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 total_compensation_and_expenses_fy20152019 table in this repository, by referencing it like:
"vermont-gov/total-compensation-and-expenses-fy20152019-69uf-6qeu:latest"."total_compensation_and_expenses_fy20152019"or in a full query, like:
SELECT
":id", -- Socrata column ID
"agency",
"total_compensation", -- The sum of "Total Pay," and "Total Benefits."
"job_type", -- Classified, Exempt, General Assembly, Temporary, or Contractual
"total_expenses", -- Includes reimbursement for mileage, meals and other expenses.
"multiple_record", -- When "Multiple Record" is indicated this means that there is more than one record for the same employee. This can be the result of that employee changing jobs, being employed in multiple temporary jobs, or moving from one job type to another (e.g., moving from a temporary job to a classified job).
"department",
"total_benefits", -- State contribution to Retirement and Social Security, and where applicable Health, Dental, Life, and Long Term Disability Insurance, and Employee Assistance Program.
"total_pay", -- The sum of "Pay," "Other Pay," and "Overtime Pay."
"fiscal_year",
"other_pay", -- This is a category of payments made to the employee that are not "pay" or "overtime" and includes such items as shift differential, standby, clothing allowance, annual leave payoff (upon retirement or RIF), prior year compensatory leave payoff, etc.
"job_title",
"name",
"overtime_units", -- Units used to calculate overtime pay are based on premium and straight-time hours worked, shift differential and the like. Note: Overtime units cannot the used to determine overtime hours worked.
"pay", -- The amount paid to the employee for "standard hours", which for most full-time employees is 2,080 hours per year (some protective service employees have standard hours greater than 2,080). This includes "regular" pay, paid holidays, sick leave, annual leave, compensatory leave, personal leave, and paid leave. Historically, temporary employees were paid only for hours worked - however, as of 2017 temporary employees are also paid for sick leave per 21 V.S.A. § 487. General Assembly members are paid a weekly rate during the legislative session.
"overtime_pay" -- This is cash paid for hours worked in excess of defined workday and/or workweek at rates established by law and/or in the applicable Collective Bargaining Agreements.
FROM
"vermont-gov/total-compensation-and-expenses-fy20152019-69uf-6qeu:latest"."total_compensation_and_expenses_fy20152019"
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 vermont-gov/total-compensation-and-expenses-fy20152019-69uf-6qeu 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 vermont-gov/total-compensation-and-expenses-fy20152019-69uf-6qeu: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 vermont-gov/total-compensation-and-expenses-fy20152019-69uf-6qeuCheckout 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 vermont-gov/total-compensation-and-expenses-fy20152019-69uf-6qeu:latestThis will download all the objects for the latest tag of vermont-gov/total-compensation-and-expenses-fy20152019-69uf-6qeu 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 vermont-gov/total-compensation-and-expenses-fy20152019-69uf-6qeu: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 vermont-gov/total-compensation-and-expenses-fy20152019-69uf-6qeu: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, vermont-gov/total-compensation-and-expenses-fy20152019-69uf-6qeu is just another Postgres schema.