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 20222023_nationwide_blood_donor_seroprevalence table in this repository, by referencing it like:
"cdc-gov/20222023-nationwide-blood-donor-seroprevalence-ar8q-3jhn:latest"."20222023_nationwide_blood_donor_seroprevalence"or in a full query, like:
SELECT
":id", -- Socrata column ID
"_2_5", -- 95% Confidence Interval, Lower Limit of Seroprevalence Estimate % (weighted)
"geographic_area", -- When geographic area is Overall (all 46 states included), defined as USA. When geographic area is a U.S. Census Region, it is defined as 1) Northeast 2) Midwest 3) South 4) West. When geographic area is an individual state, the state FIPS coded is listed.
"indicator", -- Description of seroprevalence count & estimate. 1) Overall: Total number of blood specimens tested for antibodies: See n (unweighted) for value. 2) Infection-induced seroprevalence: Prevalence of people with anti-nucleocapsid antibodies, with or without anti-spike antibodies. 3) Presumed vaccination without infection: Prevalence of people with anti-spike antibodies but without anti-nucleocapsid antibodies. 4) Combined seroprevalence: Prevalence of people with anti-nucleocapsid antibodies, anti-spike antibodies, or both. 5) Indeterminate: Prevalence of people with unknown status based on blood specimens with inconclusive or unavailable results. 6) Neither past infection nor vaccination: Prevalence of people without anti-nucleocapsid or anti-spike antibodies.
"estimate_weighted", -- Seroprevalence per definition in indicator
"n_unweighted", -- Number of blood specimens tested for antibodies for when indicator is overall. For other indicators, number of blood specimens that tested positive for antibodies meeting definition in indicator
"sex", -- Self reported sex
"geographic_identifier", -- Numerical Identifier of U.S. Census Region where specimens were collected. Defined as 1) Northeast 2) Midwest 3) South 4) West
"race", -- As reported by blood donor. Categorized as 1) Hispanic 2) Non-Hispanic Asian 3) Non-Hispanic Black 4) Non-Hispanic White 5) Other
"age", -- Categorized as 16 to 29 years, 30 to 49 years, 50 to 64 years, 65 years and over
"time_period", -- Time period during which specimens were collected, defined in quarterly intervals 1) Quarter 1 : January - March 2) Quarter 2: April - June 3) Quarter 3: July - September 4) Quarter 4: October - December
"_97_5" -- 95% Confidence Interval, Upper Limit of Seroprevalence Estimate % (weighted)
FROM
"cdc-gov/20222023-nationwide-blood-donor-seroprevalence-ar8q-3jhn:latest"."20222023_nationwide_blood_donor_seroprevalence"
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/20222023-nationwide-blood-donor-seroprevalence-ar8q-3jhn 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/20222023-nationwide-blood-donor-seroprevalence-ar8q-3jhn: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/20222023-nationwide-blood-donor-seroprevalence-ar8q-3jhnCheckout 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/20222023-nationwide-blood-donor-seroprevalence-ar8q-3jhn:latestThis will download all the objects for the latest tag of cdc-gov/20222023-nationwide-blood-donor-seroprevalence-ar8q-3jhn 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/20222023-nationwide-blood-donor-seroprevalence-ar8q-3jhn: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/20222023-nationwide-blood-donor-seroprevalence-ar8q-3jhn: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/20222023-nationwide-blood-donor-seroprevalence-ar8q-3jhn is just another Postgres schema.