Query the Data Delivery Network

The 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 new_york_city_community_health_survey table in this repository, by referencing it like:

"cityofnewyork-us/new-york-city-community-health-survey-csut-3wpr:latest"."new_york_city_community_health_survey"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "q_6", -- Binge drinking is defined as five or more drinks on one occasion for men and four or more drinks on one occasion for women in the past 30 days.  1=yes, 2=no 
    "q_3", -- Do you have one person (or more than one person) you think of as your personal doctor or health care provider? 1=yes, 2=no 
    "prevelance", -- The percent of New York City adults who have the characteristic being described. Confidence Interval (CI) is a measure of the precision of an estimate: the wider the CI, the more imprecise the estimate. The Lower Bound of the confidence interval is the lower threshold of imprecision. If a different sample of New York City adults were interviewed using the same methodology, 95% of the time their answers would fall between the lower bound and the upper bound of the confidence interval. Confidence Interval (CI) is a measure of the precision of the estimate: the wider the CI, the more imprecise the estimate. The Upper Bound of the confidence interval is the upper threshold of imprecision. If a different sample of New York City adults were interviewed using the same methodology, 95% of the time their answers would fall between the lower bound and the upper bound of the confidence interval.
    "q_2", -- Was there a time in the past 12 months when you needed medical care, but did not get it?  1=yes, 2=no 
    "q_10", -- Percent of adults who received a flu shot in their arm or a flu vaccine that was sprayed in their nose among adults ages 65 and older 1= yes, 2= no 
    "q_7", -- Body Mass Index (BMI) is calculated based on respondents self-reported weight and height. A BMI of 30 or greater is classified as obese. "1= Under/Normal: <25 2=Overwt: 25<bmi<30 3=Obese: 30<bmi<100 . =Missing" 
    "q_5", -- Smoking status is defined as being a current or former smoker or having smoked less than 100 cigarettes ever (never smoker).    1=never, 2= current, 3= former  
    "year", -- The year that the survey was conducted
    "q_9", -- Percentage of adults who report their health is “excellent,” “very good” or “good”  1=Excellent/Very good/Good 2=Fair/Poor 
    "q_8", -- Timely colon cancer screening is defined as having had a colonoscopy in the past 10 years among adults ages 50 and older 1=Less than10 yrs ago 2=Never or >10yr 
    "q_1", -- Do you have a health insurance policy? 1=yes, 2=no 
    "q_4" -- Respondents were asked how many 12 oz. sugar-sweetened beverages (sodas, iced tea, sports drinks, etc.) they drink per day on average.  1= yes, 2= no 
FROM
    "cityofnewyork-us/new-york-city-community-health-survey-csut-3wpr:latest"."new_york_city_community_health_survey"
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/new-york-city-community-health-survey-csut-3wpr with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.cityofnewyork.us. When you querycityofnewyork-us/new-york-city-community-health-survey-csut-3wpr: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)"

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.cityofnewyork.us, 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 \
  "cityofnewyork-us/new-york-city-community-health-survey-csut-3wpr" \
  --handler-options '{
    "domain": "data.cityofnewyork.us",
    "tables": {
        "new_york_city_community_health_survey": "csut-3wpr"
    }
}'

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, cityofnewyork-us/new-york-city-community-health-survey-csut-3wpr is just another Postgres schema.