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 dallas_animals_field_data_fiscal_year_2016_2017 table in this repository, by referencing it like:
"dallasopendata/dallas-animals-field-data-fiscal-year-2016-2017-xy7j-y59g:latest"."dallas_animals_field_data_fiscal_year_2016_2017"or in a full query, like:
SELECT
":id", -- Socrata column ID
"location_1_state",
"location_1_address",
"month", -- Months within which the incidents were reported throughout the FY2017
"user_id", -- System assigned user ID
"completed_date_time", -- Date & Time when the issue was resolved and completed
"created_date_time", -- Date & Time call was created in the system
"call_date_time", -- Date & Time call came in through 311
"radio_signal_code", -- Radio Signal Code
"cancel_reason", -- Reason 311 call was cancelled
"activity_status", -- Status of action(s) taken
"officer_id", -- Animal Services Officer ID number
"activity_comment", -- Dallas Animal Services Officer's Comment
"activity_quantity_3", -- Third Response
"activity_result_2", -- Second Response Result
"activity_sequence", -- Number of times the call has been worked
"tag_number", -- Tag or microchip number
"animal_type", -- Type or nature of animal
"animal_intake_id", -- Computer generated number assigned to an animal when taken to the shelter
"mapsco_page", -- Page in Mapsco Directory
"census_tract", -- Census tract number
"zip_code", -- Zip Code
"apartment", -- Apartment number
"street_direction", -- Address direction
"street_number", -- Address number
"street_address", -- Single field Street Address
"activity_subtype", -- Assessment of the call by the field agent
"activity_type", -- Action of the Field Agent related to the call
"activity_number", -- Computer generated call number
"location_1", -- Socrata System generated field for address mapping
"working_date_time", -- Date & Time Animal Services Officer started working per the call
"dispatch_date_time", -- Date & Time call was dispatched
"final_activity_result", -- Final Action Result
"activity_quantity_2", -- Second Response
"activity_result_1", -- First Response Result
"activity_quantity_1", -- First Response
"council_district", -- Council District Number
"city", -- City
"service_request_number", -- 311 Service Request Number
"activity_quantity_4", -- Fourth Response
"final_activity_quantity", -- Final Action taken
"animal_description", -- Details about the health, behavior and response of the animal
"activity_result_5", -- Fifth Response Result
"activity_result_3", -- Third Response Result
"state", -- State
"activity_result_4", -- Fourth Response Result
"year", -- Fiscal year
"street_type", -- Street type
"street_name", -- Street Name
"activity_quantity_5", -- Fifth Response
"activity_priority", -- Priority Level of the call
"location_1_city",
"location_1_zip",
":@computed_region_28rh_izyk",
":@computed_region_sjyw_rtbm",
":@computed_region_at43_7y52",
":@computed_region_2f7u_b5gs",
":@computed_region_3qur_xvie"
FROM
"dallasopendata/dallas-animals-field-data-fiscal-year-2016-2017-xy7j-y59g:latest"."dallas_animals_field_data_fiscal_year_2016_2017"
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 dallasopendata/dallas-animals-field-data-fiscal-year-2016-2017-xy7j-y59g 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 dallasopendata/dallas-animals-field-data-fiscal-year-2016-2017-xy7j-y59g: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 dallasopendata/dallas-animals-field-data-fiscal-year-2016-2017-xy7j-y59gCheckout 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 dallasopendata/dallas-animals-field-data-fiscal-year-2016-2017-xy7j-y59g:latestThis will download all the objects for the latest tag of dallasopendata/dallas-animals-field-data-fiscal-year-2016-2017-xy7j-y59g 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 dallasopendata/dallas-animals-field-data-fiscal-year-2016-2017-xy7j-y59g: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 dallasopendata/dallas-animals-field-data-fiscal-year-2016-2017-xy7j-y59g: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, dallasopendata/dallas-animals-field-data-fiscal-year-2016-2017-xy7j-y59g is just another Postgres schema.