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 citizen_statewide_lake_monitoring_assessment table in this repository, by referencing it like:
"ny-gov/citizen-statewide-lake-monitoring-assessment-b6x3-58ht:latest"."citizen_statewide_lake_monitoring_assessment"or in a full query, like:
SELECT
":id", -- Socrata column ID
"lake_name", -- Name of lake
"last_year_sampled", -- The last year in which the lake was sampled by CSLAP volunteers
"area_acres", -- The area of lake in acres
"depth_feet", -- Mean lake depth in feet
"sampling_years", -- Years that the lake was sampled by CSLAP volunteers
"trophic_state", -- A ranking of nutrient enrichment status of a lake where: oligotrophic = nutrient poor and low productivity; mesotrophic = moderately productive; and eutrophic = very productive and fertile; mesoligotrophic = between oligotrophic and mesotrophic; mesoeutrophic = between mesotrophic and eutrophic
"longitude", -- Longitude in decimal degrees
"latitude", -- Latitude in decimal degrees
"mean_depth_meters", -- Mean lake depth in meters
"town", -- The town in which the sampling location on the lake is in.
"dec_region", -- NYSDEC Region where facility is located: Region 1: (Long Island) Nassau and Suffolk counties; Region 2: (New York City) Brooklyn, Bronx, Manhattan, Queens and Staten Island; Region 3: (Lower Hudson Valley) Dutchess, Orange, Putnam, Rockland, Sullivan, Ulster and Westchester counties; Region 4: (Capital Region/Northern Catskills) Albany, Columbia, Delaware, Greene, Montgomery, Otsego, Rensselaer, Schenectady and Schoharie counties; Region 5: (Eastern Adirondacks/Lake Champlain) Clinton, Essex, Franklin, Fulton, Hamilton, Saratoga, Warren and Washington counties; Region 6: (Western Adirondacks/Eastern Lake Ontario) Herkimer, Jefferson, Lewis, Oneida and St. Lawrence counties; Region 7: (Central New York) Broome, Cayuga, Chenango, Cortland, Madison, Onondaga, Oswego, Tioga and Tompkins counties; Region 8: (Western Finger Lakes) Chemung, Genesee, Livingston, Monroe, Ontario, Orleans, Schuyler, Seneca, Steuben, Wayne and Yates counties; Region 9: (Western New York) Allegany, Chautauqua, Cattaraugus, Erie, Niagara and Wyoming counties.
"area_hectares", -- Area in hectares
"waterbody_index_number", -- A unique string used to identify the lake within New York State Regulations
"cslap_number", -- A unique number used to identify each program lake
"georeference", -- Open Data/Socrata-generated geocoding information from supplied address components.
"water_quality_classification", -- The lakes designated best usage classification. A,AA, AASpec = Drinking water supply; B = Public Bathing; C = Recreation; (T) trout survival; (TS) trout propagation.
"access_yes_no", -- Indicates if public access is available on lake
"bathymetric_map_available_yes_no", -- Indicates if a bathymetric map is available for the lake
"public_private_access", -- Description of the access type to the lake
"public_access", -- Description of public access to the lake
"priority_waterbody_list_number", -- Priority waterbody listing identification number. Blanks indicate lakes without an assigned priority waterbody list number.
"number_of_years_sampled", -- Number of years the lake has been sampled by CSLAP volunteers
"watershed_name", -- The major drainage basin within which the lake is located.
"watershed_area_hectares", -- Lake watershed area in hectares
"county", -- The county in which the sampling location on the lake is in.
"elevation_meters" -- Elevation in meters above sea level
FROM
"ny-gov/citizen-statewide-lake-monitoring-assessment-b6x3-58ht:latest"."citizen_statewide_lake_monitoring_assessment"
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 ny-gov/citizen-statewide-lake-monitoring-assessment-b6x3-58ht 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 ny-gov/citizen-statewide-lake-monitoring-assessment-b6x3-58ht: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 ny-gov/citizen-statewide-lake-monitoring-assessment-b6x3-58htCheckout 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 ny-gov/citizen-statewide-lake-monitoring-assessment-b6x3-58ht:latestThis will download all the objects for the latest tag of ny-gov/citizen-statewide-lake-monitoring-assessment-b6x3-58ht 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 ny-gov/citizen-statewide-lake-monitoring-assessment-b6x3-58ht: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 ny-gov/citizen-statewide-lake-monitoring-assessment-b6x3-58ht: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, ny-gov/citizen-statewide-lake-monitoring-assessment-b6x3-58ht is just another Postgres schema.