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 sonoma_county_sheriffs_office_event_data table in this repository, by referencing it like:
"sonomacounty-ca-gov/sonoma-county-sheriffs-office-event-data-bpq8-s7gr:latest"."sonoma_county_sheriffs_office_event_data"or in a full query, like:
SELECT
":id", -- Socrata column ID
"id", -- Sequential unique identifier, primary key.
"location_address",
"location", -- Geographic location of the reported nearest intersection. Entry of coordinates "(0,0)" indicates that the system was unable to identify the geographic location of the event.
"disposition", -- Disposition of the event as determined by the responding deputy or dispatcher. Disposition is not required data for event data so there are occasionally blanks in this field.
"nature_description", -- Nature of the event that was reported to dispatch.
"nature_code", -- Nature code for event, abbreviated version of nature description.
"date_time", -- Date and time event was created in the dispatch system.
"disposition_code", -- Abbreviated version of the event disposition. Disposition is not required data for event data so there are occasionally blanks in this field.
"incident_num", -- Incident number if a incident report was written by the responding deputy. This data corresponds to Sonoma County Sheriff's Office Incident data based upon incident number and agency.
"event_num", -- Sequential event number assigned by the dispatch system.
"address", -- Closest intersection to the location where the event was reported. Intersections are used to protect victims identities. Blank data in this field indicates that the system could not find the nearest intersection to the location of the event.
"event_source", -- Source from which the event was created either a call to our dispatch center or an event initiated by a deputy during the course of their work.
"agency", -- Agency code of responding agency
"agency_name", -- Full name of responding agency
"location_zip",
"beat_zone", -- Geographic area where the event occurred, the Sheriff's jurisdiction is currently divided into six zones. Windsor and Sonoma do not use beats/zones. Blanks data in this field indicates the system was unable to identify the zone/beat where the event occurred.
"location_state",
"location_city",
":@computed_region_xw9s_pz78",
":@computed_region_dig5_f3vy"
FROM
"sonomacounty-ca-gov/sonoma-county-sheriffs-office-event-data-bpq8-s7gr:latest"."sonoma_county_sheriffs_office_event_data"
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 sonomacounty-ca-gov/sonoma-county-sheriffs-office-event-data-bpq8-s7gr 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 sonomacounty-ca-gov/sonoma-county-sheriffs-office-event-data-bpq8-s7gr: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 sonomacounty-ca-gov/sonoma-county-sheriffs-office-event-data-bpq8-s7grCheckout 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 sonomacounty-ca-gov/sonoma-county-sheriffs-office-event-data-bpq8-s7gr:latestThis will download all the objects for the latest tag of sonomacounty-ca-gov/sonoma-county-sheriffs-office-event-data-bpq8-s7gr 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 sonomacounty-ca-gov/sonoma-county-sheriffs-office-event-data-bpq8-s7gr: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 sonomacounty-ca-gov/sonoma-county-sheriffs-office-event-data-bpq8-s7gr: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, sonomacounty-ca-gov/sonoma-county-sheriffs-office-event-data-bpq8-s7gr is just another Postgres schema.