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 commercial_vacancy_multifamily table in this repository, by referencing it like:
"citydata-mesaaz-gov/commercial-vacancy-multifamily-v8b7-pc2z:latest"."commercial_vacancy_multifamily"or in a full query, like:
SELECT
":id", -- Socrata column ID
"total_number_of_mf_units", -- Total number of existing Multi-Family units in Mesa.
"number_of_units_under", -- The total number of new Multi-Family units that are under construction in a given period of time
"under_construction_percent",
"concessions_percentage", -- The percentage of effective rent per unit over the asking rent per unit.
"number_of_buildings_delivered", -- The total number of new Multi-Family buildings that were completed in a given period of time.
"average_square_footage_of", -- Average square footage of existing Multi-Family units in Mesa.
"number_of_occupied_mf_units", -- The number of occupied Multi-Family units in Mesa.
"mf_unit_vacancy_rate", -- The number of vacant Multi-Family units in Mesa against the total inventory of Multi Family units in Mesa.
"location", -- Indicates if the row is part of the Downtown area or not
"annual_year_over_year_growth_1", -- The year-over-year growth in effective rent price over the same quarter from the previous year. i.e. Q3 2013 to Q3 2014.
"net_absorption_percent", -- The percent of units absorbed in the current quarter compared to the total number of occupancy units in the same quarter.
"asking_rent_per_unit", -- The asking price of rent per Multi-Family unit in Mesa.
"deliveries_percent",
"effective_rent_per_square", -- The average price of rent paid per square foot over the term by a tenant, adjusted downward from asking for concessions paid for by the landlord (such as free rent, moving expenses, or other allowances)
"effective_rent_per_unit", -- The average price of rent paid per unit over the term by a tenant, adjusted downward from asking for concessions paid for by the landlord (such as free rent, moving expenses, or other allowances)
"number_of_vacant_mf_units", -- The total number of vacant Multi-Family units in Mesa.
"net_absorption_units", -- The total amount of Multi-Family units in Mesa that was net absorbed, which is the total units occupied less the total units vacated over a period of time.
"building_inventory", -- Total number of existing Multi-Family buildings in Mesa.
"total_percent_occupied", -- Percentage of the Inventory Square Footage in Mesa that is occupied.
"number_of_buildings_under", -- The total number of new Multi-Family buildings that are under construction in a given period of time.
"calendar_year", -- The year that the data pertains to.
"vacancy_growth_year", -- The percentage change in the vacancy rate for the quarter compared to the same quarter in the previous year.
"asking_rent_per_sf", -- The average asking price of rent per square foot for Multi-Family units in Mesa.
"number_of_units_delivered", -- The total number of new Multi-Family units that were completed in a given period of time.
"quarter_date", -- The 3-month period, or quarter, that the data pertains to in date format.
"calendar_year_and_quarter", -- The 3-month period, or quarter, that the data pertains to.
"annual_year_over_year_growth_2", -- The year-over-year change in occupancy over the same quarter from the previous year. i.e. Q3 2013 to Q3 2014.
"annual_year_over_year_growth" -- The year-over-year growth in asking rent price over the same quarter from the previous year. i.e. Q3 2013 to Q3 2014.
FROM
"citydata-mesaaz-gov/commercial-vacancy-multifamily-v8b7-pc2z:latest"."commercial_vacancy_multifamily"
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 citydata-mesaaz-gov/commercial-vacancy-multifamily-v8b7-pc2z 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 citydata-mesaaz-gov/commercial-vacancy-multifamily-v8b7-pc2z: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 citydata-mesaaz-gov/commercial-vacancy-multifamily-v8b7-pc2zCheckout 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 citydata-mesaaz-gov/commercial-vacancy-multifamily-v8b7-pc2z:latestThis will download all the objects for the latest tag of citydata-mesaaz-gov/commercial-vacancy-multifamily-v8b7-pc2z 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 citydata-mesaaz-gov/commercial-vacancy-multifamily-v8b7-pc2z: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 citydata-mesaaz-gov/commercial-vacancy-multifamily-v8b7-pc2z: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, citydata-mesaaz-gov/commercial-vacancy-multifamily-v8b7-pc2z is just another Postgres schema.