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 food_service_inspection_violations table in this repository, by referencing it like:
"fultoncountyga-gov/food-service-inspection-violations-56iw-x8kz:latest"."food_service_inspection_violations"or in a full query, like:
SELECT
":id", -- Socrata column ID
"date", -- The date on which the inspection was conducted.
"permit_number", -- An alphanumeric sequence of characters used to uniquely identify each permit required to operate a food service establishment.
"last_date", -- The date of the inspection immediately preceding the inspection subject of the record.
"foodborne_illness_risk", -- Indicates whether the violation is associated with a risk of foodborne illness.
"grade", -- A letter grade based on the inspection score (A=90-100, B=80-89, C=70-79, U=0-69).
"risk_type", -- A number (1, 2 or 3) indicating the risk of food-borne illness based on the menu items served; the food preparation process performed, and the previous food safety history of the food service establishment. The Risk Type determines the frequency at which the establishment must be inspected. Risk Type 3 represents the highest risk and requires the highest frequency of inspection. Detailed information on risk types can be found at https://dph.georgia.gov/sites/dph.georgia.gov/files/EnvHealth/Food/InterpretationManual/10InterpretationManualInspectionsComplainceProcedures.pdf.
"last_score", -- The overall score (0-100) resulting from the inspection immediately preceding the inspection subject of the record.
"last_grade", -- A letter grade based on the inspection immediately preceding the inspection subject of the record.
"prior_grade", -- A letter grade based on the score from the inspection prior to the immediately preceding the inspection subject of the record.
"follow_up_needed", -- Indicates whether a followup inspection is required.
"state", -- The state component of the address of the food service establishment.
"score", -- The overall score (0-100) resulting from the inspection
"purpose", -- The purpose of the inspection (Initial, Routine, FollowUp, Temporary)
"type", -- A value of COS (Corrected On-Site) indicates that the violation was corrected and verified before the completion of the inspection. A value of R (Repeat) indicates that the same violation of the code provision was cited on the previous routine inspection report.
"date_time_in", -- The date and time of the start of the inspection.
"date_time_out", -- The date and time of the end of the inspection.
"inspection_id", -- A unique numeric identifier for the inspection. This is not part of the source inspection data but rather is a number generated in the process of extracting the data from the source. The Inspection ID will be duplicated for each violation in the dataset associated with the same inspection.
"follow_up_date", -- The scheduled date of a followup inspection if needed.
"prior_date", -- The date of the inspection prior to the inspection immediately preceding the inspection subject of the record.
"zipcode", -- The zip code component of the address of the food service establishment.
"item", -- A code and abbreviated description of the rule violated as it appears on the inspection form. The violation can be cross-referenced with the associated food service rule in the Georgia Department of Public Health Food Service Rules and Regulations using the Instructions for Marking the Georgia Food Establishment Inspection Report Form at https://dph.georgia.gov/sites/dph.georgia.gov/files/EnvHealth/Food/Misc/EnvHealthFoodInstructionsMarkingInspection2016.pdf.
"facility", -- The name of the food service establishment.
"address", -- The street number and street name component of the address of the food service establishment.
"city", -- The city component of the address of the food service establishment.
"prior_score" -- The overall score (0-100) resulting from the inspection prior to the immediately preceding the inspection subject of the record.
FROM
"fultoncountyga-gov/food-service-inspection-violations-56iw-x8kz:latest"."food_service_inspection_violations"
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 fultoncountyga-gov/food-service-inspection-violations-56iw-x8kz 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 fultoncountyga-gov/food-service-inspection-violations-56iw-x8kz: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 fultoncountyga-gov/food-service-inspection-violations-56iw-x8kzCheckout 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 fultoncountyga-gov/food-service-inspection-violations-56iw-x8kz:latestThis will download all the objects for the latest tag of fultoncountyga-gov/food-service-inspection-violations-56iw-x8kz 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 fultoncountyga-gov/food-service-inspection-violations-56iw-x8kz: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 fultoncountyga-gov/food-service-inspection-violations-56iw-x8kz: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, fultoncountyga-gov/food-service-inspection-violations-56iw-x8kz is just another Postgres schema.