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 mta_subsidies_beginning_2019 table in this repository, by referencing it like:
"ny-gov/mta-subsidies-beginning-2019-dfg9-c5rx:latest"."mta_subsidies_beginning_2019"or in a full query, like:
SELECT
":id", -- Socrata column ID
"account_subcategory_1", -- This field is the second lowest level of account detail in this dataset. This field will not always have a value.
"type", -- This field separates subsidy accounts that are Total Dedicated Taxes & State and Local Subsidies and Less: Inter-agency Subsidy Transactions.
"subtype", -- This field is a further breakdown of subsidy categories. It breaks subsidies down into three categories (Subtotal: Taxes & State and Local Subsidies, Other Funding Agreements, B&T Operating Surplus Transfer). This field is blank for rows with the type Less: Inter-agency Subsidy Transactions.
"account_category", -- This field is the third lowest level of account detail in this dataset. This field will not always have a value.
"account_subcategory_2", -- This field is the lowest level of account detail in this dataset. This field will not always have a value.
"general_ledger", -- The general ledger field aggregates the chart of accounts into meaningful categories that are consistently published monthly by the MTA in its Accrual Statement of Operations. This field is blank for the Subtype B&T Operating Surplus Transfer, and for Type Less: Inter-agency Subsidy Transactions.
"agency", -- This is the agency for the subsidy. This can be one of: NYC Transit (NYCT), Metro-North Railroad (MNR), Bridges & Tunnels (BT), Headquarters (MTAHQ), Staten Island Railway (SIR), Commuter Railroads (CRR), and MTA Bus Company (MTABC)
"expense_type", -- Non-reimbursable (NREIMB) or Cash. The NREIMB expense type is equivalent to subsidies on an accrual basis.
"fiscal_year", -- The Fiscal Year of the data (i.e. 2023, 2024)
"scenario", -- The type of budget scenario, such as whether the data is actuals (Actual) or budgeted (Adopted Budget, July Plan, November Plan)
"amount", -- Can be a decimal or negative (for transfers within the agency), in dollars
"month", -- The month of the data, rounded up to the first day of the month. For example, data for January 2023 will appear under 2023-01-01
"financial_plan_year" -- The year the budget scenario was published. For actuals, Financial Plan year will always equal the fiscal year.
FROM
"ny-gov/mta-subsidies-beginning-2019-dfg9-c5rx:latest"."mta_subsidies_beginning_2019"
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/mta-subsidies-beginning-2019-dfg9-c5rx 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/mta-subsidies-beginning-2019-dfg9-c5rx: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/mta-subsidies-beginning-2019-dfg9-c5rxCheckout 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/mta-subsidies-beginning-2019-dfg9-c5rx:latestThis will download all the objects for the latest tag of ny-gov/mta-subsidies-beginning-2019-dfg9-c5rx 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/mta-subsidies-beginning-2019-dfg9-c5rx: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/mta-subsidies-beginning-2019-dfg9-c5rx: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/mta-subsidies-beginning-2019-dfg9-c5rx is just another Postgres schema.