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 building_code_scofflaw_list table in this repository, by referencing it like:
"cityofchicago/building-code-scofflaw-list-crg5-4zyp:latest"."building_code_scofflaw_list"or in a full query, like:
SELECT
":id", -- Socrata column ID
"record_id", -- A unique identifier for the record. A record is considered an updated version of a previously existing record if it has the same CIRCUIT COURT CASE NUMBER and BUILDING LIST DATE. This will most commonly happen when the DEFENDANT OWNER value is added a month after the record is first published.
"address", -- Primary address of building (used for geocoding and other geographic information).
"ward", -- The number of the ward (city council district) as of the building list date
"defendant_owner", -- A list of the building owners who are named as defendants in the code enforcement proceeding as of the owner list date. Owners are listed one month after the building list date.
":@computed_region_awaf_s7ux", -- This column was automatically created in order to record in what polygon from the dataset 'Historical Wards 2003-2015' (awaf-s7ux) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"x_coordinate", -- The east-west coordinate defined in feet, based on NAD 83 - State Plane Eastern IL
"latitude", -- The latitude of the building (based on the ADDRESS column).
"location", -- The location of the building (based on the ADDRESS column) in a format that allows for mapping and other geographic analysis on this portal.
":@computed_region_6mkv_f3dw", -- This column was automatically created in order to record in what polygon from the dataset 'Zip Codes' (6mkv-f3dw) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_vrxf_vc4k", -- This column was automatically created in order to record in what polygon from the dataset 'Community Areas' (vrxf-vc4k) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"building_list_date", -- The date of the building list for this record.
"y_coordinate", -- The north-south coordinate defined in feet, based on NAD 83 - State Plane Eastern IL
":@computed_region_rpca_8um6", -- This column was automatically created in order to record in what polygon from the dataset 'Boundaries - ZIP Codes' (rpca-8um6) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"circuit_court_case_number", -- Case number for the Cook County Circuit Court
":@computed_region_bdys_3d7i", -- This column was automatically created in order to record in what polygon from the dataset 'Census Tracts' (bdys-3d7i) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_43wa_7qmu", -- This column was automatically created in order to record in what polygon from the dataset 'Wards' (43wa-7qmu) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"longitude", -- The longitude of the building (based on the ADDRESS column).
"secondary_address", -- An alternative address that could be used to describe building location. This was not used for any geocoding.
"tertiary_address", -- An alternative address that could be used to describe building location. This was not used for any geocoding.
"owner_list_date", -- The date of the owner list for this record.
"community_area", -- The name of the community area. For context, see: http://en.wikipedia.org/wiki/Community_areas_in_Chicago
"community_area_number" -- The ID that corresponds with COMMUNITY AREA in this dataset. For context, see: http://en.wikipedia.org/wiki/Community_areas_in_Chicago
FROM
"cityofchicago/building-code-scofflaw-list-crg5-4zyp:latest"."building_code_scofflaw_list"
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 cityofchicago/building-code-scofflaw-list-crg5-4zyp with SQL in under 60 seconds.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.cityofchicago.org. When you querycityofchicago/building-code-scofflaw-list-crg5-4zyp: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)"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 data.cityofchicago.org, 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 \
"cityofchicago/building-code-scofflaw-list-crg5-4zyp" \
--handler-options '{
"domain": "data.cityofchicago.org",
"tables": {
"building_code_scofflaw_list": "crg5-4zyp"
}
}'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, cityofchicago/building-code-scofflaw-list-crg5-4zyp is just another Postgres schema.