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_violation_comments table in this repository, by referencing it like:
"fultoncountyga-gov/food-service-inspection-violation-comments-eyfj-j5ac:latest"."food_service_inspection_violation_comments"or in a full query, like:
SELECT
":id", -- Socrata column ID
"grade", -- A letter grade based on the inspection score (A=90-100, B=80-89, C=70-79, U=0-69).
"zipcode", -- The zip code component of the address of the food service establishment.
"observations",
"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.
"state", -- The state component of the address of the food service establishment.
"last_date", -- The date of the inspection immediately preceding the inspection subject of the record.
"prior_date", -- The date of the inspection prior to the inspection immediately preceding the inspection subject of the record.
"follow_up_date", -- The scheduled date of a followup inspection if needed.
"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.
"last_grade", -- A letter grade based on the inspection immediately preceding the inspection subject of the record.
"facility", -- The name 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)
"prior_grade", -- A letter grade based on the score from the inspection prior to the immediately preceding the inspection subject of the record.
"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.
"follow_up_needed", -- Indicates whether a followup inspection is required.
"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.
"permit_number", -- An alphanumeric sequence of characters used to uniquely identify each permit required to operate a food service establishment.
"item_number",
"last_score", -- The overall score (0-100) resulting from the inspection immediately preceding the inspection subject of the record.
"prior_score", -- The overall score (0-100) resulting from the inspection prior to the immediately preceding the inspection subject of the record.
"date" -- The date on which the inspection was conducted.
FROM
"fultoncountyga-gov/food-service-inspection-violation-comments-eyfj-j5ac:latest"."food_service_inspection_violation_comments"
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-violation-comments-eyfj-j5ac with SQL in under 60 seconds.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.fultoncountyga.gov. When you queryfultoncountyga-gov/food-service-inspection-violation-comments-eyfj-j5ac: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.fultoncountyga.gov, 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 \
"fultoncountyga-gov/food-service-inspection-violation-comments-eyfj-j5ac" \
--handler-options '{
"domain": "data.fultoncountyga.gov",
"tables": {
"food_service_inspection_violation_comments": "eyfj-j5ac"
}
}'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, fultoncountyga-gov/food-service-inspection-violation-comments-eyfj-j5ac is just another Postgres schema.