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 energy_usage_2010 table in this repository, by referencing it like:
"cityofchicago/energy-usage-2010-8yq3-m6wp:latest"."energy_usage_2010"or in a full query, like:
SELECT
":id", -- Socrata column ID
"kwh_july_2010", -- Kilowatt hours (kWh) for July 2010
"kwh_june_2010",
"kwh_april_2010", -- Kilowatt hours (kWh) for April 2010
"kwh_march_2010", -- Kilowatt hours (kWh) for March 2010
"kwh_january_2010", -- Kilowatt hours (kWh) for January 2010
"building_subtype", -- Building Sub-Type (6): Single Family, Multi <7, Multi 7+, Commercial, Industrial, Municipal. Blank cells correspond to blank Census Blocks.
"census_block", -- Census Block number obtained in the address matching/geocoding algorithms. Blank Census Blocks correspond to data that was aggregated to the Community Area due to privacy issues.
"community_area_name", -- Community Name
"therm_november_2010", -- Therm consumption for November 2010.
"therm_october_2010", -- Therm consumption for October 2010.
"kwh_sqft_3rd_quartile_2010",
"therms_sqft_standard_deviation_2010",
"kwh_standard_deviation_2010",
"kwh_1st_quartile_2010",
"gas_accounts", -- Number of accounts with THERM information from the Peoples Account and Usage files in 2010. Accounts does not equate to buildings.
"therm_december_2010", -- Therm consumption for December 2010.
"therm_2nd_quartile_2010",
"therm_minimum_2010",
"kwh_sqft_mean_2010", -- Average kWh per square foot in 2010.
"renter_occupied_housing_units", -- Number of housing units that are renter occupied from Census 2010 report (QT-H2) Tenure, Household Size and Age of Householder.
"kwh_sqft_maximum_2010",
"kwh_february_2010", -- Kilowatt hours (kWh) for February 2010
"therm_mean_2010", -- Average Total Therms for 2010.
"kwh_sqft_minimum_2010",
"kwh_3rd_quartile_2010",
"therms_sqft_maximum_2010",
"therm_maximum_2010",
"therms_sqft_3rd_quartile_2010",
"therm_may_2010", -- Therm consumption for May 2010.
"therm_february_2010", -- Therm consumption for February 2010.
"therms_sqft_1st_quartile_2010",
"kwh_may_2010", -- Kilowatt hours (kWh) for June 2010
"total_units", -- Total number of housing units from census 2010 report (H1) Occupancy Status.
"occupied_units", -- Number of housing units that are occupied from census 2010 report (H1) Occupancy Status.
"kwh_sqft_standard_deviation_2010",
"therm_standard_deviation_2010",
"therm_1st_quartile_2010",
"average_housesize", -- Average household size from Census 2010 report QT-P11 Households and Families.
"average_stories", -- Average number of stories based on data from Cook County Assessor's Office.
"building_type", -- Building Type: Residential, Commercial, Industrial. Blank data corresponds to blank Census Blocks.
"total_kwh", -- Total 2010 kWh from ComEd accounts.
"occupied_units_percentage",
"therm_january_2010", -- Therm consumption for January 2010.
"renter_occupied_housing_percentage", -- Percentage of occupied housing units that are renters from Census report (QT-H2) Tenure, Household Size and Age of Householder
"kwh_september_2010", -- Kilowatt hours (kWh) for September 2010
"kwh_sqft_1st_quartile_2010",
"kwh_sqft_2nd_quartile_2010",
"therm_3rd_quartile_2010",
"therms_sqft_mean_2010", -- Average Therms per square foot in 2010.
"therms_sqft_minimum_2010",
"therms_sqft_2nd_quartile_2010",
"ten_ohu_tot", -- Number of occupied housing units from Census 2010 report (QT-H2) Tenure, Household Size and Age of Householder.
"kwh_2nd_quartile_2010",
"total_population", -- Total population from Census 2010 report (QT-P6) Race alone or in combination and Hispanic or Latino 2010.
"kwh_minimum_2010",
"kwh_maximum_2010",
"average_building_age", -- Average Age of the buildings based on data from Cook County Assessor's Office.
"kwh_mean_2010", -- Average Total KWHs for 2010.
"therms_total_sqft", -- Total square footage associated with the natural gas energy usage for Kilowatt Hours in 2010 according to Cook County Assessor Records.
"kwh_total_sqft", -- Total square footage associated with the electric energy usage in 2010 according to Cook County Assessor Records.
"total_therms", -- Total 2010 Therms from Peoples accounts.
"therm_september_2010", -- Therm consumption for September 2010.
"therm_august_2010", -- Therm consumption for August 2010.
"therm_july_2010", -- Therm consumption for July 2010.
"therm_june_2010", -- Therm consumption for June 2010.
"term_april_2010", -- Therm consumption for April 2010.
"therm_march_2010", -- Therm consumption for March 2010.
"zero_kwh_accounts", -- Number of accounts with 0 kilowatt hours amounts for 12 months in 2010 from ComEd.
"electricity_accounts", -- Number of accounts with kilowatt hour information from ComEd. An account does not equate to buildings.
"kwh_december_2010", -- Kilowatt hours (kWh) for December 2010
"kwh_november_2010", -- Kilowatt hours (kWh) for November 2010
"kwh_october_2010", -- Kilowatt hours (kWh) for October 2010
"kwh_august_2010" -- Kilowatt hours (kWh) for August 2010
FROM
"cityofchicago/energy-usage-2010-8yq3-m6wp:latest"."energy_usage_2010"
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 cityofchicago/energy-usage-2010-8yq3-m6wp 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 cityofchicago/energy-usage-2010-8yq3-m6wp: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 cityofchicago/energy-usage-2010-8yq3-m6wpCheckout 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 cityofchicago/energy-usage-2010-8yq3-m6wp:latestThis will download all the objects for the latest tag of cityofchicago/energy-usage-2010-8yq3-m6wp 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 cityofchicago/energy-usage-2010-8yq3-m6wp: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 cityofchicago/energy-usage-2010-8yq3-m6wp: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, cityofchicago/energy-usage-2010-8yq3-m6wp is just another Postgres schema.