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 open_budget_revenues table in this repository, by referencing it like:
"citydata-mesaaz-gov/open-budget-revenues-69ew-t3nx:latest"."open_budget_revenues"or in a full query, like:
SELECT
":id", -- Socrata column ID
"department_name", -- The department that benefited from the transaction.
"fund_category", -- A higher-level grouping of independent fiscal and accounting entity with a self-balancing set of accounts recording cash and/or other resources, together with all related liabilities, for the purpose of carrying on specific activities or attaining certain objectives in accordance with special regulations. Examples include Enterprise and Restricted Funds.
"core_business_process", -- The number assigned to a collection of related programs and services that are designed and managed to achieve a common outcome or set of outcomes.
"fiscal_year", -- A 12-month period of time to which the Annual Budget applies and at the end of which, a governmental unit determines its financial position and the results of its operations. For the City of Mesa, the fiscal year is July 1 through June 30.
"year_end_estimate", -- A projection of where the City will financially end for the fiscal year.
"service_level", -- The number assigned to a group of core business processes related by a common purpose (mission, outcomes, and expected performance). This level of structure is optional depending on departmental needs.
"fund", -- The number assigned to the governmental segment that benefited from the transaction.
"business_objective_name", -- A major line of business that aligns with one or more of the Council's strategic initiatives; denotes a primary public purpose; and defines where the City allocates its resources. Examples include Fleet Services and Economic Development.
"service_level_name", -- A group of core business processes related by a common purpose (mission, outcomes, and expected performance). This level of structure is optional depending on departmental needs. Examples include Operations Bureau and Investigations Bureau.
"core_business_process_name", -- A collection of related programs and services that are designed and managed to achieve a common outcome or set of outcomes. These include Patrol, Metro, and Criminal Investigations.
"activity", -- The number assigned to the activity the department was engaged in that led to the transaction.
"revenue_object_category", -- The number assigned to the category of revenue on the chart of accounts.
"revenue_object_category_name", -- The grouping of revenue types into categories on the chart of accounts. Category examples include Taxes and Intergovernmental.
"revenue_object_code", -- The number assigned to the type of revenue being paid. In the private sector, this would be called a general ledger account number, however in the government sector, these are most often referred to as object codes.
"adopted_budget", -- Represents the budget as approved by formal action of the City Council which sets the spending limits for the fiscal year.
"actual_revenues", -- The transactions amount.
"department", -- The number assigned to the department that benefited from the transaction.
"row_id", -- Unique row identifier
"amended_budget", -- Represents a placeholder for amendments to the adopted budget to align with the Open Expenditures dataset. Will always show 0 in the amount.
"fund_name", -- The governmental segment that benefited from the transaction. Examples include Cemetery, Transit and Arts and Culture.
"business_objective", -- The number assigned to a major line of business that aligns with one or more of the Council's strategic initiatives; denotes a primary public purpose; and defines where the City allocates its resources.
"fiscal_year_date", -- A date representation of the Fiscal Year
"appropriation", -- The number assigned to the type of appropriation of the transaction.
"activity_name", -- The activity that the department was engaged in that led to the transaction. Activities are generally recurring activities such as accounting, inspections or planning.
"revenue_object_code_name", -- The type of revenue being paid. In the private sector, this would be called a general ledger account, however in the government sector, these are most often referred to as objects. Examples include City Sales Tax and Federal Grants.
"appropriation_name" -- The type of appropriation of the transaction. These will either be the base operating budget or a capital improvement project.
FROM
"citydata-mesaaz-gov/open-budget-revenues-69ew-t3nx:latest"."open_budget_revenues"
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 citydata-mesaaz-gov/open-budget-revenues-69ew-t3nx 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 citydata-mesaaz-gov/open-budget-revenues-69ew-t3nx: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 citydata-mesaaz-gov/open-budget-revenues-69ew-t3nxCheckout 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 citydata-mesaaz-gov/open-budget-revenues-69ew-t3nx:latestThis will download all the objects for the latest tag of citydata-mesaaz-gov/open-budget-revenues-69ew-t3nx 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 citydata-mesaaz-gov/open-budget-revenues-69ew-t3nx: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 citydata-mesaaz-gov/open-budget-revenues-69ew-t3nx: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, citydata-mesaaz-gov/open-budget-revenues-69ew-t3nx is just another Postgres schema.