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 brec_park_amenities table in this repository, by referencing it like:
"brla-gov/brec-park-amenities-phg8-g77c:latest"."brec_park_amenities"or in a full query, like:
SELECT
":id", -- Socrata column ID
"multi_purpose_ball_field", -- Outdoor field for many purposes
"mountain_bike_trail", -- Outdoor mountain bike trails
"airnasium", -- Covered multi-purpose court with open sides
"airgun_range", -- Outdoor airgun ranges
"tennis_court", -- Tennis courts
"fitness_center", -- Facilities with fitness equipment and programs
"bird_watching_wildlife_viewing", -- Locations for watching birds and viewing wildlife
"stadium", -- Olympia Field, Memorial Stadium, Goldsby Stadium
"rugby_field", -- Outdoor rugby fields
"dog_park", -- Outdoor dog parks
"blueway_trailhead", -- Start or end point for canoeing and kayaking designated streams
":@computed_region_4pgc_bhg2", -- This column was automatically created in order to record in what polygon from the dataset 'Census 2010 Tracts' (4pgc-bhg2) the point in column 'geolocation' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_ntzg_c2w3", -- This column was automatically created in order to record in what polygon from the dataset 'Council Districts_2021_from_d8sa-f3ec' (ntzg-c2w3) the point in column 'geolocation' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_jrqt_zu77", -- This column was automatically created in order to record in what polygon from the dataset 'ZIP Codes_from_tqy7_429i' (jrqt-zu77) the point in column 'geolocation' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_uvg4_nwq8", -- This column was automatically created in order to record in what polygon from the dataset 'Neighborhoods_from_qfmj_2fwi' (uvg4-nwq8) the point in column 'geolocation' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"sand_volleyball", -- Facilities for playing sand volleyball
"rv_park", -- Park with facilities for motor homes
"golf_course_18_hole", -- 18-Hole golf courses
"disc_golf", -- 18-Hole outdoor disc golf courses
"canoe_kayak_launch", -- Point of entry and exit for launching canoes and kayaks
"indoor_basketball", -- Basketball courts in gymnasiums
"archery_range", -- Outdoor archery ranges
"acreage", -- Total area of the park facility in acres
"full_address", -- Street address of the park or facility
"outdoor_basketball", -- Outdoor basketball courts including half courts or full courts
"website", -- URL to the web page for more information about the park
"geolocation", -- Coordinates of the park for mapping purposes
"swimming_pool", -- Outdoor swimming pools
"city", -- Postal city name for the address
"golf_course_9_hole", -- 9-Hole golf courses
"zip", -- Postal ZIP code for the address
"bmx_track", -- Outdoor BMX tracks
"park_name", -- Name of park or facility
"walking_track_indoor", -- Indoor walking paths
"walking_loop_outdoor", -- Outdoor paved walking path loops
"spray_pad", -- Outdoor water splash pads
"playgound", -- Playgrounds and play features included in that particular playground
"fitness_station_outdoor", -- Park facility with outdoor fitness stations for exercising
"cricket_pitch", -- Facility for playing cricket
"parkid", -- Unique identification code for each park
"velodrome", -- Outdoor cycling velodromes
"skate_park", -- Outdoor skate parks
"garden_aboretum", -- Park with gardens and/or an arboretum
"fishing_access", -- Access to outdoor fishing
"tennis_center", -- Complex of tennis courts
"shuffleboard", -- Facilities for playing shuffleboard
"pavilion", -- Covered outdoor facility for picnics and gatherings
"greenway_trailhead", -- Start or end point for walking a greenway trail
"soccer_field", -- Outdoor soccer fields
"rec_center", -- Recreation Centers provide facilities for indoor programs; some include gymnasiums
"primitive_trail", -- Outdoor long distance hiking or walking trails
"nature_trail", -- Outdoor short distance walking trail through a natural area
"croquet_court", -- Outdoor croquet courts
"bike_repair_station", -- Bicycle repair station featuring tools for cyclists to keep their bikes rolling
"classification", -- Classification of park
"state", -- The state in which the park is located.
"equestrian" -- Equestrian facilities including courses, stables, indoor horse events, pastures, horse trails
FROM
"brla-gov/brec-park-amenities-phg8-g77c:latest"."brec_park_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 brla-gov/brec-park-amenities-phg8-g77c 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 brla-gov/brec-park-amenities-phg8-g77c: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 brla-gov/brec-park-amenities-phg8-g77cCheckout 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 brla-gov/brec-park-amenities-phg8-g77c:latestThis will download all the objects for the latest tag of brla-gov/brec-park-amenities-phg8-g77c 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 brla-gov/brec-park-amenities-phg8-g77c: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 brla-gov/brec-park-amenities-phg8-g77c: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, brla-gov/brec-park-amenities-phg8-g77c is just another Postgres schema.