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 financial_services_payroll_run_times table in this repository, by referencing it like:
"citydata-mesaaz-gov/financial-services-payroll-run-times-vf2j-h8vy:latest"."financial_services_payroll_run_times"or in a full query, like:
SELECT
":id", -- Socrata column ID
"ach_activation_time", -- The date/time associated with job that sends the payroll ACH file to the bank
"kronos_runtime_minutes", -- Number of hours (fractional ex. 2.5 = 2 and half hours) the job ran that imports time cards into the payroll system
"run_start_timestamp", -- Timestamp the payroll deposit processing job is initiated
"job_name", -- Name of the job running in AWS
"combined_ach_end_datetime_rund_end_timestamp_data", -- Date derived from first - ACH End Datetime if available. If ACH End Datetime is blank, then the value is derived from the Run End Timestamp column. Otherwise, it defaults back to the ACH End Datetime column
"rowid", -- Unique Row Identifier
"kronos_job_runtime", -- How long (in seconds) the job ran that imports timecards for the previous pay period into payroll system
"run_end_timestamp", -- Time the payroll deposit processing job ends
"agent_id", -- Unique identifier of the instance of the job running
"kronos_job_status", -- Status of the job that imports timecards for the previous pay period into payroll system
"before_5pm_flag", -- Payroll file created before 5PM Flag (used in performance measure calculation)
"ret_cd", -- Return Code. The code that the job sends back once it is completed, indicating whether it completed successfully or with errors
"agent_name", -- Name of the bot/job running the job
"ach_job_termination", -- Termination code of the job that sends the payroll ACH file to the bank
"run_time_seconds", -- Timestamp the payroll deposit processing job ends
"run_year", -- Calendar year the job ran
"ach_status", -- Status of the job that sends the payroll ACH file to the bank
"ach_runtime", -- How long (in seconds) the job ran that sends the payroll ACH file to the bank
"run_month", -- Month the job ran
"employee_termination_count", -- Number of employees terminated (retirement, resignation, etc.) during the payroll period
"run_datetime", -- Time the payroll deposit processing job is initiated
"denominator", -- Denominator (used in performance measure calculation)
"mmo", -- Job result
"kronos_job_start_datetime", -- Date / time the job started that imports timecards for the previous pay period into payroll system
"ach_end_datetime", -- Date/time of the job ends that sends the payroll ACH file to the bank
"ach_start_datetime", -- Date/time of the job start that sends the payroll ACH file to the bank
"kronos_job_end_datetime" -- Date / time the job ended that imports timecards for the previous pay period into payroll system
FROM
"citydata-mesaaz-gov/financial-services-payroll-run-times-vf2j-h8vy:latest"."financial_services_payroll_run_times"
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/financial-services-payroll-run-times-vf2j-h8vy 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/financial-services-payroll-run-times-vf2j-h8vy: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/financial-services-payroll-run-times-vf2j-h8vyCheckout 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/financial-services-payroll-run-times-vf2j-h8vy:latestThis will download all the objects for the latest tag of citydata-mesaaz-gov/financial-services-payroll-run-times-vf2j-h8vy 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/financial-services-payroll-run-times-vf2j-h8vy: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/financial-services-payroll-run-times-vf2j-h8vy: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/financial-services-payroll-run-times-vf2j-h8vy is just another Postgres schema.