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 dohmh_new_york_city_restaurant_inspection_results table in this repository, by referencing it like:
"cityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8j:latest"."dohmh_new_york_city_restaurant_inspection_results"or in a full query, like:
SELECT
":id", -- Socrata column ID
":@computed_region_f5dn_yrer", -- This column was automatically created in order to record in what polygon from the dataset 'Community Districts' (f5dn-yrer) the point in column 'location_point1' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"dba", -- This field represents the name (doing business as) of the entity (restaurant); Public business name, may change at discretion of restaurant owner
"community_board",
"location_point1",
"bbl",
"inspection_type", -- A combination of the inspection program and the type of inspection performed; See Data Dictionary for full list of expected values
"grade", -- Grade associated with the inspection; • N = Not Yet Graded• A = Grade A• B = Grade B• C = Grade C• Z = Grade Pending• P= Grade Pending issued on re-opening following an initial inspection that resulted in a closure
"critical_flag", -- Indicator of critical violation; "• Critical • Not Critical • Not Applicable"; Critical violations are those most likely to contribute to food-borne illness
"violation_description", -- Violation description associated with an establishment (restaurant) inspection
"boro", -- Borough in which the entity (restaurant) is located.;• 1 = MANHATTAN • 2 = BRONX • 3 = BROOKLYN • 4 = QUEENS • 5 = STATEN ISLAND • Missing; NOTE: There may be discrepancies between zip code and listed boro due to differences in an establishment's mailing address and physical location
":@computed_region_sbqj_enih", -- This column was automatically created in order to record in what polygon from the dataset 'Police Precincts' (sbqj-enih) the point in column 'location_point1' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_92fq_4b7q", -- This column was automatically created in order to record in what polygon from the dataset 'City Council Districts' (92fq-4b7q) the point in column 'location_point1' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_yeji_bk3q", -- This column was automatically created in order to record in what polygon from the dataset 'Borough Boundaries' (yeji-bk3q) the point in column 'location_point1' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_efsh_h5xi", -- This column was automatically created in order to record in what polygon from the dataset 'Zip Codes' (efsh-h5xi) the point in column 'location_point1' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"street", -- Street name for establishment (restaurant) location
"camis", -- This is an unique identifier for the entity (restaurant); 10-digit integer, static per restaurant permit
"census_tract",
"bin",
"council_district",
"record_date", -- The date when the extract was run to produce this data set
"action", -- This field represents the actions that is associated with each restaurant inspection. ; • Violations were cited in the following area(s). • No violations were recorded at the time of this inspection. • Establishment re-opened by DOHMH • Establishment re-closed by DOHMH • Establishment Closed by DOHMH. Violations were cited in the following area(s) and those requiring immediate action were addressed. • "Missing" = not yet inspected;
"inspection_date", -- This field represents the date of inspection; NOTE: Inspection dates of 1/1/1900 mean an establishment has not yet had an inspection
"cuisine_description", -- This field describes the entity (restaurant) cuisine. ; Optional field provided by provided by restaurant owner/manager
"longitude",
"nta",
"zipcode", -- Zip code of establishment (restaurant) location
"building", -- Building number for establishment (restaurant) location
"score", -- Total score for a particular inspection; Scores are updated based on adjudication results
"latitude",
"grade_date", -- The date when the current grade was issued to the entity (restaurant)
"violation_code", -- Violation code associated with an establishment (restaurant) inspection
"phone" -- Phone Number; Phone number provided by restaurant owner/manager
FROM
"cityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8j:latest"."dohmh_new_york_city_restaurant_inspection_results"
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 cityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8j 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 cityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8j: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 cityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8jCheckout 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 cityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8j:latestThis will download all the objects for the latest tag of cityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8j 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 cityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8j: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 cityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8j: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, cityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8j is just another Postgres schema.