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 311_service_requests_for_fiscal_year_2017_2018 table in this repository, by referencing it like:
"dallasopendata/311-service-requests-for-fiscal-year-2017-2018-p5f8-ffa7:latest"."311_service_requests_for_fiscal_year_2017_2018"or in a full query, like:
SELECT
":id", -- Socrata column ID
"prc_instance_id", -- Internal Key
"lat_long_location_address",
"lat_long_location", -- Denotes a location point on a longitude line (perpendicular to the equator) and latitude line (parallel to the equator)
"y_coordinate", -- Denotes a point where vertical (y axis) lines intersect on a map
"prc_outcome_desc", -- A brief statement describing the outcome of the request for service
"created_date", -- The date the service request was submitted
"overall_service_request_due_date", -- The date the request the department must have the service request completed by for the request to be on time. Requests completed after this date are considered late.
"city_council_district", -- The number of the City Council District where the service will take place
"location_display_name", -- The location where the service will take place
"service_request_number", -- Tracking number associated with a service request beginning with the last two digits of the year the service request was created.
"x_coordinate", -- Denotes a point where (x axis) horizontal lines intersect on a map
"closed_date", -- The date the request was completed and closed
"update_date", -- The last date an update was made to the request
"prc_type_desc", -- The type of service being requested
"res_department_desc", -- The City Department responsible to responding to the request for service
"priority_desc", -- Describes the priorty of the request (Standard, Urgent, Dispatch, etc.)
"method_received_desc", -- Describes the method the service request was received (I.E. Phone, Web, Mobile Device, etc.)
"ert_estimated_response_time", -- The estimated amount of time it will take the City Department to begin their investigation/response to the request for service
"lat_long_location_state",
"lat_long_location_zip",
":@computed_region_2f7u_b5gs", -- This column was automatically created in order to record in what polygon from the dataset 'Dallas City Limits GIS Layer' (2f7u-b5gs) the point in column 'lat_long_location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_sjyw_rtbm", -- This column was automatically created in order to record in what polygon from the dataset 'Current Council Districts' (sjyw-rtbm) the point in column 'lat_long_location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"lat_long_location_city",
"status_desc" -- Describes the status of the request for service (I.E. New, In Progress, Closed)
FROM
"dallasopendata/311-service-requests-for-fiscal-year-2017-2018-p5f8-ffa7:latest"."311_service_requests_for_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/311-service-requests-for-fiscal-year-2017-2018-p5f8-ffa7 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/311-service-requests-for-fiscal-year-2017-2018-p5f8-ffa7: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/311-service-requests-for-fiscal-year-2017-2018-p5f8-ffa7Checkout 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/311-service-requests-for-fiscal-year-2017-2018-p5f8-ffa7:latestThis will download all the objects for the latest tag of dallasopendata/311-service-requests-for-fiscal-year-2017-2018-p5f8-ffa7 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/311-service-requests-for-fiscal-year-2017-2018-p5f8-ffa7: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/311-service-requests-for-fiscal-year-2017-2018-p5f8-ffa7: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/311-service-requests-for-fiscal-year-2017-2018-p5f8-ffa7 is just another Postgres schema.