgary-ca/civic-census-by-ward-and-dwelling-structure-yr3w-mcsuCheckout 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 calgary-ca/civic-census-by-ward-and-dwelling-structure-yr3w-mcsu:latestThis will download all the objects for the latest tag of calgary-ca/civic-census-by-ward-and-dwelling-structure-yr3w-mcsu 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 calgary-ca/civic-census-by-ward-and-dwelling-structure-yr3w-mcsu: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 calgary-ca/civic-census-by-ward-and-dwelling-structure-yr3w-mcsu: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, calgary-ca/civic-census-by-ward-and-dwelling-structure-yr3w-mcsu is just another Postgres schema.