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 radon_mitigation_system_year_1987_current_annual table in this repository, by referencing it like:
"pa-gov/radon-mitigation-system-year-1987-current-annual-vm4e-6nir:latest"."radon_mitigation_system_year_1987_current_annual"or in a full query, like:
SELECT
":id", -- Socrata column ID
"remediationsystemdesc", -- Description of remediation type.
"latitude", -- Latitude in degrees for center of the county in which the mitigation occurred.
"teststopdate", -- End Date of the Radon Test.
"testmethodtypecode", -- Test Method Type Code. "1 = Pre (before mitigation) 2 = Post (after mitigation) NC = Otherwise".
"municipalityname", -- The name of the municipality.
"longitude", -- Longitude in degrees for center of the county in which the mitigation occurred.
":@computed_region_nmsq_hqvv", -- This column was automatically created in order to record in what polygon from the dataset 'Pennsylvania County Boundaries' (nmsq-hqvv) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_d3gw_znnf", -- This column was automatically created in order to record in what polygon from the dataset 'Pa Senatorial Districts (2020-01)' (d3gw-znnf) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"dataidentifier", -- A unique identfier for each Radon Mitigation.
":@computed_region_r6rf_p9et", -- This column was automatically created in order to record in what polygon from the dataset 'Pa House Districts (2020-01)' (r6rf-p9et) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"floorleveltested", -- The floor level in the building where the Radon test was conducted. "Basement, Slab on Grade, First Floor, Second Floor, Third Floor, Above Third Floor, Crawl Space, Unknown".
"mitigationcounty", -- A name used to identify a primary geopolitical unit of the world.
"countycode", -- The Pennsylvania code that represents the county.
":@computed_region_amqz_jbr4", -- This column was automatically created in order to record in what polygon from the dataset 'Municipality Boundary' (amqz-jbr4) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_rayf_jjgk", -- This column was automatically created in order to record in what polygon from the dataset 'Pa School Districts (2019-06)' (rayf-jjgk) the point in column 'georeferenced_latitude_longitude' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"buildingtypecode", -- The designator that represents the Type of the building purpose. "2-STORY, 3-STORY, 3-STORY, RANCH, SPLIT LEVEL, BI-LEVEL, TRAILER, APARTMENT, TOWNHOUSE, CAPE COD, CONTEMPORARY, RAISED RANCH, COMMERCIAL BUILDING SCHOOL, PUBLIC BUILDING, UNKNOWN".
"datasourcename", -- Radon Result List. Pennsylvania Department of Environmental Protection.
"testresultidentifier", -- The unique identifier assigned for each Radon Miti Result.
"mitiyear", -- Year Mitigation is done (ended)
"mitigationcomments", -- Mitigation Comments
"georeferenced_latitude_longitude",
"measurevalue", -- The recorded dimension, capacity, quality, or amount of something ascertained by measuring or observing. All values are pCi/L, or picocuries per liter.
"buildingpurposecode", -- The designator that represents the building purpose. "Residential Non Residential School Unknown".
"countyfipscode", -- FIPS Counties codes used for the identification of the Counties and County equivalents of the United States.
"mitigationtesttype", -- Test done before or after mitigation. "Pre (before mitigation) Post (after mitigation)".
"devicetypename", -- Device used for testing. "ALPHA-TRCK-TEST, CW-PRI-TEST, LS-TEST, CHARCOAL TEST, WATER TEST, DROP DEVICE-AC, CR-SEC-TEST, E-PERM SEC-TEST, SOIL TEST, E-PERM PRI-TEST, GRAB RADON-TEST, CR-PRI-TEST, RPISU, CW-SEC-TEST".
"systemcost", -- Dollar Amount for the cost of the system.
"addresspostalcode" -- The combination of the 5-digit Zone Improvement Plan (ZIP) code and the four-digit extension code (if available) that represents the geographic segment that is a subunit of the ZIP Code, assigned by the U.S. Postal Service to a geographic location to facilitate mail delivery; or the postal zone specific to the country, other than the U.S., where the mail is delivered.
FROM
"pa-gov/radon-mitigation-system-year-1987-current-annual-vm4e-6nir:latest"."radon_mitigation_system_year_1987_current_annual"
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 pa-gov/radon-mitigation-system-year-1987-current-annual-vm4e-6nir 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 pa-gov/radon-mitigation-system-year-1987-current-annual-vm4e-6nir: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 pa-gov/radon-mitigation-system-year-1987-current-annual-vm4e-6nirCheckout 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 pa-gov/radon-mitigation-system-year-1987-current-annual-vm4e-6nir:latestThis will download all the objects for the latest tag of pa-gov/radon-mitigation-system-year-1987-current-annual-vm4e-6nir 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 pa-gov/radon-mitigation-system-year-1987-current-annual-vm4e-6nir: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 pa-gov/radon-mitigation-system-year-1987-current-annual-vm4e-6nir: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, pa-gov/radon-mitigation-system-year-1987-current-annual-vm4e-6nir is just another Postgres schema.