datacatalog-cookcountyil-gov/historical-ccgisdata-parcel-2019-c5mi-ck9v
Icon for Socrata external plugin

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 historical_ccgisdata_parcel_2019 table in this repository, by referencing it like:

"datacatalog-cookcountyil-gov/historical-ccgisdata-parcel-2019-c5mi-ck9v:latest"."historical_ccgisdata_parcel_2019"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "pinu", -- the last 4 digits of the 14 digit number.  This is usually 0000, except in cases such as individual Condo units and Leasholds.  Individual units are not represented by a polygon.
    "pinp", -- The 8th, 9th & 10th digits, that represent a Clerk assigned Parcel number.
    "pinb", -- The 5th, 6th & 7th digits, represents the Block area of the Parcel as assigned by the Clerks Office.
    "pinsa", -- The 3rd & 4th digits of the parcel number, represents the subarea number (section of an Area)
    "pina", -- First 2 digits of the parcel number, represents the Area number, (Township and Range)
    "taxcode", -- A tax code is a code which identifies the combination of taxing districts which provide services and levy property taxes to each tax parcel in Cook County.
    "assessornbhd", -- Neighborhoods are based on similar characteristics such as sales and housing type. 
    "assessorbldgclass", -- Codes for classification of REAL property assigned by the Assessor.
    "censustract_geoid", -- Full Census FIPS code of the tract containing the parcel
    "unitschltaxdist", -- Name of the Unit School district the parcel falls within
    "highschltaxdist", -- Name of the High School district the parcel falls within
    "elemschltaxdist", -- Name of the elementary/middle school district the parcel falls within
    "tifdistrict", -- TIF District name, if parcel is within a TIF District
    "statesenatedistrict", -- Illinois State Senate district
    "staterepresentativedistrict", -- Illinois State Representative district
    "municipalityfips", -- Municipality FIPS code
    "municipality", -- Municipality name
    "latitude", -- Latitude of the parcel centroid (within the polygon)
    "longitude", -- Longitude of the parcel centroid (within the polygon)
    "pin10", -- 10 digit parcel number, the first 10 digits of the parcel , often called the base parcel
    "parceltype", -- 1-Baseparcel, 2-Elevated, 3-Condominium, 4-NoPIN, 5-ElevatedCondo, 6-Leasehold, 7-ElevatedLeasehold, 8-ROWOverlap, 9-CondominiumLeasehold
    "chicagoward", -- City of Chicago ward number, if parcel is within the City of Chicago
    "commissionerdistrict", -- Cook County Commissioner district
    "politicaltownship", -- Political Township name
    "congressionaldistrict", -- Congressional district
    "the_geom"
FROM
    "datacatalog-cookcountyil-gov/historical-ccgisdata-parcel-2019-c5mi-ck9v:latest"."historical_ccgisdata_parcel_2019"
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 datacatalog-cookcountyil-gov/historical-ccgisdata-parcel-2019-c5mi-ck9v with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at datacatalog.cookcountyil.gov. When you querydatacatalog-cookcountyil-gov/historical-ccgisdata-parcel-2019-c5mi-ck9v: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

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 (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 datacatalog.cookcountyil.gov, 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 \
  "datacatalog-cookcountyil-gov/historical-ccgisdata-parcel-2019-c5mi-ck9v" \
  --handler-options '{
    "domain": "datacatalog.cookcountyil.gov",
    "tables": {
        "historical_ccgisdata_parcel_2019": "c5mi-ck9v"
    }
}'

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, datacatalog-cookcountyil-gov/historical-ccgisdata-parcel-2019-c5mi-ck9v is just another Postgres schema.