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 shortterm_employment_projections_in_colorado table in this repository, by referencing it like:
"colorado-gov/shortterm-employment-projections-in-colorado-u2t6-bfhr:latest"."shortterm_employment_projections_in_colorado"or in a full query, like:
SELECT
":id", -- Socrata column ID
"stateabbrv", -- The two letter state abbreviation.
"statename", -- State name.
"stfips", -- State FIPS code.
"areatype", -- Code describing type of geographic area: e.g. county, service delivery area, MSA.
"areatyname", -- Descriptive title of the areatype.
"areaname", -- Geographic area name.
"periodid", -- A 2-digit code identifying the type of period used.
"perioddesc", -- Description of period range.
"periodtype", -- Code describing type of period (e.g. Annual, quarterly, monthly, etc.)
"matincodty", -- A code to identify matincode as SIC code based or NAICS code based; 1 = SIC based, 2 = NAICS based
"matintitle", -- Industry title.
"matoccodty", -- A code to identify matoccode as 9 char and OES code based or 10 char and SOC code based. 1 = OES based, 2 = SOC based
"matocctitl", -- Occupation title.
"estemp", -- The base-year employment estimate.
"projemp", -- The projected-year employment estimate.
"nchg", -- Numeric Change between the projected estimate and the base estimate.
"aopeng", -- A value representing the annual average openings due to growth. The value is calculated by subtracting the Base Year Employment estimate from the Projected Year Employment estimate, then dividing by the number of years in the projection period. (projemp-estemp)/No. years.
"aopenr", -- A value representing the annual average openings due to net replacement. The value is calculated by dividing the total openings due to net replacements (in the projection period) by the number of years in the projection period.
"aopent", -- A value representing the total annual average openings due to growth and net replacements.
"suppress", -- An indicator that the record contains confidential data that must be suppressed for public use: 0 = Not Confidential; 1 = Confidential
"area", -- A 6-digit code assigned to represent a geographic area. Front fill with zeroes.
"matincode", -- Industry code from Micro Matrix.
"matoccode", -- Occupation code from Micro Matrix. For codes not 10 characters long, left justify and blank (ASCII 32) fill.
"pctestind", -- The percentage of projected employment for the indicated industry represented by projected employment for the indicated occupation within that industry.
"pctprojind", -- The percentage of projected employment for the indicated industry represented by projected employment for the indicated occupation within that industry.
"pchg", -- Percent change over period.((projemp-estemp)/estemp)*100
"growrate", -- A value representing the annualized percentage growth. This value is calculated by dividing the Projected year by the Base year. Taking the results to the 1/n power, where n is the number of years in the projection period, subtracting 1 from the result and multiplying that result by 100. Ie. grrate=(((projemp/estemp)^1/n)-1)*100
"pctprojocc", -- The percentage of the projected employment estimate for the indicated occupation represented by the projected employment estimate for the indicated industry within that occupation.
"pctestocc" -- The percentage of the projected employment estimate for the indicated occupation represented by the projected employment estimate for the indicated industry within that occupation.
FROM
"colorado-gov/shortterm-employment-projections-in-colorado-u2t6-bfhr:latest"."shortterm_employment_projections_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/shortterm-employment-projections-in-colorado-u2t6-bfhr 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/shortterm-employment-projections-in-colorado-u2t6-bfhr: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/shortterm-employment-projections-in-colorado-u2t6-bfhrCheckout 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/shortterm-employment-projections-in-colorado-u2t6-bfhr:latestThis will download all the objects for the latest tag of colorado-gov/shortterm-employment-projections-in-colorado-u2t6-bfhr 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/shortterm-employment-projections-in-colorado-u2t6-bfhr: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/shortterm-employment-projections-in-colorado-u2t6-bfhr: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/shortterm-employment-projections-in-colorado-u2t6-bfhr is just another Postgres schema.