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 national_health_interview_survey_nhis_vision_and table in this repository, by referencing it like:
"cdc-gov/national-health-interview-survey-nhis-vision-and-2t2r-sf6s:latest"."national_health_interview_survey_nhis_vision_and"or in a full query, like:
SELECT
":id", -- Socrata column ID
"geolocation_zip",
"categoryid", -- Lookup identifier for the Category
"questionid", -- Lookup identifier for the Question
"responseid", -- Lookup identifier for the Response
"ageid", -- Lookup identifier for the Age stratification
"genderid", -- Lookup identifier for the Gender stratification
"raceethnicityid", -- Lookup identifier for the Race/Ethnicity stratification
"riskfactorresponseid", -- Lookup identifier for the Major Risk Factor Response
"data_value_footnote_symbol", -- Footnote symbol
"age", -- Stratification value for age group (e.g., All ages, 0-17 years, 18-39 years, 40-64 years, 65-84 years, or 85 years and older)
"gender", -- Stratification value for gender (e.g., Total, Male, or Female)
"data_value_footnote", -- Footnote text
"geolocation_city",
"datasource", -- Abbreviation of Data Source
"data_value", -- A numeric data value greater than or equal to 0, or no value when footnote symbol and text are present
"data_value_type", -- The data value type, such as age-adjusted prevalence or crude prevalence
"data_value_unit", -- The unit, such as "%" for percent
"riskfactorresponse", -- Column holding the response for the risk factor that was evaluated (e.g., All Participants, Borderline, Current Smoker, Former Smoker, Never Smoker, Yes, or No)
"riskfactor", -- Stratification value for major risk factor (e.g., All Participants, Diabetes, Hypertension, Smoking)
"raceethnicity", -- Stratification value for race (e.g., All races, Asian, Black, non-hispanic, Hispanic, any race, North American Native, White, non-hispanic, or Other)
"response", -- Optional column to hold the response value that was evaluated.
"question", -- Question description (e.g., Percentage of adults with diabetic retinopathy)
"category", -- Category description
"topic", -- Topic description
"locationdesc", -- Location full name
"locationabbr", -- Location abbreviation
"yearend", -- Ending year for year range. Same as starting year if single year used in evaluation.
"yearstart", -- Starting year for year range
"riskfactorid", -- Lookup identifier for the Major Risk Factor
"geolocation", -- No Geolocation is provided for national data
"geographic_level",
"datavaluetypeid", -- Lookup identifier for the data value type
"low_confidence_limit", -- 95% confidence interval lower bound
"geolocation_state",
"high_confidence_limit", -- 95% confidence interval higher bound
"numerator", -- The prediction of the number of people who may have this condition in the state/country (n)
"sample_size", -- Sample size used to calculate the data value
"locationid", -- Lookup identifier value for the location
"geolocation_address",
"topicid" -- Lookup identifier for the Topic
FROM
"cdc-gov/national-health-interview-survey-nhis-vision-and-2t2r-sf6s:latest"."national_health_interview_survey_nhis_vision_and"
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 cdc-gov/national-health-interview-survey-nhis-vision-and-2t2r-sf6s 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 cdc-gov/national-health-interview-survey-nhis-vision-and-2t2r-sf6s: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 cdc-gov/national-health-interview-survey-nhis-vision-and-2t2r-sf6sCheckout 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 cdc-gov/national-health-interview-survey-nhis-vision-and-2t2r-sf6s:latestThis will download all the objects for the latest tag of cdc-gov/national-health-interview-survey-nhis-vision-and-2t2r-sf6s 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 cdc-gov/national-health-interview-survey-nhis-vision-and-2t2r-sf6s: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 cdc-gov/national-health-interview-survey-nhis-vision-and-2t2r-sf6s: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, cdc-gov/national-health-interview-survey-nhis-vision-and-2t2r-sf6s is just another Postgres schema.