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 feed_the_future_zambia_baseline_population_based table in this repository, by referencing it like:
"usaid-gov/feed-the-future-zambia-baseline-population-based-mgix-5cq8:latest"."feed_the_future_zambia_baseline_population_based"or in a full query, like:
SELECT
":id", -- Socrata column ID
"h15", -- Pumpkin, carrots, squash, or sweet potatoes that are yellow or orange inside or other local yellow orange foods
"h20", -- Liver, kidney, heart, or other organ meats
"d07", -- Does this household have electricity?
"zih_women_age_range", -- Are you between the ages of 15 and 49 years old?
"f05", -- In the past 4 weeks 30 days did you or any household member go a whole day and night without eating anything at all because there was not enough food?
"h14", -- Food made from grains, such as bread, rice, noodles, porridge, or other local grain food
"underwght", -- Underweight (BMI 18.5)
"d02", -- Floor material
"h03", -- Please tell me how old you are. What was your age at your last birthday? RECORD AGE IN COMPLETED YEARS
"h22", -- Eggs
"f04", -- How often did this happen in the past 4 weeks 30 days ?
"foodsum", -- Number of Food Groups Consumed 0-9
"a06", -- Type of household
"h06", -- Are you currently pregnant?
"h16", -- White potatoes, white yams, manioc, cassava, other local root crops or any other foods made from roots
"othfrtgrp", -- Other fruits and vegetables
"f06", -- How often did this happen in the past 4 weeks 30 days ?
"a21", -- Final outcome of interview
"d01", -- Roof top material (outer covering)
"pbs_id", -- Unique household ID
"f02", -- How often did this happen in the past 4 weeks 30 days ?
"h19", -- Any other fruits or vegetables
"h28", -- Condiments for flavor, such as chilies, spices, herbs, or fish powder
"h27", -- Any sugary foods such as chocolates, sweets, candies, pastries, cakes, or biscuits
"h24", -- Any foods made from beans, peas, lentils, nuts, or seeds add any local names
"d03", -- Exterior Walls
"h29", -- Grubs, snails, or insects
"othvitagrp",
"a09",
"fleshgrp", -- Flesh foods (meat, fish, poultry and liver organ meats)
"h21", -- Any meat, such as beef, pork, lamb, goat, chicken, or duck
"d04", -- How many rooms are there in this dwelling? (Do not count bathrooms, hallways, garage, toilet, cellar, kitchen)
"h24x", -- NOT ASKED IN THIS SURVEY
"h26", -- Any oil, fats, or butter, or foods made with any of these
"zih_age_check_yn", -- IS THE RESPONDENT BETWEEN THE AGES OF 15 AND 49 YEARS?
"h25", -- Cheese, yogurt, or other milk products
"f03", -- In the past 4 weeks 30 days did you or any household member go to sleep at night hungry because there was not enough food?
"lfygrngrp", -- Other Vitamin A rich vegetables and fruits
"h18", -- Ripe mangoes, ripe papayas or other local vitamin A-rich fruits
"h07", -- WEIGHT IN KILOGRAMS
"modf_missing", -- Missing all elements from module F
"eggsgrp", -- Eggs
"zih_married",
"c02", -- What is NAME s sex?
"id_code", -- Respondent ID in the household
"beansgrp", -- Legumes and nuts
"grainsgrp",
"modd_missing", -- Missing all elements from module D
"urbrur", -- Urban, rural
"country", -- Country recorded in module G
"bmi", -- Women s BMI (weight in kg (height in m squared))
"h02_year", -- In what year were you born?
"h02_month", -- In what month were you born?
"a02", -- Household listing number
"zih_bmi", -- Derived in ODK
"wmwght", -- Women s sample weight
"hhwght", -- Derived variable, household weight
"h08", -- HEIGHT IN CENTIMETERS
"d06", -- What is the main source of drinking water for your household?
"h23", -- Fresh or dried fish, shellfish, or seafood
"c04", -- What is NAME s age? (in years)
"organgrp", -- Organ meat
"h17", -- Any dark green leafy vegetables such as local dark green leafy vegetables
"c03", -- What is NAME s relationship to the primary respondent?
"milkgrp", -- Dairy products (milk, yogurt, cheese)
"h30", -- Foods made with red palm oil, red palm nut, or red palm nut pulp sauce
"f01", -- In the past 4 weeks 30 days was there ever no food to eat of any kind in your house because of lack of resources to get food?
"d05", -- What is the main type of toilets your household uses?
"d08", -- What is the main source of cooking fuel for your household?
"a05" -- Sex of respondent
FROM
"usaid-gov/feed-the-future-zambia-baseline-population-based-mgix-5cq8:latest"."feed_the_future_zambia_baseline_population_based"
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/feed-the-future-zambia-baseline-population-based-mgix-5cq8 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/feed-the-future-zambia-baseline-population-based-mgix-5cq8: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/feed-the-future-zambia-baseline-population-based-mgix-5cq8Checkout 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/feed-the-future-zambia-baseline-population-based-mgix-5cq8:latestThis will download all the objects for the latest tag of usaid-gov/feed-the-future-zambia-baseline-population-based-mgix-5cq8 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/feed-the-future-zambia-baseline-population-based-mgix-5cq8: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/feed-the-future-zambia-baseline-population-based-mgix-5cq8: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/feed-the-future-zambia-baseline-population-based-mgix-5cq8 is just another Postgres schema.