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 iowa_aging_services_consumer_counts_by_fiscal_year table in this repository, by referencing it like:
"mydata-iowa-gov/iowa-aging-services-consumer-counts-by-fiscal-year-3qxc-gxc2:latest"."iowa_aging_services_consumer_counts_by_fiscal_year"or in a full query, like:
SELECT
":id", -- Socrata column ID
"hawaiian_pacific_islander_consumers", -- Total number of individuals who self-reported race status as Native Hawaiian/Other Pacific Islander
"asian_consumers", -- Total number of individuals who self-reported race status as Asian
"native_american_consumers", -- Total number of individuals who self-reported race status as American Indian/Native Alaskan
"white_not_hispanic_consumers", -- Total number of individuals who self-reported race status as White (not Hispanic).
"non_hispanic_consumers", -- Total number of individuals who did not self-report ethnicity as Hispanic.
"hispanic_consumers", -- Total number of individuals who self-reported ethnicity as Hispanic.
"consumers_in_poverty", -- Total number of individuals whose self-reported household size and income indicated a poverty status..
"conumsers_living_alone", -- Total number of individuals who self-reported gender as living alone.
"consumers_in_rural_areas", -- Total number of individuals whose self-reported zip code is considered rural. Rural definition up to 2018 was the U.S. Census Bureau definition. In 2019, the rural definition was changed to match rural designations according to The rural-urban commuting area (RUCA) codes classify U.S. census tracts using measures of population density, urbanization, and daily commuting developed an published by U.S. Dept of Agriculture Economic Research Sevice.
"male_consumers", -- Total number of individuals who self-reported gender as male.
"consumers", -- Total number of individuals who received at least 1 unit of the service.
"service", -- Services provided in accordance with Older Americans Act authorization.
"consumers_other_race", -- Total number of individuals who self-reported race status as Other
"african_american_consumers", -- Total number of individuals who self-reported race status as African American
"year", -- The state fiscal year for which the services were provided. The State Fiscal Year runs from July 1 - June 30 and is numbered for the year in which it ends.
"consumers_two_or_more_races", -- Total number of individuals who self-reported race status as two or more races
"white_hispanic_consumers", -- Total number of individuals who self-reported race status as White (Hispanic).
"female_consumers", -- Total number of individuals who self-reported gender as female.
"age_group" -- Age group as determined by date of birth and age at the end of the previous federal fiscal year.
FROM
"mydata-iowa-gov/iowa-aging-services-consumer-counts-by-fiscal-year-3qxc-gxc2:latest"."iowa_aging_services_consumer_counts_by_fiscal_year"
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 mydata-iowa-gov/iowa-aging-services-consumer-counts-by-fiscal-year-3qxc-gxc2 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 mydata-iowa-gov/iowa-aging-services-consumer-counts-by-fiscal-year-3qxc-gxc2: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 mydata-iowa-gov/iowa-aging-services-consumer-counts-by-fiscal-year-3qxc-gxc2Checkout 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 mydata-iowa-gov/iowa-aging-services-consumer-counts-by-fiscal-year-3qxc-gxc2:latestThis will download all the objects for the latest tag of mydata-iowa-gov/iowa-aging-services-consumer-counts-by-fiscal-year-3qxc-gxc2 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 mydata-iowa-gov/iowa-aging-services-consumer-counts-by-fiscal-year-3qxc-gxc2: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 mydata-iowa-gov/iowa-aging-services-consumer-counts-by-fiscal-year-3qxc-gxc2: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, mydata-iowa-gov/iowa-aging-services-consumer-counts-by-fiscal-year-3qxc-gxc2 is just another Postgres schema.