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 ticket_sales_ice_rink_at_city_plaza table in this repository, by referencing it like:
"citydata-mesaaz-gov/ticket-sales-ice-rink-at-city-plaza-bca4-y9b7:latest"."ticket_sales_ice_rink_at_city_plaza"or in a full query, like:
SELECT
":id", -- Socrata column ID
"venue_city", -- Name of city where event is located
"venue_street", -- Full street address of event for which ticket was sold
"payment_type", -- Type of payment method used
"billing_country", -- Country of billing location
"month_of_purchase", -- Month of ticket purchase
"refunded", -- Would indicate if any money was refunded but we do not do refunds
"buyer_postal_code", -- Zip code of ticket buyer
"redeemed_date", -- Redemption date of ticket purchased online
"payment_processor_id", -- Unique ID generated by payment system
"order_id", -- Unique ID generated by system for order
"buyer_city", -- City of ticket buyer
"currency", -- Type of currency used
"date_of_purchase", -- Date and time of ticket purchase
"ticket_net_proceeds", -- Proceeds on purchase after any seller fees
"face_value", -- Dollar amount of ticket being purchased
"performance", -- Start date/time of the event
"barcode", -- Barcode generated by system for each transaction
"confirmation_code", -- Unique ID generated by system for each transaction
"order_total", -- Total dollar amount paid for tickets ordered
"year_of_purchase", -- Year of ticket purchase
"venue_name", -- Name of venue where event for which ticket was sold
"ticketleap_fees_paid_by_buyer", -- Convenience fee paid by buyer when tickets are purchased online
"ticket_order_total", -- Same as face value
"buyer_country", -- Country of ticket buyer
"buyer_region", -- State of ticket buyer
"card_type", -- Credit card type used for purchase
"performance_month", -- Month of the event
"tickets_in_order", -- Number of tickets purchased/ordered
"ticketleap_fees_paid_by_seller", -- Convenience fee paid by seller (City) for each paid credit card transaction done at the ice rink
"venue_postal_code", -- Zip code where event is located
"purchase_point", -- Where the ticket was purchased
"ticket_type", -- Type of ticket sold
"billing_postal_code", -- Zip code of billing location
"order_method", -- Type of system ticket was purchased from
"venue_country", -- Country where event took place
"venue_region", -- Name of state where event is located
"performance_year", -- Year of the event
"redeemed_year", -- Redemption year of ticket purchased online
"referrer", -- How was the order referred, if known
"event", -- Name of event for each transaction
"redeemed_month", -- Redemption month of ticket purchased online
"redeemed", -- Redemption of printed tickets that were purchased online (yes). Tickets purchased onsite would have a no response.
"redemption_method" -- Device used to redeem (scan) an online ticket.
FROM
"citydata-mesaaz-gov/ticket-sales-ice-rink-at-city-plaza-bca4-y9b7:latest"."ticket_sales_ice_rink_at_city_plaza"
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 citydata-mesaaz-gov/ticket-sales-ice-rink-at-city-plaza-bca4-y9b7 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 citydata-mesaaz-gov/ticket-sales-ice-rink-at-city-plaza-bca4-y9b7: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 citydata-mesaaz-gov/ticket-sales-ice-rink-at-city-plaza-bca4-y9b7Checkout 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 citydata-mesaaz-gov/ticket-sales-ice-rink-at-city-plaza-bca4-y9b7:latestThis will download all the objects for the latest tag of citydata-mesaaz-gov/ticket-sales-ice-rink-at-city-plaza-bca4-y9b7 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 citydata-mesaaz-gov/ticket-sales-ice-rink-at-city-plaza-bca4-y9b7: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 citydata-mesaaz-gov/ticket-sales-ice-rink-at-city-plaza-bca4-y9b7: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, citydata-mesaaz-gov/ticket-sales-ice-rink-at-city-plaza-bca4-y9b7 is just another Postgres schema.