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 population_by_gender_and_age_us_census_acs_5_year table in this repository, by referencing it like:
"pa-gov/population-by-gender-and-age-us-census-acs-5-year-ib65-r5ts:latest"."population_by_gender_and_age_us_census_acs_5_year"or in a full query, like:
SELECT
":id", -- Socrata column ID
"female_pop_35to39", -- The female population age 35 to 39
"female_pop_50to54", -- The female population age 50 to 54
"female_pop_55to59", -- The female population age 55 to 59
"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.
"female_pop_60to61", -- The female population age 60 and 61
"female_pop_62to64", -- The female population age 62 to 64
"female_pop_67to69", -- The female population age 67 to 69
"female_pop_70to74", -- The female population age 70 to 74
"female_pop_80to84", -- The female population age 80 to 84
"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
"female_pop_21", -- The female population age 21
"female_pop_20", -- The female population age 20
"female_pop_18to19", -- The female population age 18 and 19
"female_pop_15to17", -- The female population age 15 to 17
"female_pop_10to14", -- The female population age 10 to 14
"female_pop_5to9", -- The female population age 5 to 9
"female_pop", -- The total female population
"male_pop_70to74", -- The male population age 70 to 74
"male_pop_62to64", -- The male population age 62 to 64
"male_pop_60to61", -- The male population age 60 and 61
"male_pop_55to59", -- The male population age 55 to 59
"male_pop_45to49", -- The male population age 45 to 49
"male_pop_35to39", -- The male population age 35 to 39
"male_pop_22to24", -- The male population age 22 to 24
"male_pop_21", -- The male population age 21
"male_pop_20", -- The male population age 20
"male_pop_under5", -- The male population under 5 years of age
"population", -- The total population
"county_name", -- This is the name of the Pennsylvania County. Pennsylvania has 67 counties.
"year", -- The end year of the 5-year period of the estimate. Ex. 2019 refers to the years 2015 - 2019.
"male_pop_40to44", -- The male population age 40 to 44
"female_pop_85over", -- The female population age 85 and over
"female_pop_75to79", -- The female population age 75 to 79
"female_pop_65to66", -- The female population age 65 and 66
"male_pop_50to54", -- The male population age 50 to 54
"male_pop_10to14", -- The male population age 10 to 14
"male_pop", -- The total male population
"male_pop_85over", -- The male population age 85 and over
"male_pop_75to79", -- The male population age 75 to 79
"male_pop_65to66", -- The male population age 65 and 66
"female_pop_45to49", -- The female population age 45 to 49
"female_pop_30to34", -- The female population age 30 to 34
"female_pop_under5", -- The female population age under 5
"male_pop_80to84", -- The male population age 80 to 84
"male_pop_67to69", -- The male population age 67 to 69
"male_pop_25to29", -- The male population age 25 to 29
"male_pop_5to9", -- The male population age 5 to 9
"male_pop_18to19", -- The male population age 18 and 19
"male_pop_15to17", -- The male population age 15 to 17
"male_pop_30to34", -- The male population age 30 to 34
"female_pop_22to24", -- The female population age 22 to 24
"female_pop_25to29", -- The female population age 25 to 29
"female_pop_40to44" -- The female population age 40 to 44
FROM
"pa-gov/population-by-gender-and-age-us-census-acs-5-year-ib65-r5ts:latest"."population_by_gender_and_age_us_census_acs_5_year"
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/population-by-gender-and-age-us-census-acs-5-year-ib65-r5ts with SQL in under 60 seconds.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.pa.gov. When you querypa-gov/population-by-gender-and-age-us-census-acs-5-year-ib65-r5ts:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).
We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.
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 (like this repository), 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, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Mounting Data
This repository is an external repository. It's not hosted by Splitgraph. It is hosted by data.pa.gov, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.
First, install Splitgraph if you haven't already.
Mount the table with sgr mount
sgr mount socrata \
"pa-gov/population-by-gender-and-age-us-census-acs-5-year-ib65-r5ts" \
--handler-options '{
"domain": "data.pa.gov",
"tables": {
"population_by_gender_and_age_us_census_acs_5_year": "ib65-r5ts"
}
}'That's it! Now you can query the data in the mounted table like any other Postgres table.
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/population-by-gender-and-age-us-census-acs-5-year-ib65-r5ts is just another Postgres schema.