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 hours_worked_by_employees_in_colorado table in this repository, by referencing it like:
"colorado-gov/hours-worked-by-employees-in-colorado-pt2g-89wc:latest"."hours_worked_by_employees_in_colorado"or in a full query, like:
SELECT
":id", -- Socrata column ID
"stateabbrv", -- The two letter state abbreviation.
"statename", -- State name.
"stfips", -- Field Description: State FIPS code.
"areatyname", -- Descriptive title of the areatype.
"areaname", -- Geographic area name.
"area", -- Six-digit code assigned to represent a geographic area. Front fill with zeroes.
"periodyear", -- Character representation of calendar-year (e.g. 2000).
"periodtype", -- Code describing type of period (e.g. Annual, quarterly, monthly, etc.).
"pertypdesc", -- A description of the period type.
"period", -- Period code.
"seriescode", -- Industrial Series code.
"seriesttls", -- Short title used to identify industry division.
"seriesdesc", -- Description of the industries comprising the series.
"adjusted", -- Indicates if record contains seasonally adjusted data. 0 = not adjusted, 1 = adjusted
"benchmark", -- Benchmark-year of the data.
"prelim", -- Preliminary/revised flag 0 = Not Preliminary, 1 = Preliminary
"empces", -- Number of all workers employed by place of work; actual number, not in thousands.
"empprodwrk", -- Number of Production workers employed; actual number, not in thousands.
"empfemale", -- Number of female workers employed; actual number, not in thousands.
"hours", -- Average hours worked per week for production workers.
"earnings", -- Average weekly earnings for production workers.
"hourearn", -- Average hourly earnings for production workers.
"supprecord", -- Suppress total record. 0 = Not Suppressed, 1 = Suppressed
"supphe", -- Suppress hours and earnings for production workers. 0 = Not Suppressed, 1 = Suppressed
"supppw", -- Suppress Production Workers employed. 0 = Not Suppressed, 1 = Suppressed
"suppfem", -- Suppress Female workers employed. 0 = Not Suppressed, 1 = Suppressed
"hoursallwrkr", -- Average hours worked per week for all workers.
"earningsallwrkr", -- Average weekly earnings for all workers.
"hourearnallwrkr", -- Average hourly earnings for all workers.
"suppheallwrkr" -- Suppress hours and earnings for all workers. 0 = Not Suppressed, 1 = Suppressed
FROM
"colorado-gov/hours-worked-by-employees-in-colorado-pt2g-89wc:latest"."hours_worked_by_employees_in_colorado"
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 colorado-gov/hours-worked-by-employees-in-colorado-pt2g-89wc 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 colorado-gov/hours-worked-by-employees-in-colorado-pt2g-89wc: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 colorado-gov/hours-worked-by-employees-in-colorado-pt2g-89wcCheckout 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 colorado-gov/hours-worked-by-employees-in-colorado-pt2g-89wc:latestThis will download all the objects for the latest tag of colorado-gov/hours-worked-by-employees-in-colorado-pt2g-89wc 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 colorado-gov/hours-worked-by-employees-in-colorado-pt2g-89wc: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 colorado-gov/hours-worked-by-employees-in-colorado-pt2g-89wc: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, colorado-gov/hours-worked-by-employees-in-colorado-pt2g-89wc is just another Postgres schema.