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 medicare_claims_vision_and_eye_health_surveillance table in this repository, by referencing it like:
"cdc-gov/medicare-claims-vision-and-eye-health-surveillance-e28h-tx85:latest"."medicare_claims_vision_and_eye_health_surveillance"or in a full query, like:
SELECT
":id", -- Socrata column ID
"geolocation", -- Latitude & Longitude to be provided for formatting GeoLocation in the format (latitude, longitude)
"riskfactorid", -- Lookup identifier for the Major Risk Factor
"raceethnicityid", -- Lookup identifier for the Race/Ethnicity stratification
"questionid", -- Lookup identifier for the Question
"high_confidence_limit", -- 95% confidence interval upper bound
"low_confidence_limit", -- 95% confidence interval lower bound
"data_value", -- A numeric data value greater than or equal to 0, or no value when footnote symbol and text are present
"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)
"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
"locationdesc", -- Location (National, state or territory) full name
"categoryid", -- Lookup identifier for the Category
"locationid", -- Lookup identifier for the Location
"sample_size", -- Sample size used to calculate the data value
"numerator", -- The actual number of patients in the dataset with the condition (n)
"riskfactorresponse", -- Column holding the response for the risk factor that was evaluated (e.g., Total)
"riskfactor", -- Stratification value for major risk factor (e.g., All Patients)
"locationabbr", -- Location (National, state or territory) abbreviation
"data_value_type", -- The data value type, such as age-adjusted prevalence or crude prevalence
"datasource", -- Abbreviation of Data Source
"geographiclevel",
"yearstart", -- Starting year for year range
"datavaluetypeid", -- Lookup identifier for the data value type
"responseid", -- Lookup identifier for the Response
"data_value_footnote", -- Footnote text
"yearend", -- Ending year for year range. Same as starting year if single year used in evaluation.
"gender", -- Stratification value for gender (e.g., Total, Male, or Female)
"topicid", -- Lookup identifier for the Topic
"topic", -- Topic description
"riskfactorresponseid", -- Lookup identifier for the Major Risk Factor Response
"raceethnicity", -- Stratification value for race (e.g., All races, Asian, Black, non-hispanic, Hispanic, any race, North American Native, White, non-hispanic, or Other)
"genderid", -- Lookup identifier for the Gender stratification
"data_value_unit", -- The unit, such as "%" for percent
"data_value_footnote_symbol", -- Footnote symbol
"ageid", -- Lookup identifier for the Age stratification
":@computed_region_bxsw_vy29",
":@computed_region_he4y_prf8"
FROM
"cdc-gov/medicare-claims-vision-and-eye-health-surveillance-e28h-tx85:latest"."medicare_claims_vision_and_eye_health_surveillance"
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/medicare-claims-vision-and-eye-health-surveillance-e28h-tx85 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/medicare-claims-vision-and-eye-health-surveillance-e28h-tx85: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/medicare-claims-vision-and-eye-health-surveillance-e28h-tx85Checkout 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/medicare-claims-vision-and-eye-health-surveillance-e28h-tx85:latestThis will download all the objects for the latest tag of cdc-gov/medicare-claims-vision-and-eye-health-surveillance-e28h-tx85 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/medicare-claims-vision-and-eye-health-surveillance-e28h-tx85: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/medicare-claims-vision-and-eye-health-surveillance-e28h-tx85: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/medicare-claims-vision-and-eye-health-surveillance-e28h-tx85 is just another Postgres schema.