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 animal_field_data_fiscal_year_2020_2021 table in this repository, by referencing it like:
"dallasopendata/animal-field-data-fiscal-year-2020-2021-367v-7rud:latest"."animal_field_data_fiscal_year_2020_2021"or in a full query, like:
SELECT
":id", -- Socrata column ID
"user_id", -- Unique ID number assigned to the staff person who entered the record.
"complete_date", -- Date the activity was completed.
"working_date", -- Date and time officer started working on the assigned activity
"call_date_time", -- Date and time a call was received.
"cancel_reason", -- Reason an activity was cancelled.
"activity_status", -- Status used to track progress of activity.
"activity_comment", -- Additional staff notes.
"activity_result_5", -- Notes for work completed
"activity_quantity_5", -- Count of the respective result field (Activity Result 5)
"activity_result_4", -- Notes for work completed
"activity_result_2", -- Notes for work completed
"activity_quantity_1", -- Count of the respective result field (Activity Result 1)
"animal_description", -- Field in which DAS Staff can enter notes related to the appearance of the animal involved in a service request or activity.
"animal_type", -- Animal category: dog, cat, wildlife, other, etc.
"zip_code", -- The five digit zip code in which an activity or service request occurred.
"street_type", -- Identifies the kind of street on which the activity or service request occurred (ex. Drive or avenue).
"street_number", -- House, apartment, or building number at which the activity or service request occurred.
"service_request_number", -- Unique number assigned to a service request by Salesforce when it is submitted by a resident through 3-1-1.
"activity_subtype", -- Sub-category that further narrows the purpose of the activity or service request.
"officer_id", -- Unique number assigned to every Animal Service Officer in Database.
"final_citation_quantity", -- Count of the citations for Activity or SR Number
"activity_quantity_4", -- Count of the respective result field (Activity Result 4)
"street_name", -- Street on which the activity or service request occurred.
"activity_sequence", -- Sequence starts with 1 usually then a follow up sequence is created until activity is completed.
"apartment", -- The unit number at the address where a service request or activity occurred.
"animal_count", -- Number of animals involved in a specific activity or service request.
"street_direction", -- Direction of the street on which the activity or service request occurred.
"final_activity_quantity", -- Count of the respective result field (Final Activity Result)
"state", -- The state in which an activity or service request occurred.
"month", -- Month the record was created.
"final_activity_result", -- Notes for work completed
"activity_number", -- Unique number assigned to an activity related to a service request.
"activity_priority", -- Priority assigned to the activity or service request based on pre-determined guidelines reflecting the urgency of the matter.
"tag_number", -- Unique number assigned to a microchip implanted in an animal for identification.
"activity_type", -- Category that identifies the purpose of the activity or service request.
"year", -- City of Dallas Fiscal Year the record was created.
"dispatch_date", -- Date and time activity was dispatched.
"activity_result_3", -- Notes for work completed
"census_tract", -- Unique area mapped out by the federal government for purposes of the census.
"city", -- The city in which an activity or service request occurred.
"created_date_time", -- Date and time a record was created.
"activity_quantity_2", -- Count of the respective result field (Activity Result 2)
"animal_intake_id", -- Unique number assigned to each animal when their record is created in the database.
"street_address", -- Full address of the location in which the activity or service request occurred.
"activity_quantity_3", -- Count of the respective result field (Activity Result 3)
"council_district", -- Unique area mapped out by the City of Dallas that is represented by a specific City Council Office and Member.
"activity_result_1", -- Notes for work completed
"mapsco" -- Unique code used in GIS mapping to more accurately identify a location.
FROM
"dallasopendata/animal-field-data-fiscal-year-2020-2021-367v-7rud:latest"."animal_field_data_fiscal_year_2020_2021"
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/animal-field-data-fiscal-year-2020-2021-367v-7rud 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/animal-field-data-fiscal-year-2020-2021-367v-7rud: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/animal-field-data-fiscal-year-2020-2021-367v-7rudCheckout 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/animal-field-data-fiscal-year-2020-2021-367v-7rud:latestThis will download all the objects for the latest tag of dallasopendata/animal-field-data-fiscal-year-2020-2021-367v-7rud 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/animal-field-data-fiscal-year-2020-2021-367v-7rud: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/animal-field-data-fiscal-year-2020-2021-367v-7rud: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/animal-field-data-fiscal-year-2020-2021-367v-7rud is just another Postgres schema.