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 employee_payroll_data_fmps_payroll_costing table in this repository, by referencing it like:
"cityofchicago/employee-payroll-data-fmps-payroll-costing-dawh-m56b:latest"."employee_payroll_data_fmps_payroll_costing"or in a full query, like:
SELECT
":id", -- Socrata column ID
"department", -- Code and description associated with the City department as designated in the Budget Ordinance and aligning with the City's financial management systems.
"payroll_year", -- This is the year associated with the Payroll Period. This is in alignment with the Payroll Costing module of FMPS.
"department_function", -- Department Function is defined by Department (Code) and aligns with the Budget Ordinance (specifically Summary E).
"employee", -- This field combines Employee Dataset ID (CODE) and Employee (Name) to enhance usability of this dataset.
"amount", -- Amounts include Debits and Credits, and may include negative dollar amounts.
"title_code", -- Code associated with the job title as designated in the Budget Ordinance and aligning with the City's financial management systems.
"pay_element", -- Pay Element is a function of the CIty's FMPS Payroll Costing module and a part of the financial process. This object is not defined in the City's Budget Ordinance.
"title", -- Code and description associated with employee's job title as designated in the Budget Ordinance and aligning with the City's financial management system.
"payroll_period", -- Payroll Period as a Number (1-24) for improved dataset usability.
"department_code", -- Code associated with the City department as designated in the Budget Ordinance and aligning with the City's financial management systems.
"fund_code", -- Code associated with the Fund as designated in the Budget Ordinance and aligning with the City's financial management systems.
"fund_type", -- Fund Type is associated with the Fund Code, as designated in the Budget Ordinance (specifically Summary E) and aligning with the City's financial management systems.
"employee_dataset_id", -- This Employee Dataset identifier has been created exclusively for this dataset. This Employee ID does not match any official city system (such as FMPS, CHIPPS).
"appropriation_code", -- Code associated with the Appropriation as designated in the Budget Ordinance and aligning with the City's financial management systems.
"appropriation", -- Code and description associated with the Appropriation as designated in the Budget Ordinance and aligning with the City's financial management systems.
"fund" -- Code and description associated with the Fund as designated in the Budget Ordinance and aligning with the City's financial management systems.
FROM
"cityofchicago/employee-payroll-data-fmps-payroll-costing-dawh-m56b:latest"."employee_payroll_data_fmps_payroll_costing"
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 cityofchicago/employee-payroll-data-fmps-payroll-costing-dawh-m56b 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 cityofchicago/employee-payroll-data-fmps-payroll-costing-dawh-m56b: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 cityofchicago/employee-payroll-data-fmps-payroll-costing-dawh-m56bCheckout 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 cityofchicago/employee-payroll-data-fmps-payroll-costing-dawh-m56b:latestThis will download all the objects for the latest tag of cityofchicago/employee-payroll-data-fmps-payroll-costing-dawh-m56b 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 cityofchicago/employee-payroll-data-fmps-payroll-costing-dawh-m56b: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 cityofchicago/employee-payroll-data-fmps-payroll-costing-dawh-m56b: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, cityofchicago/employee-payroll-data-fmps-payroll-costing-dawh-m56b is just another Postgres schema.