edmonton-ca/20122014-graffiti-audit-2q9r-ang2
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Query the Data Delivery Network

Query the DDN

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 20122014_graffiti_audit table in this repository, by referencing it like:

"edmonton-ca/20122014-graffiti-audit-2q9r-ang2:latest"."20122014_graffiti_audit"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "location", -- Spatial location of graffiti markings
    "longitude", -- Longitude coordinate for graffiti marking
    "latitude", -- Latitude coordinate for graffiti marking
    "lir_value", -- Intensity value rating for graffiti vandalism at each location. LIR rates nine characteristics (size, colour, complexity, artistic, visibility, longevity, access, surface and reoccurrence) on a scale of one to five where 1 is low and five is high.
    "number_of_tags", -- The number of tags found at the specific location where the graffiti was found.
    "size", -- Predetermined categories to identify the variations in the size of the graffiti. 
    "target", -- The object/structure upon which the graffiti was placed. 
    "category", -- Predefined categories to describe the visual appearance of the graffiti.
    "site_type", -- The type of area within the neighbourhood where the graffiti was found. Hot spot areas were those within a top 20 neighbourhood where significant reports of graffiti vandalism were recorded in the City’s data. A “random” area, the same approximate size.
    "neighbourhood", -- The neighbourhoods within Edmonton where the audit was conducted.
    "year", -- Year of Incidence
    "geometry_point",
    "lir_group_value", -- LIR Value category grouping.
    "location_zip",
    "location_state",
    "location_city",
    ":@computed_region_3z8d_fhyb",
    ":@computed_region_da6r_6gkw",
    ":@computed_region_mnf4_kaez",
    ":@computed_region_5jki_au6x",
    ":@computed_region_izdr_ja4x",
    ":@computed_region_ecxu_fw7u",
    ":@computed_region_7ccj_gre3",
    "location_address"
FROM
    "edmonton-ca/20122014-graffiti-audit-2q9r-ang2:latest"."20122014_graffiti_audit"
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 edmonton-ca/20122014-graffiti-audit-2q9r-ang2 with SQL in under 60 seconds.

Query Your Local Engine

Install Splitgraph Locally
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 edmonton-ca/20122014-graffiti-audit-2q9r-ang2: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 edmonton-ca/20122014-graffiti-audit-2q9r-ang2

Checkout 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 edmonton-ca/20122014-graffiti-audit-2q9r-ang2:latest

This will download all the objects for the latest tag of edmonton-ca/20122014-graffiti-audit-2q9r-ang2 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 edmonton-ca/20122014-graffiti-audit-2q9r-ang2: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 edmonton-ca/20122014-graffiti-audit-2q9r-ang2:latest

This 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, edmonton-ca/20122014-graffiti-audit-2q9r-ang2 is just another Postgres schema.

Related Documentation:

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