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 parks_locations_and_amenities table in this repository, by referencing it like:
"citydata-mesaaz-gov/parks-locations-and-amenities-djym-pkpp:latest"."parks_locations_and_amenities"or in a full query, like:
SELECT
":id", -- Socrata column ID
"street_number", -- The street number of the park or basin address.
"facility_id", -- Unique identifier for each park or basin. Parks are indicated by PKPK followed by a number, Basins are indicated by PKBN followed be a number.
"longitude", -- The longitude for the park or basin
"city", -- City where the park or basin is located
"location", -- Geo coded location of the park or basin
"type", -- Is the facility a park or basin. Basins are undeveloped green spaces managed by the city. Typically used for community water drainage and/or retention basins.
"exercise_course", -- Does this park have a designated exercise course amenity
"raquetball", -- Does this park have racquetball courts and if so, are they lighted.
"bleachers", -- Are there bleacher amenities at this park
"baseball", -- Does the park have baseball fields, and if so how many, what type and are they lighted
"pickelball", -- Does this park have pickelball courts and if so, are they lighted.
"park_site_map", -- Link to additional information about the park, may include a site map
"trails", -- Does the park have walking or hiking trails
"picnic_tables", -- Does the park have picnic tables, and if so are they shaded
"barbecue_grills", -- Does the park have barbecue grills
"horsehoes", -- Does the park have horseshoe pits
"ramada", -- Does the park have a ramada, and if so is can it be reserved. Is it shaded.
"basketball", -- Does the Park have basketball courts and if so, how many and are they lighted.
"street_type", -- The street type of the park or basin address.
"street_name", -- The street name of the park or basin address.
"full_address", -- The street address for the park or basin
"disc_golf", -- Does this park have disc golf amenities
"soccer", -- Does the park have soccer fields, and if so how many and are they lighted
"volleyball", -- Does the park have volleyball courts, and if so how many, what type and are they lighted.
"name", -- Name of Park or Basin
"tennis", -- Does this park have tennis courts and if so, are they lighted.
"street_direction", -- The street direction of the park or basin address.
"dog_area", -- Does this park have a designated dog area
"restroom", -- Does the park have restroom facilities
"playground", -- Does the park have playground amenities, and if so are they shaded.
"splashpad", -- Does the park have a splash pad amenity, and if so is it shaded.
"location_address",
"latitude", -- The latitude for the park or basin
"location_state",
":@computed_region_fcpr_wj2n",
"location_zip",
"location_city",
"number_of_acres", -- Estimated number of acres
"lake" -- Does the park have a lake amenity
FROM
"citydata-mesaaz-gov/parks-locations-and-amenities-djym-pkpp:latest"."parks_locations_and_amenities"
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 citydata-mesaaz-gov/parks-locations-and-amenities-djym-pkpp 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 citydata-mesaaz-gov/parks-locations-and-amenities-djym-pkpp: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 citydata-mesaaz-gov/parks-locations-and-amenities-djym-pkppCheckout 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 citydata-mesaaz-gov/parks-locations-and-amenities-djym-pkpp:latestThis will download all the objects for the latest tag of citydata-mesaaz-gov/parks-locations-and-amenities-djym-pkpp 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 citydata-mesaaz-gov/parks-locations-and-amenities-djym-pkpp: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 citydata-mesaaz-gov/parks-locations-and-amenities-djym-pkpp: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, citydata-mesaaz-gov/parks-locations-and-amenities-djym-pkpp is just another Postgres schema.