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 estimated_total_lost_wages_due_to_opioid_use_cy table in this repository, by referencing it like:
"pa-gov/estimated-total-lost-wages-due-to-opioid-use-cy-xzsj-mqqr:latest"."estimated_total_lost_wages_due_to_opioid_use_cy"or in a full query, like:
SELECT
":id", -- Socrata column ID
"latitude",
"latitude_longitude", -- Latitude and longitude coordinates in degrees for a centroid point for geographic area.
"county_fips_code", -- Last 3 digits of the 5-digit Federal Information Processing Standard (FIPS) code that designate the county association. Each state has its own 2-digit number and each county within the state has its own 3-digit number which are combined into a 5-digit number to uniquely identify every US county.
"county_code_text", -- Pennsylvania county code provided as text (1-67 for counties sorted alphabetically, 0 for Commonwealth).
"county_code_number", -- Pennsylvania county code provided as a number (1-67 for counties, 0 for Commonwealth).
"estimated_lost_state_income_tax", -- Estimated state income tax revenue associated with lost wages due to opioid use; 3.07% of lost wages.
"longitude",
"estimated_lost_state_income_tax_description", -- Description of estimated lost state income tax revenue.
"estimated_lost_wages", -- Estimated wages lost due to absenteeism, reduced productivity, hospitalization, unemployment and labor force exits due to opioid use. This value is the product of the estimated number of individuals with drug use disorder, the average weekly wages for county of residence—as reported by the Bureau of Labor Statistics—multiplied by 52 to represent annual wages, and a 17% reduction in productivity.
"drug_use_disorder_estimate_notes", -- Indicates if the estimated number of individuals with drug use disorder has been suppressed to protect confidentiality. Counts less than 11 are not provided.
"drug_use_disorder_estimate", -- Estimated number of individuals with drug use disorder.
"time_period", -- Period of measurement (annual, federal fiscal year, or quarterly if available).
"year", -- Calendar year of measurement (January 1–December 31).
"geographic_name", -- Name of geographic area.
"geocoded_column", -- Georeferenced column as a point used for creating visuals such as maps. A generic point for reach county is supplied so a map can be created.
"age", -- Ages of individuals hospitalized (21 to 64 years).
"gender", -- Gender of individuals hospitalized.
"time_period_dates", -- Start and end dates of time period.
"geographic_area", -- Region measured, either total for Commonwealth or county.
":@computed_region_nmsq_hqvv",
":@computed_region_d3gw_znnf",
":@computed_region_rayf_jjgk",
":@computed_region_r6rf_p9et",
":@computed_region_amqz_jbr4",
"state_fips_code", -- First 2 digits of the 5-digit Federal Information Processing Standard (FIPS) code that designate the state association. Each state has its own 2-digit number and each county within the state has its own 3-digit number which are combined into a 5-digit number to uniquely identify every US county.
"estimated_lost_wages_description", -- Description of estimated lost wages.
"drug_use_disorder_description" -- Describes number of estimated individuals with drug use disorder.
FROM
"pa-gov/estimated-total-lost-wages-due-to-opioid-use-cy-xzsj-mqqr:latest"."estimated_total_lost_wages_due_to_opioid_use_cy"
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 pa-gov/estimated-total-lost-wages-due-to-opioid-use-cy-xzsj-mqqr 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 pa-gov/estimated-total-lost-wages-due-to-opioid-use-cy-xzsj-mqqr: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 pa-gov/estimated-total-lost-wages-due-to-opioid-use-cy-xzsj-mqqrCheckout 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 pa-gov/estimated-total-lost-wages-due-to-opioid-use-cy-xzsj-mqqr:latestThis will download all the objects for the latest tag of pa-gov/estimated-total-lost-wages-due-to-opioid-use-cy-xzsj-mqqr 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 pa-gov/estimated-total-lost-wages-due-to-opioid-use-cy-xzsj-mqqr: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 pa-gov/estimated-total-lost-wages-due-to-opioid-use-cy-xzsj-mqqr: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, pa-gov/estimated-total-lost-wages-due-to-opioid-use-cy-xzsj-mqqr is just another Postgres schema.