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 school_nutrition_programs_meal_reimbursement table in this repository, by referencing it like:
"texas-gov/school-nutrition-programs-meal-reimbursement-uuki-47ik:latest"."school_nutrition_programs_meal_reimbursement"or in a full query, like:
SELECT
":id", -- Socrata column ID
"snackreimbursement", -- Total snack meal reimbursement for claim month
"lunchreimbursement", -- Total lunch meal reimbursement for claim month
"breakfastreimbursement", -- Total breakfast meal reimbursement for claim month
"milkservedpaid", -- Total number of Special Milk Program (SMP) paid milk portions served at site for claim month
"milkadp", -- Average Daily Participation (ADP) for milk. Calculated as the number of half pint milks served at the site in claim month divided by the number of milk service days at the site in claim month.
"milktotal", -- Total number of Special Milk Program (SMP) milk portions served at site for claim month
"milkdays", -- Number of days milk was served as part of the Special Milk Program (SMP) at site for claim month
"snacksservedpaid", -- Total number of paid snack meals served at site for claim month
"snacksservedredc", -- Total number of reduced price snack meals served at site for claim month
"snackadp", -- Average Daily Participation (ADP) for snacks. Calculated as the number of snacks served at site in claim month divided by the number of snack service days at site in claim month.
"snacktotal", -- Total number of snack meals served at site for claim month
"snackdays", -- Number of days snack meals were served at site for claim month
"lunchservedpaid", -- Total number of paid lunch meals served at site for claim month
"lunchservedredc", -- Total number of reduced price lunch meals served at site for claim month
"lunchservedfree", -- Total number of free lunch meals served at site for claim month
"lunchadp", -- Average Daily Participation (ADP) for lunch. Calculated as the number of lunch meals served at site in claim month divided by the number of lunch service days at site in claim month.
"lunchtotal", -- Total number of lunch meals served at site for claim month
"lunchdays", -- Number of days lunch was served at site for claim month
"breakfastservedpaid", -- Total number of paid breakfast meals served at site for claim month
"breakfastservedredc", -- Total number of reduced price breakfast meals served at site for claim month
"breakfastservedfree", -- Total number of free breakfast meals served at site for claim month
"breakfastadp", -- Average Daily Participation (ADP) for breakfast. Calculated as the number of breakfast meals served at site in claim month divided by the number of breakfast service days at site in claim month.
"breakfasttotal", -- Total number of breakfast meals served at site for claim month
"breakfastdays", -- Number of days breakfast was served at site for claim month
"paideligqty", -- Number of children enrolled at site not approved for free or reduced price meals for claim month
"freeeligqty", -- Number of children enrolled at site approved for free meals for claim month
"enrollmentqty", -- Number of children enrolled at site for claim month
"claimdate", -- Month and year being reported for reimbursement of meals served
"sitecounty", -- County in which the site is located
"cecounty", -- County in which the Contracting Entity (CE) is located
"sitename", -- Site name
"siteid", -- Number assigned to identify site within CE
"esc", -- Educational Service Center (ESC) region
"typeofsnporg", -- Type of organization the sponsored site operates as. Data displayed as: Charter/Private/Public/RCCI (Residential Child Care Institution)
"cename", -- Contracting Entity (CE) name
"ceid", -- Unique number assigned to Contracting Entity (CE) to identify organization as a school nutrition program sponsor
"reporttype", -- Type of information being reported in the dataset
"programyear", -- A program year for school nutrition programs is defined as July 1 of one year through June 30 of the following year.
"totalmealssnacks", -- Sum of BreakfastTotal, LunchTotal, and SnackTotal for site for claim month.
"milkreimbursement", -- Total Special Milk Program (SMP) milk reimbursement for claim month
"totalreimbursement", -- Total federal meal reimbursement for claim month
"milkservedreduced", -- Total number of Special Milk Program (SMP) reduced price milk portions served at site for claim month
"milkservedfree", -- Total number of Special Milk Program (SMP) free milk portions served at site for claim month
"snacksservedfree", -- Total number of free snack meals served at site for claim month
"redceligqty", -- Number of children enrolled at site approved for reduced price meals for claim month
"countydistrictcode", -- County District Code for county in which Contracting Entity (CE) in located
"tdaregion", -- Texas Department of Agriculture (TDA) service region
"typeofagency" -- Type of agency the Contracting Entity (CE) operates as. Data displayed as: Educational Institution/Government Agency/Private Non-Profit Organization/Other
FROM
"texas-gov/school-nutrition-programs-meal-reimbursement-uuki-47ik:latest"."school_nutrition_programs_meal_reimbursement"
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 texas-gov/school-nutrition-programs-meal-reimbursement-uuki-47ik 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 texas-gov/school-nutrition-programs-meal-reimbursement-uuki-47ik: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 texas-gov/school-nutrition-programs-meal-reimbursement-uuki-47ikCheckout 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 texas-gov/school-nutrition-programs-meal-reimbursement-uuki-47ik:latestThis will download all the objects for the latest tag of texas-gov/school-nutrition-programs-meal-reimbursement-uuki-47ik 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 texas-gov/school-nutrition-programs-meal-reimbursement-uuki-47ik: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 texas-gov/school-nutrition-programs-meal-reimbursement-uuki-47ik: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, texas-gov/school-nutrition-programs-meal-reimbursement-uuki-47ik is just another Postgres schema.