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 all_statewide_energy_storage_projects table in this repository, by referencing it like:
"ny-gov/all-statewide-energy-storage-projects-hspb-4n4p:latest"."all_statewide_energy_storage_projects"or in a full query, like:
SELECT
":id", -- Socrata column ID
"data_through_date", -- The date the dataset was refreshed
"georeference", -- Open Data platform-generated geocoding information from supplied address components. Point-type location is the centroid of the address components provided and does not reflect a specific address if the street address component is not provided. Point-type location is supplied in "POINT (<geocoded longitude> <geocoded latitude>)" format.
"metering_method", -- Metering method used; either CDG (Community Distributed Generation), FIT (Feed-in-Tariff), RNM (Remote Net Metering), RC (Remote Crediting) NM (Net Metering), RFP (utility Request for Proposal), or Whole (Wholesale Market Compensation) . Blank cells represent data that were not required or are not currently available
":@computed_region_wbg7_3whc", -- This column was automatically created in order to record in what polygon from the dataset 'New York Zip Codes' (wbg7-3whc) the point in column 'georeference' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_yamh_8v7k", -- This column was automatically created in order to record in what polygon from the dataset 'NYS Municipal Boundaries' (yamh-8v7k) the point in column 'georeference' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"city_town", -- Name of city for project location
"circuit_id", -- The utility distribution circuit to which the PV project is interconnected. Blank cells represent data that were not required or are not currently available
"utility", -- Name of electric utility for project location
"interconnection_date", -- The date on which the utility company approved the project for final grid interconnection
"county", -- Name of county for project location
"developer", -- The party which applied for interconnection approval with the distribution utility company. Blank cells represent data that were not required or are not currently available
"project_id", -- Unique identifier for project. Not provided for bulk projects in the NYISO inventory
"zip_code", -- ZIP code for project location. Blank cells represent data that are not currently available
"energy_storage_system_size", -- Alternating current storage capacity measured in kilowatts.
"division", -- Name of utility division. Blank cells represent data that were not required or are not currently available
":@computed_region_kjdx_g34t", -- This column was automatically created in order to record in what polygon from the dataset 'Counties' (kjdx-g34t) the point in column 'georeference' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"storage_type", -- The type of energy storage technology; either Battery, Flywheel, or Pumped Hydro. Blank cells represent data that were not required or are not currently available, and are most likely battery energy storage
"substation" -- Name of utility division substation. Blank cells represent data that were not required or are not currently available
FROM
"ny-gov/all-statewide-energy-storage-projects-hspb-4n4p:latest"."all_statewide_energy_storage_projects"
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/all-statewide-energy-storage-projects-hspb-4n4p 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/all-statewide-energy-storage-projects-hspb-4n4p: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/all-statewide-energy-storage-projects-hspb-4n4pCheckout 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/all-statewide-energy-storage-projects-hspb-4n4p:latestThis will download all the objects for the latest tag of ny-gov/all-statewide-energy-storage-projects-hspb-4n4p 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/all-statewide-energy-storage-projects-hspb-4n4p: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/all-statewide-energy-storage-projects-hspb-4n4p: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/all-statewide-energy-storage-projects-hspb-4n4p is just another Postgres schema.