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 daily_animals_outcome table in this repository, by referencing it like:
"dallasopendata/daily-animals-outcome-8exr-czrc:latest"."daily_animals_outcome"or in a full query, like:
SELECT
":id", -- Socrata column ID
"chip_status", -- Notates whether staff were successful in scanning animal for a microchip.
"reason", -- Reason that an animal was surrendered to or brought to DAS.
"due_out", -- Date the animal's stray hold expires and animal will be available for non-return to owner outcomes; date DAS has full ownership of the animal based on city ordinance.
"breed_group",
"animal_breed", -- Breed of the animal; typically a visual breed estimation.
"service_request_number", -- Unique number assigned to each service request (SR), including external SR's submitted by residents through 3-1-1, proactive SR's initiated by a staff member, and follow up SR's where staff follow up on a previous situation.
"receipt_number", -- Unique number assigned to each financial transaction that occurs in Chameleon database.
"outcome_condition", -- Apparent medical condition of the animal when it was released from DAS.
"intake_type", -- Type or purpose of intake; used primarily to analyze intake trends.
"source_id", -- Person who initiated the intake.
"kennel_status", -- Availability of the animal.
"kennel_number", -- Location of the animal at the time of the report
"intake_total", -- Quantity of animals intaken together.
"intake_time", -- Time the animal was intaken by DAS.
"intake_subtype", -- Additional categorization of purpose of intake; used primarily to analyze intake trends.
"intake_date", -- Date the animal was intaken by DAS.
"intake_condition", -- Apparent medical condition of the animal when it was taken in by DAS.
"days_in", -- The length of stay an animal has been in the shelter.
"council_district", -- City of Dallas Council District in which the action was located.
"animal_type", -- Animal category: dog, cat, wildlife, other, etc.
"animal_origin", -- Notates whether the animal came in through DAS' Pet Support Lobby (Over the Counter) or through Field Services (Field).
"animal_id", -- Unique number assigned to each animal when their record is created in the database.
"activity_sequence", -- Sequence starts with 1 usually then a follow up sequence is created until activity is completed.
"impound_number", -- Unique number assigned to each impound performed by DAS staff; each impound can include multiple animals.
"outcome_time", -- Time the animal was outcomed by DAS / left DAS' care.
"outcome_subtype", -- Additional details on the outcome of the animal used primiarly for outcome trend analysis.
"outcome_type", -- Final outcome of the animal if they are no longer under the care of DAS at the time of the report.
"activity_number", -- Unique number assigned to an activity related to a service request.
"census_tract", -- Government census tract in which the action was located.
"outcome_date", -- Date the animal was outcomed by DAS / left DAS' care.
"additional_information", -- Additional staff notes.
"shelter_stay" -- The length of stay an animal has been in the shelter.
FROM
"dallasopendata/daily-animals-outcome-8exr-czrc:latest"."daily_animals_outcome"
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/daily-animals-outcome-8exr-czrc 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/daily-animals-outcome-8exr-czrc: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/daily-animals-outcome-8exr-czrcCheckout 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/daily-animals-outcome-8exr-czrc:latestThis will download all the objects for the latest tag of dallasopendata/daily-animals-outcome-8exr-czrc 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/daily-animals-outcome-8exr-czrc: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/daily-animals-outcome-8exr-czrc: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/daily-animals-outcome-8exr-czrc is just another Postgres schema.