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 healthy_neighborhoods_program_housing_demographics table in this repository, by referencing it like:
"health-data-ny-gov/healthy-neighborhoods-program-housing-demographics-jbwf-vnai:latest"."healthy_neighborhoods_program_housing_demographics"or in a full query, like:
SELECT
":id", -- Socrata column ID
"location", -- The latitude and longitude of the county centroid.
"date_closed",
"percent",
"variable_detail", -- Variable detail are the responses possible for each variables. Housing conditions details are Yes, No, Unknown, and Not Applicable. The details for each building characteristic or respondent demographics depends on the variable. For example, the details for Age of Building are Pre-1950, 1950-1978, and Post-1978.
"variable", -- Variables are the individual building characteristics, respondent demographics, or housing conditions assessed at the initial visit or revisit. Information about the building, primary respondent and residents are only collected at the initial visit.
"location_address",
":@computed_region_43an_4dx5",
":@computed_region_5edz_4hdv",
":@computed_region_9yqb_tdyd",
":@computed_region_8ire_itmf",
":@computed_region_assa_msit",
"location_city",
"location_zip",
"location_state",
"frequency_count", -- This is the number of dwellings that have a specific detail (e.g., the number of dwellings built before 1950, 1950-1978, after 1978, and Unknown). These sum to the number of dwellings visited.
"visit_type", -- Types are Initial visits and Revisits. At the initial visit an HNP surveyor gathers information about the building, primary respondent and residents and conducts an assessment of the conditions of the home. At this visit, the surveyor provides the HNP interventions and guidance to assist residents in addressing potential health and safety hazards. About 25% of these homes are revisited 3-6 months after the initial visit. Homes needing a revisit are prioritized on the overall or severity of the conditions identified at the initial visit or homes with residents with asthma. The home is reassessed for ongoing or new conditions and additional interventions are provided as needed.
"funding_cycle", -- County health departments submit applications to compete for funding to implement the Healthy Neighborhoods Program. Only full-service health departments are eligible to apply for funding. (A full-service health department has a Division of Nursing and a Division of Environmental Health.) Funding runs on cycles of 4 or 5 years. Both federal and state monies have funded the program; the current cycle is funded by the state budget.
"county_name", -- The name of the county with a Healthy Neighborhoods Program (HNP). “All counties” is the combined data for all counties funded during a funding cycle.
"item" -- Variables are grouped into item types: housing information, respondent demographics, and housing conditions.
FROM
"health-data-ny-gov/healthy-neighborhoods-program-housing-demographics-jbwf-vnai:latest"."healthy_neighborhoods_program_housing_demographics"
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 health-data-ny-gov/healthy-neighborhoods-program-housing-demographics-jbwf-vnai 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 health-data-ny-gov/healthy-neighborhoods-program-housing-demographics-jbwf-vnai: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 health-data-ny-gov/healthy-neighborhoods-program-housing-demographics-jbwf-vnaiCheckout 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 health-data-ny-gov/healthy-neighborhoods-program-housing-demographics-jbwf-vnai:latestThis will download all the objects for the latest tag of health-data-ny-gov/healthy-neighborhoods-program-housing-demographics-jbwf-vnai 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 health-data-ny-gov/healthy-neighborhoods-program-housing-demographics-jbwf-vnai: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 health-data-ny-gov/healthy-neighborhoods-program-housing-demographics-jbwf-vnai: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, health-data-ny-gov/healthy-neighborhoods-program-housing-demographics-jbwf-vnai is just another Postgres schema.