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 alert_montgomery_statistics table in this repository, by referencing it like:
"montgomerycountymd-gov/alert-montgomery-statistics-encb-ivkj:latest"."alert_montgomery_statistics"or in a full query, like:
SELECT
":id", -- Socrata column ID
"monthly_percent_change_of_contacts", -- The percent increase or decrease in registered contacts over the course of the reporting month.
"contacts_at_month_s_end", -- The number of contacts that are registered in Alert Montgomery at 11:59 pm on the last day or the reporting month.
"contacts_at_month_s_start", -- The number of contacts that are registered in Alert Montgomery at midnight on the first of the reporting month.
"average_contacts_per_notification", -- The average number of contacts per notification which is derived in Excel by dividing Contacts Notified Column by Number of Notifications Column.
"contacts_notified", -- The summation of the number of contacts that are in each notification detailed in Columns Traffic/Mass Transit, Weather, Public Health, Geographic, IPAWS / WEA, Government/School, and Other.
"other", -- The total number of notifications that are sent to all other subscribers. This includes, but is not limited to: Rec Alerts, All Contact Alerts, Gilchrist Center and District Police Notifications.
"government_school", -- The total number of notifications that are sent to any of the Government or School subscribers. This does include Circuit Court subscribers.
"weather", -- The total number of notifications that are sent to any weather subscriber
"traffic_mass_transit", -- The total number of notifications that are sent to the Severe Traffic or Mass Transit subscribers.
"month_year", -- The reporting month/Year. EX: 01/2018
"number_of_notifications", -- The summation of values from columns Traffic/Mass Transit, Weather, Public Health, Geographic, IPAWS / WEA, Government/School, and Other.
"geographic", -- The total number of notifications that are sent using a geographic boundary. EX: 3/4 miles radius from a given address for a gas leak.
"public_health", -- The total number of notifications that are sent to Public Health Alert subscribers.
"ipaws_wea" -- The total number of public facing Integrated Public Alert and Warning System or Wireless Emergency Alert (IPAWS/ WEA) notifications that are disseminated to the public. This includes any public facing tests.
FROM
"montgomerycountymd-gov/alert-montgomery-statistics-encb-ivkj:latest"."alert_montgomery_statistics"
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 montgomerycountymd-gov/alert-montgomery-statistics-encb-ivkj 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 montgomerycountymd-gov/alert-montgomery-statistics-encb-ivkj: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 montgomerycountymd-gov/alert-montgomery-statistics-encb-ivkjCheckout 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 montgomerycountymd-gov/alert-montgomery-statistics-encb-ivkj:latestThis will download all the objects for the latest tag of montgomerycountymd-gov/alert-montgomery-statistics-encb-ivkj 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 montgomerycountymd-gov/alert-montgomery-statistics-encb-ivkj: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 montgomerycountymd-gov/alert-montgomery-statistics-encb-ivkj: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, montgomerycountymd-gov/alert-montgomery-statistics-encb-ivkj is just another Postgres schema.