ny-gov/multifamily-residential-existing-and-new-xt6e-eyna

  • cost
  • funding
  • kwh
  • measure
  • mmbtu
  • + 3

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 procotol. 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 multifamily_residential_existing_and_new table in this repository, by referencing it like:

"ny-gov/multifamily-residential-existing-and-new-xt6e-eyna:latest"."multifamily_residential_existing_and_new"

or in a full query, like:

SELECT 
    ":id", -- Socrata column ID
    "georeference", -- Open Data/Socrata-generated geocoding information from supplied address components.
    "total_estimated_annual_energy_savings_mmbtu_", -- Estimated annual fuel savings or consumption. A positive number indicates savings and a negative number indicates new consumption. Zero or blank cells indicate no savings BEDES* Term: Projected Annual Non Electric Energy Resource Savings
    "est_annual_energy_sav_steam", -- Pre-construction estimated annual steam savings or consumption. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings. BEDES Term: Projected Annual District Steam Resource Value
    "est_annual_energy_sav_natgas", -- Pre-construction estimated annual natural gas savings or consumption. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings. BEDES Term: Projected Annual Non Electric Energy Resource Savings
    "est_annual_energy_sav_propane", -- Estimated annual propane savings or consumption. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings.
    "est_annual_energy_sav_oil", -- Estimated annual #2/distillate oil savings or consumption. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings. BEDES Term: Projected Annual Fuel Oil Resource Value
    "total_est_electric_savings", -- Estimated annual measure electricity Resource Savings. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings. BEDES Term: Projected Annual Electricity Resource Savings
    "building_zip", -- ZIP Code where project is located. Blank cells represent data that were not required or are not currently available
    "building_city", -- City where project is located. Blank cells represent data that were not required or are not currently available
    "building_address", -- Street Address of building where project is located. Blank cells represent data that were not required or are not currently available BEDES* Term: Premise Address Line 1
    "measure_category", -- Category of upgrade - from dropdown list of options BEDES Term: Technology Category
    "measure", -- Name of upgrade - from dropdown list of options based on "Measure Category" field selection BEDES Term: Measure Name Identifier
    "percent_installed", -- Estimated percent of installation that is complete. Value will equal 100 when project is complete BEDES Term: Completed Percent of Total
    "proposed_install_unit_cost", -- Total proposed cost of installed measures in U.S dollars. A value of 0 indicate that the measure was installed in house, with no cost incurred. Blank cells represent data that were not required or are not currently available BEDES* Term: Proposed Measure Total Cost
    "program_type", -- Indicates if the project was part of NYSERDA’s Existing Buildings or New Construction program BEDES Term: Program Name Identifier
    "project_name", -- Unique project name BEDES Term: Project Name
    ":@computed_region_kjdx_g34t", -- This column was automatically created in order to record in what polygon from the dataset 'Counties' (kjdx-g34t) the point in column 'georeference' is located.  This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
    ":@computed_region_wbg7_3whc", -- This column was automatically created in order to record in what polygon from the dataset 'New York Zip Codes' (wbg7-3whc) the point in column 'georeference' is located.  This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
    ":@computed_region_yamh_8v7k", -- This column was automatically created in order to record in what polygon from the dataset 'NYS Municipal Boundaries' (yamh-8v7k) the point in column 'georeference' is located.  This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
    "est_annual_energy_sav_other", -- Pre-construction estimated annual savings or consumption from other energy types. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings.
    "total_est_electric_demand_reduc" -- Estimated annual measure electricity demand resource savings. A positive number indicates savings and a negative number indicates new consumption. Zero or blank indicates no savings. BEDES Term: Projected Annual Electricity Summer Demand Resource Savings
FROM
    "ny-gov/multifamily-residential-existing-and-new-xt6e-eyna:latest"."multifamily_residential_existing_and_new"
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 ny-gov/multifamily-residential-existing-and-new-xt6e-eyna with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.ny.gov. When you queryny-gov/multifamily-residential-existing-and-new-xt6e-eyna: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

Install Splitgraph Locally
bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"
 

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.ny.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 \
  "ny-gov/multifamily-residential-existing-and-new-xt6e-eyna" \
  --handler-options '{
    "domain": "data.ny.gov",
    "tables": {
        "multifamily_residential_existing_and_new": "xt6e-eyna"
    }
}'

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, ny-gov/multifamily-residential-existing-and-new-xt6e-eyna is just another Postgres schema.

Related Documentation: