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 campaign_reports_for_solicitation_notices_to table in this repository, by referencing it like:
"colorado-gov/campaign-reports-for-solicitation-notices-to-fdcw-ei67:latest"."campaign_reports_for_solicitation_notices_to"or in a full query, like:
SELECT
":id", -- Socrata column ID
"solicitationnoticeid", -- Solicitation Notice ID
"campaignreportid", -- Solicitation Notice Campaign Report ID
"filingtype", -- Filing Type: Master, Amendment, Renewal
"filingstatus", -- The status of the filing
"campaigndatefiled", -- Date campaign report was filed. Changes from source: Dates changed from MM/DD/YY to YYYY-MM-DD.
"charityname", -- Charitable Organization Official first and last name
"charitysigneddate", -- Date Charitable Organization Official signed campaign report. Changes from source: Dates changed from MM/DD/YY to YYYY-MM-DD.
"solicitorname", -- Paid Solicitor first and last name
"solicitorsigneddate", -- Date Paid Solicitor signed campaign report. Changes from source: Dates changed from MM/DD/YY to YYYY-MM-DD.
"grosscontributions", -- Gross proceeds; contributions
"grossticketsales", -- Gross proceeds: ticket sales
"grossadvertisingsales", -- Gross proceeds: advertising sales
"grossinkind", -- Grossproceeds: in kind
"grosstotal", -- Total of gross proceeds columns
"inkinddescription", -- Description of in-kind proceeds
"grossother", -- Gross proceeds: other revenue
"expensesalarycommision", -- Expenses: salaries and commissions
"expenseprinting", -- Expenses: printing of solicitation materials
"expensepostage", -- Expenses: postage
"expensetelephone", -- Expenses: telephone
"expenseofficerental", -- Expenses: office rental
"expenseofficeexpenses", -- Expenses: office expenses
"expensetotal", -- Total of all expense columns
"expenseauditoriumrental", -- Direct event expenses: auditorium rental
"expenseeventfee", -- Direct event expenses: show fee for performers
"expenseeventprinting", -- Direct event expenses: printing costs for tickets and program books
"expenseeventother", -- Direct event expenses: other expenses
"proceedsnet", -- Net proceeds to charity (gross proceeds minus expenses + direct event expenses)
"proceedspercenttocharity", -- Percentage of proceeds to charity (net charity divided by (expenses + direct even expenses X 100))
"isfinanciallocalonly", -- Does financial information reflect solicitation activity in Colorado only or nationally?
"bookkeeper", -- Person who is caretaker of books and records
"bookkeeperaddress", -- Address of bookkeeper
"bookkeepercity", -- City of bookkeeper. Changes from source: ran reconciliation algorithm to modify city name variations (resulting from manual data entry) to full standard (ie Colo Spg to Colorado Springs, etc).
"bookkeeperstate", -- State of bookkeeper
"bookkeeperzipcode", -- Zip of bookkeeper
"activityflag", -- Activity exclusive to Colorado. Changes from source: �Y�s changed to 1� and �N�s changed to 0�.
"comments" -- Comments
FROM
"colorado-gov/campaign-reports-for-solicitation-notices-to-fdcw-ei67:latest"."campaign_reports_for_solicitation_notices_to"
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 colorado-gov/campaign-reports-for-solicitation-notices-to-fdcw-ei67 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 colorado-gov/campaign-reports-for-solicitation-notices-to-fdcw-ei67: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 colorado-gov/campaign-reports-for-solicitation-notices-to-fdcw-ei67Checkout 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 colorado-gov/campaign-reports-for-solicitation-notices-to-fdcw-ei67:latestThis will download all the objects for the latest tag of colorado-gov/campaign-reports-for-solicitation-notices-to-fdcw-ei67 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 colorado-gov/campaign-reports-for-solicitation-notices-to-fdcw-ei67: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 colorado-gov/campaign-reports-for-solicitation-notices-to-fdcw-ei67: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, colorado-gov/campaign-reports-for-solicitation-notices-to-fdcw-ei67 is just another Postgres schema.