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 household_pulse_survey_hps_covid19_vaccination table in this repository, by referencing it like:

"cdc-gov/household-pulse-survey-hps-covid19-vaccination-muep-c3qd:latest"."household_pulse_survey_hps_covid19_vaccination"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "estimate_type", -- Estimate categorization; one of “Vaccinated”, “Definitely Plan to Get Vaccinated”, “Probably Will Get Vaccinated or Are Unsure”, or “Probably or Definitely Will Not Get Vaccinated”
    "week", -- The date range during which data was collected to produce these estimates. More information about data collection periods can be found at https://www.census.gov/programs-surveys/household-pulse-survey/technical-documentation.html.
    "coninf_95", -- The 95% confidence interval of the estimate.
    "demographic", -- The sociodemographic group for which estimates are calculated.
    "suppression_flag", -- Estimates that did not meet the National Center for Health Statistics (NCHS) standards of reliability are suppressed (left missing) and have a value of 1 for this field. If the estimate met the NCHS standards of reliability, the estimate was not suppressed, and this field has a value of 0. 
    "sample_size", -- The unweighted sample size used to create the estimate for each demographic and disability status grouping. 
    "estimate", -- The numerical estimate of the weighted proportion giving the response.
    "status", -- The reported disability status for which estimates are calculated. The two statuses are “With Disability” and “Without Disability.” 
    "disability_type", -- One of the WG-SS four domains of functioning: seeing (even when wearing glasses), hearing (even when using a hearing aid), mobility (walking or climbing stairs), and cognition (remembering or concentrating); or "Any Disability" for respondents with any disability.
    "category" -- The classification of the sociodemographic category for which estimates are calculated. The two applicable categories are "Age" and "Race/Ethnicity."
FROM
    "cdc-gov/household-pulse-survey-hps-covid19-vaccination-muep-c3qd:latest"."household_pulse_survey_hps_covid19_vaccination"
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/household-pulse-survey-hps-covid19-vaccination-muep-c3qd with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.cdc.gov. When you querycdc-gov/household-pulse-survey-hps-covid19-vaccination-muep-c3qd: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.cdc.gov, 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 \
  "cdc-gov/household-pulse-survey-hps-covid19-vaccination-muep-c3qd" \
  --handler-options '{
    "domain": "data.cdc.gov",
    "tables": {
        "household_pulse_survey_hps_covid19_vaccination": "muep-c3qd"
    }
}'

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, cdc-gov/household-pulse-survey-hps-covid19-vaccination-muep-c3qd is just another Postgres schema.