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 statewide_commercial_baseline_study_of_new_york table in this repository, by referencing it like:
"ny-gov/statewide-commercial-baseline-study-of-new-york-ttu3-cutd:latest"."statewide_commercial_baseline_study_of_new_york"or in a full query, like:
SELECT
":id", -- Socrata column ID
"equipment_category", -- Type of equipment; Chillers, Compressed Air, Motors, Water Heater, etc. All Sites indicates the base of the mean presented is all business-premises (aka sites), while All Buildings indicates the base of the mean presented is all buildings (i.e., if a business-premise contains more than one building, like a campus, each building is included in the calculation). These are not duplicative of other data points, but instead are used for particular types of mean metrics (e.g., number of stories per building, average square footage per site, etc.). Equipment Category acts as an enduse/equipment identifier, and these metrics are not equipment based, but rather building or site based.
"question_response", -- Sub-type of equipment. Blank cells represent data that were not required or are not currently available
"valid_n_respondents", -- Total number of respondents who provided a valid response to the question
"valid_quantity", -- Total widgets in the end use category. Blank cells represent data that were not required or are not currently available
"unweighted_mean", -- Unweighted average by equipment sub-type. Blank cells represent data that were not required or are not currently available
"weighted_mean", -- Weighted average by equipment sub-type. Blank cells represent data that were not required or are not currently available
"standard_error_of_weighted", -- Standard error of weighted mean. Blank cells represent data that were not required or are not currently available
"segment", -- The business segment which was surveyed; either Education, Food Service, Grocery_Convenience, Health Services/Hospitals, Lodging_Hospitality, Office_Government, Retail, Total, or Warehouse. Total represents the sum off all business segments.
"weighted_and_adjusted_mean", -- Weighted and adjusted average by equipment sub-type. Blank cells represent data that were not required or are not currently available
"usage_category", -- A categorical variable explaining how much energy the survey site uses; either All Usage, Less Than 75 MWh or 75 MWh and Greater. All Usage represents segments that could not be split by usage category.
"means_variable_label", -- The metric of interest that is summarized
"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, update, LI)
"survey_type", -- Describes how the survey was completed; either Site or Phone. Blank cells represent data that were not required or are not currently available
"end_use_category", -- Categorical variable describing the largest end-use of electricity for the site surveyed; either Building Characteristics, Building Envelope, Commercial Kitchen, Compressed Air, District Stream, Electric Vehicles, EMS, Exterior Lighting, HVAC_Controls, HVAC_Cooling, HVAC_Heating, HVAC_Ventilation, Interior Lighting, Maintenance and RCx, Motors, Occupancy Hours, Office Equipment, On-Site Generation, Refrigeration, or Water Heating.
"question" -- Question number from the survey instrument
FROM
"ny-gov/statewide-commercial-baseline-study-of-new-york-ttu3-cutd: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-ttu3-cutd 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 ny-gov/statewide-commercial-baseline-study-of-new-york-ttu3-cutd: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 ny-gov/statewide-commercial-baseline-study-of-new-york-ttu3-cutdCheckout 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 ny-gov/statewide-commercial-baseline-study-of-new-york-ttu3-cutd:latestThis will download all the objects for the latest tag of ny-gov/statewide-commercial-baseline-study-of-new-york-ttu3-cutd 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 ny-gov/statewide-commercial-baseline-study-of-new-york-ttu3-cutd: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 ny-gov/statewide-commercial-baseline-study-of-new-york-ttu3-cutd: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, ny-gov/statewide-commercial-baseline-study-of-new-york-ttu3-cutd is just another Postgres schema.