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 occupational_wages_2018_labor_and_industry table in this repository, by referencing it like:
"pa-gov/occupational-wages-2018-labor-and-industry-mdvn-squs:latest"."occupational_wages_2018_labor_and_industry"or in a full query, like:
SELECT
":id", -- Socrata column ID
"average_annual_wage", -- Average wage in this publication refers to the mean wage. Wage data in the OES program are collected and grouped in 12 intervals. The number of employees in an occupation that are paid at each wage interval is multiplied by the mid-point of the interval. These products are then summed and the sum is divided by total employment for the occupation to obtain a mean hourly wage for the occupation. Hourly wages are converted to annual wages by multiplying by 2,080 hours.
"wage_period", -- Identifies the most recent period of data included in the wage calculation. Each semi-annual panel represents one-sixth of the sample for the full three-year sample. Utilizing three years of data significantly reduces sampling error, but requires the adjustment of the earlier two years of wage data to the current time period using the national Employment Cost Index or ECI.
"county_fips", -- The FIPS county code is a five-digit Federal Information Processing Standard (FIPS) code (FIPS 6-4) which uniquely identifies counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. This is the 3-digit part of the 5-digit county FIPS code specifically standing for the county.
"wage_type", -- Indicates the geographical level for which the occupational wage is provided. When county wages are not available, the wage for the smallest available geographic area that includes the county is substituted. Wage Types are County (CTY), Workforce Development Area (WDA), Metropolitan Statistical Area (MSA) or Pennsylvania (PA).
"wage_area", -- Name of the area indicated in Wage Type.
"soc", -- Occupations are classified based on the revised national Standard Occupational Classification (SOC) system. The revised SOC was developed in response to a growing need for a universal occupational classification system. The system, which is designed to cover all occupations in which work is performed for pay or profit, reflects the current occupational structure in the US. It is used by all federal agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. Occupations are combined to form major and minor groups requiring similar job duties, skills, education, or experience.
"soc_title", -- The Standard Occupational Classification (SOC) Title conveys in brief the occupations represented by the SOC code.
"experienced_annual_wage", -- The mean of the upper two-thirds of the wages for an occupation. This calculation is provided as a proxy for an experienced-level wage.
"area_name", -- The English name for Pennsylvania state or the appropriate Pennsylvania County for that row.
"county_code", -- Two-digit county code includes the leading zeroes. There are 67 counties in Pennsylvania.
"entry_annual_wage", -- The mean of the lower-third of the wages for an occupation. This calculation is provided as a proxy for an entry-level wage.
"state_fips" -- These are the first 2 digits of the 5-digit Federal Information Processing Standard (FIPS) code that designate the State association. Each State has its own 2-digit number and each County within the state has its own 3-digit number which are combined into a 5-digit number to uniquely identify every US county. For more technical details : Federal Information Processing Standards Publications (FIPS PUBS) are issued by the National Institute of Standards and Technology (NIST) after approval by the Secretary of Commerce pursuant to Section 111 (d) of the Federal Property and Administrative Services Act of 1949 as amended by the Computer Security Act of 1987, Public Law 100-235. Federal Information Processing Standard (FIPS) 6-4, Counties and Equivalent Entities of the U.S., Its Possessions, and Associated Areas -- 90 Aug 31 , provides the names and codes that represent the counties and other entities treated as equivalent legal and/or statistical subdivisions of the 50 States, the District of Columbia, and the possessions and freely associated areas of the United States. Counties are considered to be the "first-order subdivisions" of each State and statistically equivalent entity, regardless of their local designations (county, parish, borough, etc.). Information gathered from census data - https://www.census.gov/library/reference/code-lists/ansi.html
FROM
"pa-gov/occupational-wages-2018-labor-and-industry-mdvn-squs:latest"."occupational_wages_2018_labor_and_industry"
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 pa-gov/occupational-wages-2018-labor-and-industry-mdvn-squs 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 pa-gov/occupational-wages-2018-labor-and-industry-mdvn-squs: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 pa-gov/occupational-wages-2018-labor-and-industry-mdvn-squsCheckout 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 pa-gov/occupational-wages-2018-labor-and-industry-mdvn-squs:latestThis will download all the objects for the latest tag of pa-gov/occupational-wages-2018-labor-and-industry-mdvn-squs 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 pa-gov/occupational-wages-2018-labor-and-industry-mdvn-squs: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 pa-gov/occupational-wages-2018-labor-and-industry-mdvn-squs: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, pa-gov/occupational-wages-2018-labor-and-industry-mdvn-squs is just another Postgres schema.