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
"score", -- Total score for a particular inspection; Scores are updated based on adjudication results
"record_date", -- The date when the extract was run to produce this data set
"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
"violation_description", -- Violation description associated with an establishment (restaurant) inspection
"critical_flag", -- Indicator of critical violation; "• Critical • Not Critical • Not Applicable"; Critical violations are those most likely to contribute to food-borne illness
"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
"inspection_type", -- A combination of the inspection program and the type of inspection performed; See Data Dictionary for full list of expected values
"bbl",
"location_point1",
"community_board",
"dba", -- This field represents the name (doing business as) of the entity (restaurant); Public business name, may change at discretion of restaurant owner
":@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.
"bin",
"zipcode", -- Zip code of establishment (restaurant) location
"cuisine_description", -- This field describes the entity (restaurant) cuisine. ; Optional field provided by provided by restaurant owner/manager
"latitude",
"street", -- Street name for establishment (restaurant) location
"violation_code", -- Violation code associated with an establishment (restaurant) inspection
"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
"census_tract",
"building", -- Building number for establishment (restaurant) location
"longitude",
"council_district",
"nta",
"camis", -- This is an unique identifier for the entity (restaurant); 10-digit integer, static per restaurant permit
"phone", -- Phone Number; Phone number provided by restaurant owner/manager
"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;
"grade_date", -- The date when the current grade was issued to the entity (restaurant)
":@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.
":@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.
":@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_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.
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.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.cityofnewyork.us. When you querycityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8j:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).
We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.
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 (like this repository), 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, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Mounting Data
This repository is an external repository. It's not hosted by Splitgraph. It is hosted by data.cityofnewyork.us, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.
First, install Splitgraph if you haven't already.
Mount the table with sgr mount
sgr mount socrata \
"cityofnewyork-us/dohmh-new-york-city-restaurant-inspection-results-43nn-pn8j" \
--handler-options '{
"domain": "data.cityofnewyork.us",
"tables": {
"dohmh_new_york_city_restaurant_inspection_results": "43nn-pn8j"
}
}'That's it! Now you can query the data in the mounted table like any other Postgres table.
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.