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 2018_social_vulnerability_index table in this repository, by referencing it like:
"brla-gov/2018-social-vulnerability-index-q7v5-ijjg:latest"."2018_social_vulnerability_index"or in a full query, like:
SELECT
":id", -- Socrata column ID
"ep_uninsur", -- 2014-2018 American Community Survey (ACS) estimated adjunct variable - percentage uninsured in the total civilian noninstitutionalized population
"rpl_themes", -- Overall percentile ranking for all themes
"spl_themes", -- Sum of series themes
"spl_theme4", -- Sum of series for Housing Type/ Transportation theme
"ep_noveh", -- Estimated percentage of households with no vehicle available
"ep_crowd", -- Estimated percentage of occupied housing units with more people than rooms
"e_crowd", -- 2014-2018 American Community Survey (ACS) estimated at household level (occupied housing units), more people than rooms
"e_mobile", -- 2014-2018 American Community Survey (ACS) mobile homes
"e_munit", -- 2014-2018 American Community Survey (ACS) estimated housing in structures with 10 or more units
"ep_limeng", -- 2014-2018 American Community Survey (ACS) estimated percentage of persons (age 5+) who speak English "less than well"
"e_minrty", -- 2014-2018 American Community Survey (ACS) estimated minority (all persons except white, non-Hispanic)
"ep_sngpnt", -- 2014-2018 American Community Survey (ACS) estimated percentage of single parent households with children under 18
"e_sngpnt", -- 2014-2018 American Community Survey (ACS) estimated single parent household with children under 18
"ep_disabl", -- 2014-2018 American Community Survey (ACS) estimated percentage of civilian non-institutionalized population with a disability
"ep_age17", -- 2014-2018 American Community Survey (ACS) estimated percentage of persons aged 17 and younger
"e_age17", -- 2014-2018 American Community Survey (ACS) estimated persons aged 17 and younger
"ep_age65", -- 2014-2018 American Community Survey (ACS) estimated percentage of persons aged 65 and older
"e_nohsdp", -- 2014-2018 American Community Survey (ACS) estimated persons (age 25+) with no high school diploma
"e_pci", -- 2014-2018 American Community Survey (ACS) estimated per capita income
"ep_unemp", -- Estimated unemployment rate
"ep_pov", -- Estimated percentage of persons below poverty
"e_pov", -- 2014-2018 American Community Survey (ACS) estimated persons below poverty
"e_hh", -- 2014-2018 American Community Survey (ACS) estimated households
"e_hu", -- 2014-2018 American Community Survey (ACS) estimated housing units
"e_totpop", -- 2014-2018 American Community Survey (ACS) estimated population
"area_sqmi", -- Area in square miles
"fips", -- Unique identifier for each Census tract based on the Federal Information Processing Standards (FIPS) code
"e_limeng", -- 2014-2018 American Community Survey (ACS) estimated persons (age 5+) who speak English "less than well"
"e_noveh", -- 2014-2018 American Community Survey (ACS) estimated households with no vehicle available
"e_unemp", -- 2014-2018 American Community Survey (ACS) civilian (age 16+) estimated unemployed
"shape", -- Polygon geometry displaying the Census tracts
"e_uninsur", -- 2014-2018 American Community Survey (ACS) estimated adjunct variable - uninsured in the total civilian noninstitutionalized population
"spl_theme3", -- Sum of series for Minority Status/Language theme
"spl_theme2", -- Sum of series for Household Composition theme
"spl_theme1", -- Sum of series for Socioeconomic theme
"ep_groupq", -- 2014-2018 American Community Survey (ACS) estimated percentage of persons in institutionalized group quarters
"e_groupq", -- 2014-2018 American Community Survey (ACS) estimated persons in institutionalized group quarters
"ep_mobile", -- Estimated percentage of mobile homes
"ep_munit", -- Estimated percentage of housing in structures with 10 or more units
"ep_minrty", -- 2014-2018 American Community Survey (ACS) estimated percentage minority (all persons except white, non-Hispanic)
"e_disabl", -- 2014-2018 American Community Survey (ACS) estimated civilian non-institutionalized population with a disability
"e_age65", -- 2014-2018 American Community Survey (ACS) estimated persons aged 65 and older
"ep_nohsdp", -- Estimated percentage of persons with no high school diploma (age 25+)
"e_daypop", -- LandScan 2018 adjunct variable - estimated daytime population
"st_abbr", -- State abbreviation
"county" -- Parish name
FROM
"brla-gov/2018-social-vulnerability-index-q7v5-ijjg:latest"."2018_social_vulnerability_index"
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 brla-gov/2018-social-vulnerability-index-q7v5-ijjg 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 brla-gov/2018-social-vulnerability-index-q7v5-ijjg: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 brla-gov/2018-social-vulnerability-index-q7v5-ijjgCheckout 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 brla-gov/2018-social-vulnerability-index-q7v5-ijjg:latestThis will download all the objects for the latest tag of brla-gov/2018-social-vulnerability-index-q7v5-ijjg 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 brla-gov/2018-social-vulnerability-index-q7v5-ijjg: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 brla-gov/2018-social-vulnerability-index-q7v5-ijjg: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, brla-gov/2018-social-vulnerability-index-q7v5-ijjg is just another Postgres schema.