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 audit_recommendations table in this repository, by referencing it like:
"datahub-austintexas-gov/audit-recommendations-y4qq-mqk9:latest"."audit_recommendations"or in a full query, like:
SELECT
":id", -- Socrata column ID
"status", -- Status as reported upon follow-up by Office of the City Auditor staff. A follow-up audit is an audit designed to test the efforts departments have made to address previously issued audit recommendations. During a follow-up audit, we may interview personnel, review documentation, and conduct inspections to determine if risks noted in prior audits have been effectively addressed. Follow-up audits are usually shorter and more focused than other performance audits, and the results of these audits are summarized and typically presented to the Audit and Finance Committee twice per year.
"report_title", -- The title of the audit report that included the recommendation.
"dept", -- The department to whom the recommendation is addressed. In some cases, the department has been renamed or combined with other departments since the original report was issued.
"council_strategic_outcome", -- Associated Council strategic outcome as assigned by the Office of the City Auditor.
"follow_up_priority", -- Follow-up priority is determined by the Office of the City Auditor according to the following definitions: High (H) priority: Critical Citywide issue, priority, or initiative; recommendation poses immediate safety risk. Medium (M) priority: Issue, priority, or initiative is important but affects only one or few departments; recommendation does not pose immediate safety risk. Low (L) priority: Issue, priority, or initiative is moderately important but only affects one or few departments; recommendation does not pose a safety risk.
"date_report_issued", -- The month and year the audit report was issued.
"fy", -- The City of Austin's fiscal year runs from October 1 through September 30.
"note", -- Explains why recommendation is no longer applicable if status is listed as no longer applicable.
"link_to_report", -- URL link to published report
"recommendation"
FROM
"datahub-austintexas-gov/audit-recommendations-y4qq-mqk9:latest"."audit_recommendations"
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 datahub-austintexas-gov/audit-recommendations-y4qq-mqk9 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 datahub-austintexas-gov/audit-recommendations-y4qq-mqk9: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 datahub-austintexas-gov/audit-recommendations-y4qq-mqk9Checkout 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 datahub-austintexas-gov/audit-recommendations-y4qq-mqk9:latestThis will download all the objects for the latest tag of datahub-austintexas-gov/audit-recommendations-y4qq-mqk9 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 datahub-austintexas-gov/audit-recommendations-y4qq-mqk9: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 datahub-austintexas-gov/audit-recommendations-y4qq-mqk9: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, datahub-austintexas-gov/audit-recommendations-y4qq-mqk9 is just another Postgres schema.