sonomacounty-ca-gov/homeless-dedicated-housing-4svx-khws
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Query the Data Delivery Network

Query the DDN

The 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 homeless_dedicated_housing table in this repository, by referencing it like:

"sonomacounty-ca-gov/homeless-dedicated-housing-4svx-khws:latest"."homeless_dedicated_housing"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "last_update_on",
    "hmis_beds_hh_w_children", -- Beds for Households with Children listed in Homeless Management Information System.  
    "beds_hh_w_o_children", -- Beds for Households with Children
    "beds_hh_w_children", -- Beds for Households with Children
    "project_name",
    "ch_beds_hh_w_o_children", -- Beds for Chronically Homeless Households without Children (Head of Household is Chronically Homeless).
    "target_pop_a", -- SM=Single Males, SF=Single Females, SMF=Single Males & Females, HC=Households with Children, YMF=Youth Male & Female, SMHC/SFHC/SMF+HC=combinations of the foregoing
    "year", -- The year of data collection.
    "location_1_address",
    "location_1_city",
    "location_1_state",
    ":@computed_region_xw9s_pz78",
    ":@computed_region_dig5_f3vy",
    "location_1_zip",
    "inventory_type", -- C=Current, N=New (first year active), U=Under development (not yet active)
    "veteran_beds_hh_w_o_children", -- Beds for Veteran Households without Children (Head of Household is Veteran).
    "th_unit_type",
    "bed_type", -- Facility-based beds = all beds in one facility, Other beds = beds in scattered locations.
    "target_pop_b", -- DV=Victims of Domestic Violence, HIV=Individuals with HIV, NA=Not Applicable
    "mckinney_vento", -- Whether the project receives any HUD McKinney-Vento funding.
    "units_hh_w_children", -- Units (rooms) for Households with Children
    "veteran_beds_hh_w_children", -- Beds for Veteran Households with Children (Head of Household is Veteran)
    "ch_beds_hh_w_children", -- Beds for Chronically Homeless Households with Children (Head of Household is Chronically Homeless).
    "year_round_beds", -- Number of year-round beds available.
    "hmis_beds_hh_w_only_children", -- Beds for Households with only Children listed in Homeless Management Information System.
    "of_hmis_beds_hh_w_only_children", -- Beds for Households with only Children listed in Homeless Management Information System / Beds for Households with only Children.
    "seasonal_beds_available_in_hmis", -- Number of seasonal (partial year) beds listed in Homeless Management Information System.
    "total_seasonal_beds", -- Number of seasonal (partial year) beds.
    "pit_count", -- Count of participants in program at Point In Time Count (January 27, 2017).
    "overflow_beds", -- Overflow beds are available on an ad hoc or temporary basis during the year in response to demand that exceeds planned (year-round or seasonal) bed capacity.
    "utilization_rate", -- Utilization Rate = Point in Time Count / Total Beds.
    "total_beds", -- Total beds in program.
    "proj_type", -- ES = Emergency Shelter  OPH =  Other Perminant Housing  PSH = Permanent Supportive Housing  RRH = Rapid Re-Housing  TH = Transitional Housing
    "location_1", -- Location of the center of the Census Tract and not the actual location of the shelter. 
    "beds_hh_w_only_children", -- Beds for Households with only Children
    "youth_beds_hh_w_children", -- Beds for Youth Households with Children (Head of Household is Youth.
    "ch_beds_hh_w_only_children", -- Beds for Chronically Homeless Households with only Children (Head of Household is under age 19 and Chronically Homeless).
    "other_federal_funding_other", -- What other HUD (non McKinney-Vento) funding is received.
    "youth_beds_hh_w_o_children", -- Beds for Youth Households with Children (Head of Household is Youth).
    "other_federal_funding", -- Whether the project receives other HUD (non McKinney-Vento) funding.
    "hmis_beds_hh_w_o_children", -- Beds for Households without Children listed in Homeless Management Information System.
    "of_hmis_beds_hh_with_children", -- Beds for Households with Children listed in Homeless Management Information System / Beds for Households with Children.
    "of_hmis_beds_hh_without_children" -- Beds for Households without Children listed in Homeless Management Information System / Beds for Households without Children. 
FROM
    "sonomacounty-ca-gov/homeless-dedicated-housing-4svx-khws:latest"."homeless_dedicated_housing"
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 sonomacounty-ca-gov/homeless-dedicated-housing-4svx-khws with SQL in under 60 seconds.

Query Your Local Engine

Install Splitgraph Locally
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 sonomacounty-ca-gov/homeless-dedicated-housing-4svx-khws: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 sonomacounty-ca-gov/homeless-dedicated-housing-4svx-khws

Checkout 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 sonomacounty-ca-gov/homeless-dedicated-housing-4svx-khws:latest

This will download all the objects for the latest tag of sonomacounty-ca-gov/homeless-dedicated-housing-4svx-khws 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 sonomacounty-ca-gov/homeless-dedicated-housing-4svx-khws: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 sonomacounty-ca-gov/homeless-dedicated-housing-4svx-khws:latest

This 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, sonomacounty-ca-gov/homeless-dedicated-housing-4svx-khws is just another Postgres schema.

Related Documentation:

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