Query the Data Delivery Network

The 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_2016_2017 table in this repository, by referencing it like:

"dallasopendata/311-service-requests-for-fiscal-year-2016-2017-vf4v-ygb2:latest"."311_service_requests_for_fiscal_year_2016_2017"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "prc_instance_id", -- Internal Key
    "y_value", -- Denotes a point where vertical (y axis) lines intersect on a map
    "method_received_desc", -- Mode in which 311 receives the request for service
    "prc_outcome_desc", -- Documents the action taken on a service request
    "status_date", -- Date the service request was completed
    "updated_date", -- Date the service request was last updated with new information or activity
    "created_date", -- Date the service request was created
    "status_desc", -- Denotes whether a service request is currently being actively worked or has been resolved
    "over_due_on", -- The service request and all activities should be completed by the listed date
    "prc_type_desc", -- Street Type of complaint submitted by customer or service offered by the City
    "location_display_name", -- Street Address
    "service_request_number", -- Unique ID given to each documented request for a city service; the first two digits designate the last two digits of the calendar year the service request was created (e.g., 16 = 2016)
    "x_value", -- Denotes a point where (x axis) horizontal lines intersect on a map
    "config_location_value", -- Geographical borderlines for legislative representation throughout the city
    "lat_long_location_address",
    "lat_long_location_zip",
    "lat_long_location_city",
    "suspense_duration_days", -- The time allotted for the initial inspection and/or assessment. (There may be variance from department to department concerning calendar versus business days.)
    "res_department_desc", -- City Departments and Offices
    "lat_long_location", -- Denotes a location point on a longitude line (perpendicular to the equator) and latitude line (parallel to the equator)
    "lat_long_location_state",
    ":@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.
    "priority_desc" -- Denotes the precedence of how a service request will be handled
FROM
    "dallasopendata/311-service-requests-for-fiscal-year-2016-2017-vf4v-ygb2:latest"."311_service_requests_for_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/311-service-requests-for-fiscal-year-2016-2017-vf4v-ygb2 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/311-service-requests-for-fiscal-year-2016-2017-vf4v-ygb2: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)"

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/311-service-requests-for-fiscal-year-2016-2017-vf4v-ygb2" \
  --handler-options '{
    "domain": "www.dallasopendata.com",
    "tables": {
        "311_service_requests_for_fiscal_year_2016_2017": "vf4v-ygb2"
    }
}'

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/311-service-requests-for-fiscal-year-2016-2017-vf4v-ygb2 is just another Postgres schema.