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_animal_shelter_data_fiscal_year_2016_2017 table in this repository, by referencing it like:
"dallasopendata/dallas-animal-shelter-data-fiscal-year-2016-2017-sjyj-ydcj:latest"."dallas_animal_shelter_data_fiscal_year_2016_2017"or in a full query, like:
SELECT
":id", -- Socrata column ID
"months", -- Added column to improve visualization of activities by month
"additional_information", -- Staffs comments and notes related to services provided to specific animals at various stages
"outcome_condition", -- The condition of the animal when the animal leaves the shelter with respect to the Asilomar Accord
"service_request_number", -- Number auto generated by 3-1-1 when a call comes in for services
"outcome_time", -- The time of the outcome of the animal
"hold_request", -- This contains any requests that have been made concerning the animal
"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
"intake_date", -- The date of impoundment or when the animal was 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
"activity_number", -- Auto generated number assigned to a field animal rescued from field incidents to the shelter
"intake_condition", -- The condition of the animal when it is impounded with respect to the Asilomar Accord
"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
"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
"animal_id", -- System auto generated number, unique to every animal impounded
"impound_number", -- Auto generated number by the Chameleon software for record keeping
"outcome_type", -- Final disposition of the animal
"animal_type", -- Intake details about the type of animal a the shelter
"receipt_number", -- Auto generated number by Chameleon when a transaction occurs, such as adoption or redemption
"outcome_date", -- The date of the outcome of the animal
"kennel_number", -- Kennel number in which the animal is housed at the shelter
"animal_origin", -- Comments related to the origin of the animal impounded
"staff_id", -- Impounding staff initials
"year", -- Added column to improve visualization of activities for specific fiscal years
"tag_type", -- Types of the tags include but may not be limited to microchip, rabies, and/or registration tag that is assign by DAS
"activity_sequence", -- This represents the sequence for the activity/call, and one activity/call can have multiple sequence.
"animal_breed", -- Animal breed or wildlife animal
"chip_status" -- Results of animal being scanned for microchip
FROM
"dallasopendata/dallas-animal-shelter-data-fiscal-year-2016-2017-sjyj-ydcj:latest"."dallas_animal_shelter_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-animal-shelter-data-fiscal-year-2016-2017-sjyj-ydcj with SQL in under 60 seconds.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at www.dallasopendata.com. When you querydallasopendata/dallas-animal-shelter-data-fiscal-year-2016-2017-sjyj-ydcj:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).
We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.
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 (like this repository), 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, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Mounting Data
This repository is an external repository. It's not hosted by Splitgraph. It is hosted by www.dallasopendata.com, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.
First, install Splitgraph if you haven't already.
Mount the table with sgr mount
sgr mount socrata \
"dallasopendata/dallas-animal-shelter-data-fiscal-year-2016-2017-sjyj-ydcj" \
--handler-options '{
"domain": "www.dallasopendata.com",
"tables": {
"dallas_animal_shelter_data_fiscal_year_2016_2017": "sjyj-ydcj"
}
}'That's it! Now you can query the data in the mounted table like any other Postgres table.
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-animal-shelter-data-fiscal-year-2016-2017-sjyj-ydcj is just another Postgres schema.