ny-gov/statewide-commercial-baseline-study-of-new-york-y59s-sd8a
Icon for Socrata external plugin

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 statewide_commercial_baseline_study_of_new_york table in this repository, by referencing it like:

"ny-gov/statewide-commercial-baseline-study-of-new-york-y59s-sd8a:latest"."statewide_commercial_baseline_study_of_new_york"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "valid_n_for_total_widgets", -- Total number of respondents with valid quantities
    "region", -- The geographic region of NY which was surveyed; either All Regions, Downstate, LI/Hudson Valley, or Upstate. The regions are mutually exclusive; All Regions represents Statewide (not enough information to be able to segment by Downstate, Upstate, LI)
    "unit", -- Definition of widget used as the base of the calculation; either Compressors, Fixtures, Lamps, Nozzles, Server Racks, Servers, Sq Ft, or Widgets. Blank cells represent data that were not required or are not currently available
    "equipment_category", -- Type of equipment; Chillers, Cooking Equipment, Laundry, Motors_Type, etc. 
    "weighted_with_response", -- Weighted percent of valid respondents with the equipment sub-type (between 0 and 1)
    "sub_type_equipment_category", -- Sub-type of equipment. Blank cells represent data that were not required or are not currently available
    "total_n_respondents", -- Total number of respondents to the question
    "standard_error", -- Standard error of weighted percent of valid respondents with the equipment sub-type. Blank cells represent data that were not required or are not currently available.
    "sites_with_response", -- Total number of respondents who have the equipment sub-type. Blank cells represent data that were not required or are not currently available.
    "unweighted_with_response", -- Unweighted percent of valid respondents with the equipment sub-type (between 0 and 1). Blank cells represent data that were not required or are not currently available.
    "standard_error_of_of_equipment", -- Standard error of weighted percent of widgets in the end use category that are the equipment sub-type. Blank cells represent data that were not required or are not currently available
    "survey_type", -- Describes how the survey was completed; either Site or Phone
    "pct_of_equipment_unweighted", -- Unweighted percent of widgets in the end use category that are the equipment sub-type (between 0 and 1). Blank cells represent data that were not required or are not currently available. 
    "pct_of_equipment_weighted", -- Weighted percent of widgets in the end use category that are the equipment sub-type (between 0 and 1). Blank cells represent data that were not required or are not currently available
    "widgets_in_enduse_category", -- Total number of widgets which are the equipment sub-type. Blank cells represent data that were not required or are not currently available
    "total_widgets_in_enduse", -- Total number of widgets of the equipment category
    "question", -- Question number or identifier from the survey instrument.
    "end_use_category", -- Categorical variable describing the largest end-use of electricity for the site surveyed; either Building Spaces, Building Envelope, Commercial Kitchen, Compressed Air, Electric Vehicles, EMS, Exterior Lighting, HVAC_Controls, HVAC_Cooling, HVAC_Heating, HVAC_Ventilation, Interior Lighting, Motors, Office Equipment, On-Site Generation, Refrigeration, or Water Heating
    "usage_category", -- A categorical variable explaining how much electrical energy the survey site uses monthly; either All Usage, 75 MWh and Greater, or less Than 75 MWh. All Usage represents segments that could not be split by category.
    "segment", -- The business segment which was surveyed; either Education, Food Service, Grocery_Convenience, Health Services, Health Services/Hospitals, Hospitals, Lodging_Hospitality, Office_Government, Retail, Total, or Warehouse. Total represents the sum off all business segments.
    "valid_n_respondents" -- Total number of respondents who provided a valid response to the question. Blank cells represent data that were not required or are not currently available, a value of “0” represents 0.
FROM
    "ny-gov/statewide-commercial-baseline-study-of-new-york-y59s-sd8a:latest"."statewide_commercial_baseline_study_of_new_york"
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/statewide-commercial-baseline-study-of-new-york-y59s-sd8a 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/statewide-commercial-baseline-study-of-new-york-y59s-sd8a: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)"
 

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.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/statewide-commercial-baseline-study-of-new-york-y59s-sd8a" \
  --handler-options '{
    "domain": "data.ny.gov",
    "tables": {
        "statewide_commercial_baseline_study_of_new_york": "y59s-sd8a"
    }
}'

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/statewide-commercial-baseline-study-of-new-york-y59s-sd8a is just another Postgres schema.