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 census_tracts_sf1_in_colorado_2000 table in this repository, by referencing it like:
"colorado-gov/census-tracts-sf1-in-colorado-2000-ibr4-saca:latest"."census_tracts_sf1_in_colorado_2000"or in a full query, like:
SELECT
":id", -- Socrata column ID
"v_seasonal", -- The number of vacant housing units that are for seasonal use
"state", -- The state id number that the designated area is in (Colorado is 08).
"renter", -- The number of houses that are rented by their occupants in the designated area
"pct_white", -- The percentage of people self designated as white (not hispanic).
"pct_own", -- The percentage of houses that are owned by their occupants in the designated area
"pct_other", -- The percentage of people self designated as none of the above.
"pct_occ", -- The percentage of houses occupied in the designated area
"pct_hawpi", -- The percentage of people self designated as hawaiian.
"pct_black", -- The percentage of people self designated as black (not hispanic)
"pct_asian", -- The percentage of people self designated as asian
"pct_amind", -- The percentage of people self designated as native american (not hispanic).
"other_nh", -- Estimate for the population of those identifying with any other race (Non-Hispanic)
"oth_family", -- The number of family households designated as not having a husband and wife.
"occupied", -- The number of houses occupied in the designated area
"non_fam", -- The number of households self designated as not family
"namesf1", -- The name of the designated area (eg Block 1081)
"housing_un", -- Estimated total number of housing units
"hispanic", -- Estimate for the Hispanic Population
"hh_w_nonr", -- The number of households with non-relatives living together
"gq_pop", -- The number in group quarters
"gq_ot_inst", -- The number living in an institutional facility not mentioned above
"gq_nurs", -- The number living in nursing homes
"geoidtr", -- The ID for the designated area, created by combining the state, county, and tract codes (eg 08051963900)
"family_hh", -- The number of households self designated as family
"black_nh", -- Estimate for the Black, Non-Hispanic Population
"avg_hh", -- The average number of people in a household
"asian_nh", -- Estimate for the Asian, Non-Hispanic Population
"ageunder10", -- The number of people younger than 10
"age65plus", -- Estimate for the total population 65 years of age or older
"v_forsale", -- The number of vacant housing units for sale in the designated area
"pct_rent", -- The percentage of houses that are rented by their occupants in the designated area
"gq_o_noni", -- The number living in any non-institutional facility not mentioned above
"gq_correc", -- The number in group quarters living in an adult correctional facility
"hh_n_nonr", -- The number of households with no non-relatives living together
"hh_alone", -- The number of households where the resident is living alone
"husb_wife", -- The number of family households designated as having a husband and wife.
"pct_u10", -- The percentage of people younger than 10
"pct_65plus", -- The percentage of people age 65 and older
"pct_male", -- The percentage of people self designated as males
"pct_mult", -- The percentage of people self designated as multi-ethnic
"mult_nh", -- The number of people self designated as multi-ethnic
"amerind_nh", -- The number of people self designated as native american (not hispanic).
"white_nh", -- Estimate for the White, Non-Hispanic Population
"pct_hisp", -- The percentage of people self designated as hispanic (not white).
"tract", -- The Tract code that the designated area is in (eg 974800)
"county", -- The FIPS County code that the designated area is in (eg 071).
"awater00", -- The amount of land in the designated area, in ft2.
"v_other", -- The number of vacant housing units that are vacant for any reason not mentioned above
"v_migrant", -- The number of vacant housing units for migrant workers
"v_rs_n_occ", -- The number of vacant, rented, but not occupied housing units
"v_forrent", -- The number of vacant houses for rent in the designated area
"owner", -- The number of houses that are owned by their occupants in the designated area
"pct_vac", -- The percentage of houses vacant in the designated area
"vacant", -- The number of houses vacant in the designated area
"gq_college", -- The number living in college housing
"gq_noninst", -- The number in group quarters living in a non-institutional facility
"gq_inst", -- The number in group quarters living in an institutional facility
"avg_fam", -- The average number of people in a family
"households", -- Estimate for the total number of household
"med_female", -- The median age of all self designated females in the designated area
"female", -- Estimate for the total female population
"aland00", -- The amount of water in the designated area, in ft2.
"med_age", -- Median age of the population for the area
"pct_fem", -- The percentage of people self designated as females
"hawpac_nh", -- The number of people self designated as hawaiian.
"pct_u18", -- The percentage of people younger than 18
"name00", -- The name of the designated area
"ageunder18", -- The number of people younger than 18
"male", -- Estimate for the total male population
"med_male", -- The median age of all self designated males in the designated area
"pop2000", -- The number of people in the designated area
"the_geom", -- GeoJSON field describing the boundaries of the area
"gq_miltry" -- The number living in military quarters
FROM
"colorado-gov/census-tracts-sf1-in-colorado-2000-ibr4-saca:latest"."census_tracts_sf1_in_colorado_2000"
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/census-tracts-sf1-in-colorado-2000-ibr4-saca 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/census-tracts-sf1-in-colorado-2000-ibr4-saca: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/census-tracts-sf1-in-colorado-2000-ibr4-sacaCheckout 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/census-tracts-sf1-in-colorado-2000-ibr4-saca:latestThis will download all the objects for the latest tag of colorado-gov/census-tracts-sf1-in-colorado-2000-ibr4-saca 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/census-tracts-sf1-in-colorado-2000-ibr4-saca: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/census-tracts-sf1-in-colorado-2000-ibr4-saca: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/census-tracts-sf1-in-colorado-2000-ibr4-saca is just another Postgres schema.