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 aerial_waterfowl_survey_data table in this repository, by referencing it like:
"delaware-gov/aerial-waterfowl-survey-data-bxyv-7mgn:latest"."aerial_waterfowl_survey_data"or in a full query, like:
SELECT
":id", -- Socrata column ID
"mute_swan", -- Number of Mute Swan counted.
"common_goldeneye", -- Number of Common Goldeneye counted.
"bufflehead", -- Number of Bufflehead counted.
"white_winged_scoter", -- Number of White-winged Scoter counted.
"red_breasted_merganser", -- Number of Red-breasted Merganser counted.
"northern_shoveler", -- Number of Northern Shoveler counted.
"american_wigeon", -- Number of American Wigeon counted.
"blue_winged_teal", -- Number of Blue-winged Teal counted.
"northern_pintail", -- Number of Northern Pintail counted.
"mallard", -- Number of Mallard counted.
"canada_goose", -- Number of Canada Geese counted
"zone", -- In which of the 11 different survey areas the count was taken. The survey areas are shown at http://www.dnrec.delaware.gov/fw/Hunting/Pages/Waterfowl Surveys.aspx.
"black_scoter", -- Number of Black Scoter counted.
"mergansers", -- Number of Mergansers counted.
"canvasback", -- Number of Canvasback counted.
"gadwall", -- Number of Gadwall counted.
"american_black_duck", -- Number of American Black Duck counted.
"atlantic_brant", -- Number of Atlantic Brant counted
"month", -- Month in which count was taken
"american_coot", -- Number of American Coot counted.
"tundra_swan", -- Number of Tundra Swan counted.
"long_tailed_duck", -- Number of Long-tailed Duck counted.
"ruddy_duck", -- Number of Ruddy Duck counted.
"common_merganser", -- Number of Common Merganser counted.
"american_green_winged_teal", -- Number of American Green-winged Teal counted.
"year", -- Year in which count was taken
"scaup", -- Number of Scaup counted.
"scoters", -- Number of Scoters counted.
"survey_sequence", -- A sequential number to indicate the order the survey was conducted in relation to other surveys. This is used to sort the data in the correct order.
"surf_scoter", -- Number of Surf Scoter counted.
"hooded_merganser", -- Number of Hooded Merganser counted.
"ring_necked_duck", -- Number of Ring-necked Duck counted.
"wood_duck", -- Number of Wood Duck counted.
"redhead", -- Number of Redhead counted.
"time_period", -- For surveys conducted between 2010 and January 2019, when surveys were conducted twice a month, this indicates in which part of the month (early or late) the survey was conducted. Early January surveys are part of the national annual Mid-Winter Survey (MWS).
"snow_goose", -- Number of Snow Geese counted
"greater_white_fronted_goose" -- Number of Greater White-fronted Geese counted.
FROM
"delaware-gov/aerial-waterfowl-survey-data-bxyv-7mgn:latest"."aerial_waterfowl_survey_data"
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 delaware-gov/aerial-waterfowl-survey-data-bxyv-7mgn 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 delaware-gov/aerial-waterfowl-survey-data-bxyv-7mgn: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 delaware-gov/aerial-waterfowl-survey-data-bxyv-7mgnCheckout 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 delaware-gov/aerial-waterfowl-survey-data-bxyv-7mgn:latestThis will download all the objects for the latest tag of delaware-gov/aerial-waterfowl-survey-data-bxyv-7mgn 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 delaware-gov/aerial-waterfowl-survey-data-bxyv-7mgn: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 delaware-gov/aerial-waterfowl-survey-data-bxyv-7mgn: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, delaware-gov/aerial-waterfowl-survey-data-bxyv-7mgn is just another Postgres schema.