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 covid19_outcomes_by_vaccination_status_historical table in this repository, by referencing it like:
"cityofchicago/covid19-outcomes-by-vaccination-status-historical-6irb-gasv:latest"."covid19_outcomes_by_vaccination_status_historical"or in a full query, like:
SELECT
":id", -- Socrata column ID
"crude_boosted_ratio", -- Crude incidence rate ratio (unvaccinated rate : boosted rate).
"age_adjusted_boosted_ratio", -- Age-adjusted incidence rate ratio (age-adjusted unvaccinated rate : age-adjusted boosted rate). Age-adjustment is only calculated for the “All” age group and is blank for all other groups.
"week_end", -- The end date of the week (Sunday-Saturday)
"population_boosted", -- Total number of boosted people in the age group.
"crude_vaccinated_ratio", -- Crude incidence rate ratio (unvaccinated rate : vaccinated rate).
"outcome", -- COVID-19 case, hospitalization, or death.
"age_adjusted_unvaccinated_rate", -- Age-adjusted rate of the outcome per 100,000 unvaccinated Chicago residents. Age-adjustment is only calculated for the “All” age group and is blank for all other groups.
"outcome_boosted", -- Total number of boosted people in the age group with the outcome.
"outcome_vaccinated", -- Total number of vaccinated people in the age group with the outcome.
"population_unvaccinated", -- Total number of unvaccinated people in the age group.
"unvaccinated_rate", -- Rate of the outcome per 100,000 unvaccinated Chicago residents in the age group
"age_adjusted_vaccinated_ratio", -- Age-adjusted incidence rate ratio (age-adjusted unvaccinated rate : age-adjusted vaccinated rate). Age-adjustment is only calculated for the “All” age group and is blank for all other groups.
"age_adjusted_boosted_rate", -- Age-adjusted rate of the outcome per 100,000 fully vaccinated and boosted Chicago residents. Age-adjustment is only calculated for the “All” age group and is blank for all other groups.
"age_adjusted_vaccinated_rate", -- Age-adjusted rate of the outcome per 100,000 fully vaccinated but not boosted Chicago residents. Age-adjustment is only calculated for the “All” age group and is blank for all other groups.
"age_group", -- The age group described, in years. Note the presence of an "All" value so care should be taken in summing values.
"boosted_rate", -- Rate of the outcome per 100,000 fully vaccinated and boosted Chicago residents in the age group
"population_vaccinated", -- Total number of fully vaccinated people in the age group.
"outcome_unvaccinated", -- Total number of unvaccinated people in the age group with the outcome.
"vaccinated_rate", -- Rate of the outcome per 100,000 fully vaccinated but not boosted Chicago residents in the age group
"age_group_min", -- The low end of the age group. This column is used primarily to allow for easier sorting. The "All" age group is set to 999.
"age_group_max" -- TO BE HIDDEN: The high end of the age group. This column is used primarily to allow for easier sorting. The All age group is set to 999. The 80+ group is set to 200.
FROM
"cityofchicago/covid19-outcomes-by-vaccination-status-historical-6irb-gasv:latest"."covid19_outcomes_by_vaccination_status_historical"
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 cityofchicago/covid19-outcomes-by-vaccination-status-historical-6irb-gasv 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 cityofchicago/covid19-outcomes-by-vaccination-status-historical-6irb-gasv: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 cityofchicago/covid19-outcomes-by-vaccination-status-historical-6irb-gasvCheckout 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 cityofchicago/covid19-outcomes-by-vaccination-status-historical-6irb-gasv:latestThis will download all the objects for the latest tag of cityofchicago/covid19-outcomes-by-vaccination-status-historical-6irb-gasv 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 cityofchicago/covid19-outcomes-by-vaccination-status-historical-6irb-gasv: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 cityofchicago/covid19-outcomes-by-vaccination-status-historical-6irb-gasv: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, cityofchicago/covid19-outcomes-by-vaccination-status-historical-6irb-gasv is just another Postgres schema.