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 influenza_laboratoryconfirmed_cases_by_county table in this repository, by referencing it like:
"health-data-ny-gov/influenza-laboratoryconfirmed-cases-by-county-jr8b-6gh6:latest"."influenza_laboratoryconfirmed_cases_by_county"or in a full query, like:
SELECT
":id", -- Socrata column ID
"season", -- Because influenza activity peaks in winter, the influenza season is named for the two calendar years over which a single influenza epidemic spans. CDC defines the influenza season as beginning with week 40 (generally the first week in October) of one calendar year and ending with week 20 of the following calendar year (generally the third week in May).
"county", -- Cases are assigned to a county based on this order of preference: 1) the patient’s address, 2) the ordering healthcare provider’s address, or 3) the ordering facility’s address.
"geocoded_column_state",
"geocoded_column_city",
"fips", -- Federal Information Processing Series (FIPS) geographic codes are assigned to each state and county; a unique county-level geographic code.
"geocoded_column", -- The mapping coordinates (latitude, longitude) for the center of the specified county.
"count", -- The number of laboratory-confirmed influenza cases reported to NYSDOH for the corresponding season, week, disease, and county.
"disease", -- When the laboratory test can discern virus type (type A or type B), cases are counted by the corresponding disease (“Influenza A” or “Influenza B”). When the virus type cannot be differentiated, cases are counted as “Influenza Unspecified”.
"weekendingdate", -- The last date of each CDC week. Each week begins on Sunday and ends on Saturday; week ending dates are always on a Saturday.
"cdcweek", -- CDC designates each week of the year with a sequential number starting with 1 to a maximum of 52 or 53. Week 1 is the first week of the year that has at least four days in the calendar year. CDC defines the influenza season as beginning with week 40 (generally the first week in October) and ending with week 20 of the following calendar year (generally the third week in May). Also known as MMWR week.
"region", -- The five regions in New York are defined by county as Capital District Region counties: Albany, Clinton, Columbia, Delaware, Essex, Franklin, Fulton, Greene, Hamilton, Montgomery, Otsego, Rensselaer, Saratoga, Schenectady, Schoharie, Warren, Washington Central Region counties: Broome, Cayuga, Chenango, Cortland, Herkimer, Jefferson, Lewis, Madison, Oneida, Onondaga, Oswego, St Lawrence, Tioga, Tompkins Metropolitan Region counties: Dutchess, Nassau, Orange, Putnam, Rockland, Suffolk, Sullivan, Ulster, Westchester New York City counties/boroughs: Bronx, Kings, New York, Queens, Richmond Western Region counties: Allegany, Cattaraugus, Chautauqua, Chemung, Erie, Genesee, Livingston, Monroe, Niagara, Ontario, Orleans, Schuyler, Seneca, Steuben, Wayne, Wyoming, Yates
"geocoded_column_address",
"geocoded_column_zip",
":@computed_region_8ire_itmf",
":@computed_region_43an_4dx5",
":@computed_region_9yqb_tdyd",
":@computed_region_assa_msit",
":@computed_region_5edz_4hdv"
FROM
"health-data-ny-gov/influenza-laboratoryconfirmed-cases-by-county-jr8b-6gh6:latest"."influenza_laboratoryconfirmed_cases_by_county"
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 health-data-ny-gov/influenza-laboratoryconfirmed-cases-by-county-jr8b-6gh6 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 health-data-ny-gov/influenza-laboratoryconfirmed-cases-by-county-jr8b-6gh6: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 health-data-ny-gov/influenza-laboratoryconfirmed-cases-by-county-jr8b-6gh6Checkout 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 health-data-ny-gov/influenza-laboratoryconfirmed-cases-by-county-jr8b-6gh6:latestThis will download all the objects for the latest tag of health-data-ny-gov/influenza-laboratoryconfirmed-cases-by-county-jr8b-6gh6 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 health-data-ny-gov/influenza-laboratoryconfirmed-cases-by-county-jr8b-6gh6: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 health-data-ny-gov/influenza-laboratoryconfirmed-cases-by-county-jr8b-6gh6: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, health-data-ny-gov/influenza-laboratoryconfirmed-cases-by-county-jr8b-6gh6 is just another Postgres schema.