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_nyct_customer_engagement_statistics_20172022 table in this repository, by referencing it like:
"ny-gov/mta-nyct-customer-engagement-statistics-20172022-6xy8-yzzu:latest"."mta_nyct_customer_engagement_statistics_20172022"or in a full query, like:
SELECT
":id", -- Socrata column ID
"help_point_activations", -- The total number of times a Help Point kiosk was activated per time period.
"help_point_total_wait_time", -- The total wait time (in minutes) across all Help Point calls (activations) during the time period before the calls were answered.
"social_media_mentions", -- The total number of mentions received across our relevant social media channels per time period.
"alerts_and_service_notices_2", -- The total number of alerts and service notice messages posted on electronic displays within the transit system per time period.
"alerts_and_service_notices_3", -- The total number of alerts and service notices sent by email or text to subscribed customers per time period. Note that this is tallying the unique content sent, not the total number of email and text messages sent to individual recipients.
"agency", -- The MTA agency.
"alerts_and_service_notices_4", -- The total number of elevator / escalator status alerts sent by email or text to subscribed customers per time period. Note that this is tallying the unique content sent, not the total number of email and text messages sent to individual recipients.
"social_media_responses_sent", -- The total number of responses sent across our relevant social media channels per time period.
"total_incoming_calls", -- The total number of incoming calls to our customer contact center per time period.
"month_of_year", -- The month.
"calls_answered_rate", -- The percentage of calls answered per time period.
"help_point_avg_time_to_answer", -- The average wait time per Help Point call per time period (in seconds).
"social_media_customer", -- The average social media customer satisfaction score per time period (based on a 5-point scale).
"alerts_and_service_notices", -- The total number of alerts and service notices posted on the MTA website (mta.info) per time period.
"year", -- The year.
"written_responses_sent", -- The total number of written responses sent via web, email, and paper correspondence per time period.
"written_feedback_received", -- The total number of written feedback / inquiries received via web, email, and paper correspondence per time period.
"alerts_and_service_notices_1", -- The total number of alerts and service notices posted on our Twitter channels per time period.
"alerts_and_service_notices_5", -- The total number of alerts and service notices posted on paper signs within the transit system per time period. Note that this is tallying the unique content posted, not the total number paper signs posted.
"month", -- The time period in which the customer engagement statistics are being recorded.
"calls_answered", -- The total number of calls answered per time period.
"total_wait_time_min", -- The total wait time (in minutes) across all telephone calls during the time period before the calls were answered.
"avg_time_to_answer_s" -- The average wait time per telephone call per time period (in seconds).
FROM
"ny-gov/mta-nyct-customer-engagement-statistics-20172022-6xy8-yzzu:latest"."mta_nyct_customer_engagement_statistics_20172022"
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-nyct-customer-engagement-statistics-20172022-6xy8-yzzu 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-nyct-customer-engagement-statistics-20172022-6xy8-yzzu: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-nyct-customer-engagement-statistics-20172022-6xy8-yzzuCheckout 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-nyct-customer-engagement-statistics-20172022-6xy8-yzzu:latestThis will download all the objects for the latest tag of ny-gov/mta-nyct-customer-engagement-statistics-20172022-6xy8-yzzu 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-nyct-customer-engagement-statistics-20172022-6xy8-yzzu: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-nyct-customer-engagement-statistics-20172022-6xy8-yzzu: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-nyct-customer-engagement-statistics-20172022-6xy8-yzzu is just another Postgres schema.