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_menustat_historical table in this repository, by referencing it like:
"cityofnewyork-us/dohmh-menustat-historical-qgc5-ecnb:latest"."dohmh_menustat_historical"or in a full query, like:
SELECT
":id", -- Socrata column ID
"total_fat_text", -- Nutrient reported in a non-specific value
"protein",
"calories_text", -- Nutrient reported in a non-specific value
"saturated_fat",
"trans_fat_100g", -- Nutrient density is calculated by standardizing the serving size to 100 g when serving size and nutrient information is reported by the restaurant.
"sugar",
"serving_size_unit", -- Serving size in grams or ounces when reported by restaurants. For a limited number of items, serving size was converted to grams for foods and to ounces for beverages; these cases are marked by an asterisk.
"dietary_fiber",
"shareable", -- Coded as ‘1’ if the restaurant describes the item as shareable and the nutrition cannot be divided in to a single serving (e.g. carafes, whole pies, quarts of ice cream, 2 liter drinks).
"protein_text", -- Nutrient reported in a non-specific value
"calories",
"menu_item_id", -- A unique identifier for each item. When the same item is served by a restaurant over multiple years it has an identical Menu Item ID to allow for tracking.
"potassium_100g", -- Nutrient density is calculated by standardizing the serving size to 100 g when serving size and nutrient information is reported by the restaurant.
"sugar_100g", -- Nutrient density is calculated by standardizing the serving size to 100 g when serving size and nutrient information is reported by the restaurant.
"sodium_text", -- Nutrient reported in a non-specific value
"regional", -- Coded as ‘1’ if the restaurant describes the item as regional (e.g. Midwest states only or at participating locations only).
"total_fat",
"kids_meal", -- Coded as ‘1’ if the restaurant describes the item as being for kids. Items described as for kids and adults (e.g. kids fries = small fries) says ‘Kids and Adults Menu’ in the Item Description.
"year", -- MenuStat data are collected annually in January.
"dietary_fiber_text", -- Nutrient reported in a non-specific value
"serving_size", -- Serving size in grams or ounces when reported by restaurants
"carbohydrates",
"carbohydrates_100g", -- Nutrient density is calculated by standardizing the serving size to 100 g when serving size and nutrient information is reported by the restaurant.
"cholesterol_100g", -- Nutrient density is calculated by standardizing the serving size to 100 g when serving size and nutrient information is reported by the restaurant.
"limited_time_offer", -- Coded as ‘1’ if the restaurant describes the item as a limited time offer or seasonal.
"cholesterol",
"dietary_fiber_100g", -- Nutrient density is calculated by standardizing the serving size to 100 g when serving size and nutrient information is reported by the restaurant.
"sodium",
"item_name", -- Menu item name
"total_fat_100g", -- Nutrient density is calculated by standardizing the serving size to 100 g when serving size and nutrient information is reported by the restaurant.
"food_category", -- Each menu item is coded into a mutually exclusive food category
"carbohydrates_text", -- Nutrient reported in a non-specific value
"cholesterol_text", -- Nutrient reported in a non-specific value
"protein_100g", -- Nutrient density is calculated by standardizing the serving size to 100 g when serving size and nutrient information is reported by the restaurant.
"calories_100g", -- Nutrient density is calculated by standardizing the serving size to 100 g when serving size and nutrient information is reported by the restaurant.
"sugar_text", -- Nutrient reported in a non-specific value
"potassium_text", -- Nutrient reported in a non-specific value
"trans_fat",
"serving_size_household", -- Household metric reported to describe serving size
"sodium_100g", -- Nutrient density is calculated by standardizing the serving size to 100 g when serving size and nutrient information is reported by the restaurant.
"restaurant_id", -- Unique Identifier for Restaurant
"restaurant_item_name", -- Concatenated variable of Restaurant and Item Name
"trans_fat_text", -- Nutrient reported in a non-specific value
"saturated_fat_100g", -- Nutrient density is calculated by standardizing the serving size to 100 g when serving size and nutrient information is reported by the restaurant.
"item_description", -- Menu item name with additional menu information (e.g. menu item components)
"saturated_fat_text", -- Nutrient reported in a non-specific value
"restaurant", -- Restaurant name
"potassium",
"serving_size_text" -- Serving size reported in a non-specific value
FROM
"cityofnewyork-us/dohmh-menustat-historical-qgc5-ecnb:latest"."dohmh_menustat_historical"
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-menustat-historical-qgc5-ecnb 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 cityofnewyork-us/dohmh-menustat-historical-qgc5-ecnb: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 cityofnewyork-us/dohmh-menustat-historical-qgc5-ecnbCheckout 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 cityofnewyork-us/dohmh-menustat-historical-qgc5-ecnb:latestThis will download all the objects for the latest tag of cityofnewyork-us/dohmh-menustat-historical-qgc5-ecnb 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 cityofnewyork-us/dohmh-menustat-historical-qgc5-ecnb: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 cityofnewyork-us/dohmh-menustat-historical-qgc5-ecnb: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, cityofnewyork-us/dohmh-menustat-historical-qgc5-ecnb is just another Postgres schema.