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 lapd_ripa_ab_953_stop_incident_details_from_712018 table in this repository, by referencing it like:
"lacity/lapd-ripa-ab-953-stop-incident-details-from-712018-5gp9-8nrb:latest"."lapd_ripa_ab_953_stop_incident_details_from_712018"or in a full query, like:
SELECT
":id", -- Socrata column ID
"officer_assignment_type", -- Officer assignment type refers to the type of assignment that Officer 1 was assigned at the time of the stop.
"school_name", -- Name of the K-12 public school where the stop occured. (Required if incident happened at a K-12 School)
"area_name", -- Name of the Geographic Area within the LAPD where the STOP Incident occurred. The LAPD has 21 Geographical Areas.
"area_id", -- Area ID where the Stop Incident Occurred. The LAPD has 21 Community Police Stations referred to as Geographic Areas within the department. These Geographic Areas are sequentially numbered from 1-21.
"reporting_district", -- Four-digit code that represents a sub-area within a Geographic Area. All arrest records reference the "RD" that it occurred in for statistical comparisons. Find LAPD Reporting Districts on the LA City GeoHub at http://geohub.lacity.org/datasets/lapd-reporting-districts?geometry=-121.023%2C33.621%2C-115.797%2C34.418
"duration_in_min", -- Approximate length of the stop measured from the time of first contact with an officer in the stop to the time the person is either free to leave or taken in to custody.
"stop_date", -- (MM/DD/YYYY) Year, month, and day that stop occurred
"officer_2_id", -- The unique employee identifier assigned to each sworn officer within LAPD
"stop_time", -- Approximate time of the stop, in 24 hour military time
"persons_contacted", -- Count of people contacted during a single stop incident. (one or more)
"k12_incident", -- Did the stop incident occur at a K-12 school?
"officer_1_id", -- The unique employee identifier assigned to each sworn officer within LAPD
"bureau_name", -- Name of Bureaus where The Stop Incident occurred. The LAPD has 4 Bureaus which contain Geographic Areas.
"action_taken", -- Identifies whether there was an action taken towards any of the involved persons by the Officer during the stop?
"officer_div", -- Division LAPD Officer 1 is assigned to when the stop occurred.
"stop_number", -- Unique identifier for the incident. (an incident can include multiple persons)
"call_for_service" -- Was stop made in response to a call for service, radio call, or dispatch?
FROM
"lacity/lapd-ripa-ab-953-stop-incident-details-from-712018-5gp9-8nrb:latest"."lapd_ripa_ab_953_stop_incident_details_from_712018"
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 lacity/lapd-ripa-ab-953-stop-incident-details-from-712018-5gp9-8nrb 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 lacity/lapd-ripa-ab-953-stop-incident-details-from-712018-5gp9-8nrb: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 lacity/lapd-ripa-ab-953-stop-incident-details-from-712018-5gp9-8nrbCheckout 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 lacity/lapd-ripa-ab-953-stop-incident-details-from-712018-5gp9-8nrb:latestThis will download all the objects for the latest tag of lacity/lapd-ripa-ab-953-stop-incident-details-from-712018-5gp9-8nrb 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 lacity/lapd-ripa-ab-953-stop-incident-details-from-712018-5gp9-8nrb: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 lacity/lapd-ripa-ab-953-stop-incident-details-from-712018-5gp9-8nrb: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, lacity/lapd-ripa-ab-953-stop-incident-details-from-712018-5gp9-8nrb is just another Postgres schema.