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 guatemala_food_consumption_data_section_6 table in this repository, by referencing it like:
"usaid-gov/guatemala-food-consumption-data-section-6-6b89-9wbv:latest"."guatemala_food_consumption_data_section_6"or in a full query, like:
SELECT
":id", -- Socrata column ID
"asset_rental_final_13", -- Daily per capita rental equivalent 2013 GTQ lavadora de ropa
"asset_rental_final_14", -- Daily per capita rental equivalent 2013 GTQ ventilador
"asset_rental_final_15", -- Daily per capita rental equivalent 2013 GTQ m quina de coser
"asset_rental_final_18", -- Daily per capita rental equivalent 2013 GTQ autom vil pick up camionetilla
"asset_rental_final_19", -- Daily per capita rental equivalent 2013 GTQ moto motoneta
"asset_rental_final_20", -- Daily per capita rental equivalent 2013 GTQ cami n
"asset_rental_final_24", -- Daily per capita rental equivalent 2013 GTQ Otros art culos personales y de esparcimiento
"asset_rental_final_25", -- Daily per capita rental equivalent 2013 GTQ Otros art culos del hogar
"id",
"housing_total", -- Daily per capita consumption 2013 GTQ Housing rental equivalent
"asset_rental_final_1", -- Daily per capita rental equivalent 2013 GTQ estufa a gas o el ctrica
"asset_rental_final_2", -- Daily per capita rental equivalent 2013 GTQ refrigeradora
"asset_rental_final_6", -- Daily per capita rental equivalent 2013 GTQ computadora
"asset_rental_final_8", -- Daily per capita rental equivalent 2013 GTQ c mara fotogr fica
"asset_rental_final_9", -- Daily per capita rental equivalent 2013 GTQ radio transistor
"asset_rental_final_11", -- Daily per capita rental equivalent 2013 GTQ panel solar
"asset_rental_final_16", -- Daily per capita rental equivalent 2013 GTQ calentador de agua
"asset_rental_final_17", -- Daily per capita rental equivalent 2013 GTQ silo met lico para guardar granos
"asset_rental_final_21", -- Daily per capita rental equivalent 2013 GTQ bicicleta
"asset_rental_final_22", -- Daily per capita rental equivalent 2013 GTQ Otro
"assets_total", -- Total expenditures in assets Daily per capita rental equivalent in 2013 GTQ
"total_consumption", -- Total daily consumption per capita 2013 UGX
"mhwt", -- Household weight for headcount ratios poverty indicators
"total_consumption_2010", -- Total consumption Daily per capita constant 2010 US dollars at 2005 PPP adjusted to 2010 US prices
"poverty_gap_index", -- Poverty gap index
"asset_rental_final_23", -- Daily per capita rental equivalent 2013 GTQ Otros art culos de cocina
"asset_25", -- Asset ownership Otros art culos del hogar
"asset_rental_final_5", -- Daily per capita rental equivalent 2013 GTQ olla a presi n
"poverty", -- Percent of people living on less than 1 25 day
"asset_24", -- Asset ownership Otros art culos personales y de esparcimiento
"asset_23", -- Asset ownership Otros art culos de cocina
"asset_rental_final_3", -- Daily per capita rental equivalent 2013 GTQ molino manual de nixtamal
"asset_rental_final_4", -- Daily per capita rental equivalent 2013 GTQ piedra de moler
"asset_rental_final_7", -- Daily per capita rental equivalent 2013 GTQ impresora
"asset_rental_final_10", -- Daily per capita rental equivalent 2013 GTQ televisor
"asset_rental_final_12" -- Daily per capita rental equivalent 2013 GTQ plancha
FROM
"usaid-gov/guatemala-food-consumption-data-section-6-6b89-9wbv:latest"."guatemala_food_consumption_data_section_6"
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 usaid-gov/guatemala-food-consumption-data-section-6-6b89-9wbv 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 usaid-gov/guatemala-food-consumption-data-section-6-6b89-9wbv: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 usaid-gov/guatemala-food-consumption-data-section-6-6b89-9wbvCheckout 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 usaid-gov/guatemala-food-consumption-data-section-6-6b89-9wbv:latestThis will download all the objects for the latest tag of usaid-gov/guatemala-food-consumption-data-section-6-6b89-9wbv 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 usaid-gov/guatemala-food-consumption-data-section-6-6b89-9wbv: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 usaid-gov/guatemala-food-consumption-data-section-6-6b89-9wbv: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, usaid-gov/guatemala-food-consumption-data-section-6-6b89-9wbv is just another Postgres schema.