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 lahd_affordable_housing_projects_list_2003_to table in this repository, by referencing it like:
"lacity/lahd-affordable-housing-projects-list-2003-to-mymu-zi3s:latest"."lahd_affordable_housing_projects_list_2003_to"or in a full query, like:
SELECT
":id", -- Socrata column ID
"geocoded_column", -- The GPS coordinates of the project site address
"longitude", -- The longitude of the project site address
"contract_numbers", -- Reference to a city contract of each project in plain text format.
"jobs", -- The amount of jobs supported
"contact_phone", -- The phone number of the management company
"management_company", -- The name of the management company
"sh_units_per_site", -- Special Needs per homeless units per site.
"tax_exempt_conduit_bond", -- The amount of tax exempt conduit bond issued for the project
"latitude", -- The latitude of the project site address
"in_service_date", -- The year when the project is ready for occupany
"hcidla_funded", -- The HCIDLA loan amount awarded for the project
"developer", -- The name of the developer of the project
"date_stamp", -- The time and date when the information is updated
"date_funded", -- Date funding amount for a project
":@computed_region_qz3q_ghft",
":@computed_region_2dna_qi2s",
":@computed_region_kqwf_mjcx",
":@computed_region_ur2y_g4cx",
":@computed_region_tatf_ua23",
":@computed_region_k96s_3jcv",
"reporturl2", -- Printable project summary report
"photo", -- The photo of the project
"tdc", -- Total development cost
"leverage", -- The amount of public and private funds leveraged for the project
"supportive_housing", -- Yes/No for total special needs if a site has at least one supportive housing unit.
"housing_type", -- The population served by the project
"project_total_units", -- Total units per project
"site_units", -- Total number of units per site
"community", -- The Community of the project
"site_cd", -- The site number of the project site. Single site project will always display 1
"council_district", -- The Council District of the project/site
"address", -- The location of the project/site
"construction_type", -- The type of construction, typically "New" or "Rehab"
"development_stage", -- The stage where the development is at
"name", -- The name of the project
"project_number", -- Housing Information Management System (HIMS) Project Number
"apn" -- Assessor Parcel Number(APN) of the project
FROM
"lacity/lahd-affordable-housing-projects-list-2003-to-mymu-zi3s:latest"."lahd_affordable_housing_projects_list_2003_to"
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 lacity/lahd-affordable-housing-projects-list-2003-to-mymu-zi3s 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 lacity/lahd-affordable-housing-projects-list-2003-to-mymu-zi3s: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 lacity/lahd-affordable-housing-projects-list-2003-to-mymu-zi3sCheckout 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 lacity/lahd-affordable-housing-projects-list-2003-to-mymu-zi3s:latestThis will download all the objects for the latest tag of lacity/lahd-affordable-housing-projects-list-2003-to-mymu-zi3s 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 lacity/lahd-affordable-housing-projects-list-2003-to-mymu-zi3s: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 lacity/lahd-affordable-housing-projects-list-2003-to-mymu-zi3s: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, lacity/lahd-affordable-housing-projects-list-2003-to-mymu-zi3s is just another Postgres schema.