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 local_law_8_of_2020_complaints_of_illegal_parking table in this repository, by referencing it like:
"cityofnewyork-us/local-law-8-of-2020-complaints-of-illegal-parking-cwy2-px8b:latest"."local_law_8_of_2020_complaints_of_illegal_parking"or in a full query, like:
SELECT
":id", -- Socrata column ID
"unique_key", -- Unique identifier of a Service Request (SR) in the open data set
"resolution_action_updated", -- Date when responding agency last updated the SR
"closed_date", -- Date SR was closed by responding agency
"city", -- City of the incident location provided by geo validation
"created_date", -- Date SR was created
"location", -- Combination of the geo based lat & long of the incident location
"x_coordinate_state_plane", -- Geo validated, X coordinate of the incident location
"cross_street_1", -- First cross street based on the geo validated incident location
"cross_street_2", -- Second cross street based on the geo validated incident location
"longitude", -- Geo based long of the incident location
"y_coordinate_state_plane", -- Geo validated, Y coordinate of the incident location
"landmark", -- If the incident location is identified as a Landmark the name of the landmark will display here
"intersection_street_2", -- Second intersecting street based on geo validated incident location
"additional_details", -- This is associated to the Descriptor, and provides further detail on the incident or condition. Additional Details values are dependent on the Descriptor, and are not always required in SR.
"open_data_channel_type", -- Indicates how the SR was submitted to 311, i.e. By Phone, Online, Mobile, Other or Unknown
"council_district", -- Provided by geo validation
"bbl", -- Borough Block and Lot, provided by geo validation. Parcel number to identify the location of location of buildings and properties in NYC
"status", -- Status of SR submitted
"community_board", -- Provided by geo validation
"intersection_street_1", -- First intersecting street based on geo validated incident location
"location_type", -- Describes the type of location used in the address information
"address_type", -- Type of incident location information available
":@computed_region_efsh_h5xi",
"resolution_description", -- Describes the last action taken on the SR by the responding agency. May describe next or future steps
"due_date", -- Date when responding agency is expected to update the SR. This is based on the Complaint Type and internal Service Level Agreements (SLAs)
":@computed_region_f5dn_yrer",
"agency", -- Acronym of responding City Government Agency
"descriptor", -- This is associated to the Complaint Type, and provides further detail on the incident or condition. Descriptor values are dependent on the Complaint Type, and are not always required in SR. Descriptor may have a corresponding Additional Details (below) or may stand alone.
"latitude", -- Geo based lat of the incident location
"complaint_type", -- This is the first level of a hierarchy identifying the topic of the incident or condition. Complaint Type may have a corresponding Descriptor (below) or may stand alone.
":@computed_region_yeji_bk3q",
"street_name", -- Street name of incident address provided by the submitter
"borough", -- Provided by the submitter and confirmed by geo validation
"agency_name", -- Full Agency name of responding City Government Agency
":@computed_region_92fq_4b7q",
":@computed_region_sbqj_enih",
"incident_zip", -- Incident location zip code, provided by geo validation
"incident_address" -- House number of incident address provided by submitter
FROM
"cityofnewyork-us/local-law-8-of-2020-complaints-of-illegal-parking-cwy2-px8b:latest"."local_law_8_of_2020_complaints_of_illegal_parking"
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 cityofnewyork-us/local-law-8-of-2020-complaints-of-illegal-parking-cwy2-px8b 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 cityofnewyork-us/local-law-8-of-2020-complaints-of-illegal-parking-cwy2-px8b: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 cityofnewyork-us/local-law-8-of-2020-complaints-of-illegal-parking-cwy2-px8bCheckout 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 cityofnewyork-us/local-law-8-of-2020-complaints-of-illegal-parking-cwy2-px8b:latestThis will download all the objects for the latest tag of cityofnewyork-us/local-law-8-of-2020-complaints-of-illegal-parking-cwy2-px8b 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 cityofnewyork-us/local-law-8-of-2020-complaints-of-illegal-parking-cwy2-px8b: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 cityofnewyork-us/local-law-8-of-2020-complaints-of-illegal-parking-cwy2-px8b: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, cityofnewyork-us/local-law-8-of-2020-complaints-of-illegal-parking-cwy2-px8b is just another Postgres schema.