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 plan_review_cases table in this repository, by referencing it like:
"datahub-austintexas-gov/plan-review-cases-n8ck-xkda:latest"."plan_review_cases"or in a full query, like:
SELECT
":id", -- Socrata column ID
"update_date", -- the date the data was created
"propy", -- State Plane Y Coordinate of the case
"total_existing_bldg_footage", -- Total existing building square footage
"remodel_repair_footage", -- square footage that will be remodelled or repaired.
"medgas_valuation_remodel", -- Estimated expenditure on patient medical gas or medical support gas components in remodelling structures.
"expires_date", -- the date the case would expire
"owner_organization_name", -- the owner's organization name
"status_date", -- the date for the current status
"project_name", -- the folder name
"referencefile", -- the case number
"permit_number", -- the permit number
"location", -- the latitude and longitude value needed to map in the Socrata data.austintexas.gov site
"propx", -- State Plane X Coordinate of the case
"legal_description", -- Legal Description of the property associated with the permit.
"total_new_add_footage", -- total new or additional square footage
"total_job_valuation", -- Estimated expenditure on construction (will include plumbing, mechanical, electrical costs etc)
"plumbing_valuation_remodel", -- Estimated expenditure on remodelling (plumbing costs)
"number_of_units", -- Number of residential units in the structure to be built.
"number_of_floors", -- Number of floors of the structure to be built.
"mechanical_valuation_remodel", -- Estimated expenditure on remodelling ( mechanical costs )
"electrical_valuation_remodel", -- Estimated expenditure on remodelling - electrical costs
"electrical_valuation", -- Estimated expenditure on electrical costs
"applied_date", -- the date the case was applied for
"calendar_year_issued", -- Calendar year of the issued date of the case was in the last 30 days
"issued_date", -- the issued date of the case
"other_full_name", -- name of other entitiy associated with the case
"owner_phone", -- the owner's phone
"owner_full_name", -- the owner's full name
"applicant_phone", -- the applicant's phone number
"applicant_organization_name", -- the applicant's organization name
"applicant_full_name", -- the applicant's full name
"status_current", -- the current status of the record
"folder_section", -- the folder section
"sub_type", -- the sub type of case
"building_valuation_remodel", -- Estimated expenditure on remodelling (building portion only)
"building_valuation", -- Estimated expenditure on new construction (building portion only)
"final_date", -- the date the case was finaled
"other_organization_name", -- name of other entitiy's organization associated with the case
"total_valuation_remodel", -- Estimated expenditure on remodelling (will include plumbing, mechanical, electrical costs etc)
"plumbing_valuation", -- Estimated expenditure on plumbing costs
"propertyrsn", -- the property record number of the AMANDA system
"appraisal_id", -- the id of the appraisal record
"other_phone", -- the other's phone
"applicant_address", -- the applicant's address
"folder_description", -- the folder description of the case
"mechanical_valuation", -- Estimated expenditure on mechanical costs
"owner_address", -- the owner's address
"medgas_valuation", -- Estimated expenditure on patient medical gas or medical support gas components in structures.
"issued_in_last_30_days", -- whether the issued date of the case was in the last 30 days
"folderrsn", -- the AMANDA folderrsn tracking number
"fiscal_year_issued", -- Fiscal year of the issued date of the case was in the last 30 days
"other_address", -- the other's address
"council_district", -- Council District in which case is located
"day_issued", -- the issued day of the case
"condominium", -- whether the project is a condominium
"express_permit", -- whether permit is express or not
"work_class", -- whether it is residential or commercial
"folder_type", -- the type of AMANDA folder
"permit_type", -- the type of case
"fiscal_year_indate", -- the fiscal year case was applied for
"web_link", -- the link to the Austin Build and Connect web site for the case
":@computed_region_xzeg_zdjk",
":@computed_region_rxpj_nzrk",
":@computed_region_m2th_e4b7",
":@computed_region_e9j2_6w3z",
":@computed_region_q9nd_rr82",
":@computed_region_jcrc_4uuy",
":@computed_region_8spj_utxs"
FROM
"datahub-austintexas-gov/plan-review-cases-n8ck-xkda:latest"."plan_review_cases"
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 datahub-austintexas-gov/plan-review-cases-n8ck-xkda 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 datahub-austintexas-gov/plan-review-cases-n8ck-xkda: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 datahub-austintexas-gov/plan-review-cases-n8ck-xkdaCheckout 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 datahub-austintexas-gov/plan-review-cases-n8ck-xkda:latestThis will download all the objects for the latest tag of datahub-austintexas-gov/plan-review-cases-n8ck-xkda 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 datahub-austintexas-gov/plan-review-cases-n8ck-xkda: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 datahub-austintexas-gov/plan-review-cases-n8ck-xkda: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, datahub-austintexas-gov/plan-review-cases-n8ck-xkda is just another Postgres schema.