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 employment_wages_in_colorado table in this repository, by referencing it like:
"colorado-gov/employment-wages-in-colorado-busm-qa5b:latest"."employment_wages_in_colorado"or in a full query, like:
SELECT
":id", -- Socrata column ID
"panelcode", -- Reference panel code (yyyymm)
"wpctrelerr", -- Relative percent error on wage.
"udrnglopct", -- Low percentile for user defined range.
"pct90", -- Wage at ninetieth percentile.
"pct75", -- Wage at seventy-fifth percentile.
"median", -- Median wage of the occupation; also the wage at fiftieth percentile.
"pct25", -- Wage at twenty-fifth percentile.
"pct10", -- Wage at tenth percentile.
"experience", -- Experienced level wage for the occupation, mean of upper two thirds (ALC definition).
"entrywg", -- Entry level wage for the occupation, mean of the first third (ALC definition).
"mean", -- Mean wage for the occupation.
"response", -- Response rate for the occupation’s actual or real survey data. Does NOT include imputed data in the rate calculation.
"empcount", -- Total employment.
"ratetydesc", -- A description of the type of wage rate.
"ratetype", -- Code which identifies the type of wage rate.
"wagesource", -- A code representing the source of the wage data.
"codetitle", -- The descriptive title for this occupation or training code.
"occodetype", -- Code describing the type of occupational coding system.
"indcode", -- A code used in the classification of establishments by type of activity in which they are engaged. For codes not 6 characters long, left justify and blank (ASCII 32) fill. Either SIC or NAICS code can be used. A siccode of 9999 means non-classifiable; industry not specified.
"indcodty", -- Code describing the industry code type.
"period", -- Period code. Will be set to ‘00’ where periodtype is annual.
"periodtype", -- Code describing type of period (e.g. Annual, quarterly, monthly, etc.).
"periodyear", -- Character representation of calendar-year (e.g. 2000).
"area", -- Six-digit code assigned to represent a geographic area. Front fill with zeroes.
"areatyname", -- Descriptive title of the areatype.
"areatype", -- Code describing type of geographic area: e.g. county, service delivery area, MSA.
"areaname", -- Geographic area name.
"stfips", -- State FIPS code.
"statename", -- State name.
"stateabbrv", -- The two letter state abbreviation.
"udpctwage", -- Wage at user defined percentile.
"udpct", -- User defined percentile.
"udrnghipct", -- High percentile for user defined range.
"udrngmean", -- Mean wage for user defined range.
"epctrelerr", -- Relative percent error on employment.
"wagesrdesc", -- A description of the source of the wage data.
"occcode", -- The occupational classification code used by the state for this data element. This code could be a DOT, OES, SOC, CENSUS, etc. For codes not 10 characters long, left justify and blank (ASCII 32) fill.
"occodetydesc", -- Title of classification Code.
"indcodetitle", -- The descriptive title for this industry code.
"pertypdesc" -- A description of the period type.
FROM
"colorado-gov/employment-wages-in-colorado-busm-qa5b:latest"."employment_wages_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/employment-wages-in-colorado-busm-qa5b 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/employment-wages-in-colorado-busm-qa5b: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/employment-wages-in-colorado-busm-qa5bCheckout 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/employment-wages-in-colorado-busm-qa5b:latestThis will download all the objects for the latest tag of colorado-gov/employment-wages-in-colorado-busm-qa5b 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/employment-wages-in-colorado-busm-qa5b: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/employment-wages-in-colorado-busm-qa5b: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/employment-wages-in-colorado-busm-qa5b is just another Postgres schema.