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 mta_subway_hourly_ridership_beginning_february table in this repository, by referencing it like:
"ny-gov/mta-subway-hourly-ridership-beginning-february-wujg-7c2s:latest"."mta_subway_hourly_ridership_beginning_february"or in a full query, like:
SELECT
":id", -- Socrata column ID
"transit_timestamp", -- Timestamp payment took place in local time. All transactions here are rounded down to the nearest hour. For example, a swipe that took place at 1:37pm will be reported as having taken place at 1pm.
"transit_mode", -- Distinguishes between the subway, Staten Island Railway, and the Roosevelt Island Tram
"station_complex_id", -- A unique identifier for station complexes
"station_complex", -- The subway complex where an entry swipe or tap took place. Large subway complexes, such as Times Square and Fulton Center, may contain multiple subway lines. The subway complex name includes the routes that stop at the complex in parenthesis, such as Zerega Av (6).
"borough", -- Represents one of the boroughs of New York City serviced by the subway system (Bronx, Brooklyn, Manhattan, Queens).
"payment_method", -- Specifies whether the payment method used to enter was from OMNY or MetroCard.
"fare_class_category", -- The class of fare payment used for the trip. The consolidated categories are: • MetroCard – Fair Fare; • MetroCard – Full Fare; • MetroCard – Other; • MetroCard – Senior & Disability; • MetroCard – Students; • MetroCard – Unlimited 30-Day; • MetroCard – Unlimited 7-Day; • OMNY – Full Fare; • OMNY – Other; • OMNY – Seniors & Disabilities
"ridership", -- Total number of riders that entered a subway complex via OMNY or MetroCard at the specific hour and for that specific fare type.
"transfers", -- Number of individuals who entered a subway complex via a free bus-to-subway, or free out-of-network transfer. This represents a subset of total ridership, meaning that these transfers are already included in the preceding ridership column. Transfers that take place within a subway complex (e.g., individuals transferring from the 2 to the 4 train within Atlantic Avenue) are not captured here.
"latitude", -- Latitude for the specified subway complex
"longitude", -- Longitude for the specified subway complex
"georeference", -- Open Data platform-generated geocoding information from supplied address components. Point-type location is the centroid of the address components provided and does not reflect a specific address if the street address component is not provided. Point-type location is supplied in "POINT (<geocoded longitude> <geocoded latitude>)" format.
":@computed_region_kjdx_g34t", -- This column was automatically created in order to record in what polygon from the dataset 'Counties' (kjdx-g34t) the point in column 'georeference' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_yamh_8v7k", -- This column was automatically created in order to record in what polygon from the dataset 'NYS Municipal Boundaries' (yamh-8v7k) the point in column 'georeference' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_wbg7_3whc" -- This column was automatically created in order to record in what polygon from the dataset 'New York Zip Codes' (wbg7-3whc) the point in column 'georeference' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
FROM
"ny-gov/mta-subway-hourly-ridership-beginning-february-wujg-7c2s:latest"."mta_subway_hourly_ridership_beginning_february"
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 ny-gov/mta-subway-hourly-ridership-beginning-february-wujg-7c2s 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 ny-gov/mta-subway-hourly-ridership-beginning-february-wujg-7c2s: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 ny-gov/mta-subway-hourly-ridership-beginning-february-wujg-7c2sCheckout 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 ny-gov/mta-subway-hourly-ridership-beginning-february-wujg-7c2s:latestThis will download all the objects for the latest tag of ny-gov/mta-subway-hourly-ridership-beginning-february-wujg-7c2s 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 ny-gov/mta-subway-hourly-ridership-beginning-february-wujg-7c2s: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 ny-gov/mta-subway-hourly-ridership-beginning-february-wujg-7c2s: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, ny-gov/mta-subway-hourly-ridership-beginning-february-wujg-7c2s is just another Postgres schema.