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 animals_inventory_fiscal_year_2017_2018 table in this repository, by referencing it like:
"dallasopendata/animals-inventory-fiscal-year-2017-2018-tq3c-nqwb:latest"."animals_inventory_fiscal_year_2017_2018"or in a full query, like:
SELECT
":id", -- Socrata column ID
"year", -- Added column to improve visualization of activities by specific fiscal years
"animal_stay_days", -- Number of days the animal has been in the shelter
"animal_origin", -- Comments related to the origin of the animal impounded
"outcome_condition", -- The condition of the animal when the animal leaves the shelter with respect to the Asilomar Accord
"impound_number", -- Auto generated number by the Chameleon software for record keeping
"receipt_number", -- Auto generated number by Chameleon when a transaction occurs, such as adoption or redemption
"intake_condition", -- The condition of the animal when it is impounded with respect to the Asilomar Accord
"due_out", -- Date at which the animal is due for review
"intake_time", -- The time at which the animal was impounded or admitted into the shelter
"reason", -- Reason provided by the animal's owners as to why they are giving up their pet to DAS
"intake_subtype", -- sub-reason or detailed reason why the animal was impounded
"intake_type", -- Reason why the animal was impounded
"council_district", -- Council District number where the animal was reported/found
"census_tract", -- Census tract numbers
"source_id", -- Auto generated number assigned to a person who found the animal, turned the animal in or the person from whom DAS picked up the animal
"activity_number", -- Auto generated number assigned to a field animal rescued from field incidents to the shelter
"kennel_number", -- Kennel number in which the animal is housed at the shelter
"animal_id", -- System auto generated number, unique to every animal impounded
"additional_information", -- Staffs comments and notes related to services provided to specific animals at various stages
"hold_request", -- This contains any hold requests that have been made concerning the animal
"intake_date", -- The date of impoundment or when the animal was admitted into the shelter
"staff_id", -- Impounding staff initials
"animal_breed", -- Record the animal breed or wildlife
"animal_type", -- Record of the type of animal
"kennel_status", -- Status of the animal during its stay at the shelter; depends on the services provided to the animal during its stay at the shelter
"activity_sequence", -- This represents the sequence for the activity/call, and one activity/call can have multiple sequence.
"outcome_date", -- The date of the outcome of the animal
"outcome_time", -- The time of the outcome of the animal
"outcome_type", -- Final disposition of the animal
"chip_status", -- Results of animal being scanned for microchip
"month", -- Added column to improve visualization of activities by month
"service_request_number", -- Number auto generated by 3-1-1 when a call comes in for services
"tag_type" -- Types of the tags include but may not be limited to microchip, rabies, and/or registration tag that is assign by DAS
FROM
"dallasopendata/animals-inventory-fiscal-year-2017-2018-tq3c-nqwb:latest"."animals_inventory_fiscal_year_2017_2018"
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/animals-inventory-fiscal-year-2017-2018-tq3c-nqwb 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/animals-inventory-fiscal-year-2017-2018-tq3c-nqwb: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/animals-inventory-fiscal-year-2017-2018-tq3c-nqwbCheckout 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/animals-inventory-fiscal-year-2017-2018-tq3c-nqwb:latestThis will download all the objects for the latest tag of dallasopendata/animals-inventory-fiscal-year-2017-2018-tq3c-nqwb 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/animals-inventory-fiscal-year-2017-2018-tq3c-nqwb: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/animals-inventory-fiscal-year-2017-2018-tq3c-nqwb: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/animals-inventory-fiscal-year-2017-2018-tq3c-nqwb is just another Postgres schema.