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 docked_bikeshare_ridership_by_system_year_and table in this repository, by referencing it like:
"bts-gov/docked-bikeshare-ridership-by-system-year-and-cvai-skrf:latest"."docked_bikeshare_ridership_by_system_year_and"or in a full query, like:
SELECT
":id", -- Socrata column ID
"yroverchange", -- Change in monthly value for system from 2019 value (as a fraction 0 to 1) for corresponding period (same days of week).
"assigned_month", -- Assigned month. See data description for explanation.
"year", -- Year
"sysname", -- Name of docked bikeshare system
"sum_min", -- Total trip time in minutes (divide by num_trip to get average trip time)
"the_geom",
"yroverchange_bin", -- Bin used for mapping year over change. 0 = no change or NA; -4: change>=-75%; -3: -75<change<=-50; -2: -50<change<=-25; -1: -25<change<0; 1: 0 < change < 25; 2: 25<=change<50; 3: 50<=change<75; 4: change>=75%
"datetxt", -- Text description of date range.
"datestr", -- Calendar dates included in value.
"lat", -- Approximate latitude for system area served
"lng", -- Approximate longitude for system area served
"sysorder", -- System ranked by 2019 number of trips for year and month.
"sysid", -- System id
"sysname_alt", -- Area served (system name)
"pct_ttl_num_trips", -- System trips as proportion of total trips for all docked bikeshare systems (in database) for year and month (multiple by 100 for percent).
"may_ind", -- Indicator (=1) if stay at home order still in effect during assigned month of May or if statewide phased reopening began in May (for Capital Bikeshare, DC stay at home and phased reopening dates used). Equal to zero if statewide phased reopening occurred in assigned month of April
"num_trip", -- Total number of trips
"mar_ind", -- Indicator if statewide stay at home order took effect in month (for Capital Bikeshare, DC date for stay at home order used)
"bin_num_trip", -- Bin used for mapping. 1: <2.5 percent of total trips (for systems in database) for year and month; 2: <=2.5 and <5.0; 3: <=5.0 and <10.0; 4: >=10.0 (CitiBike JC and NYC considered separate systems)
"trip_type", -- 1=started and ended at a docking station; 2=started or ended outside of a docking station; 9999=started and ended outside of a docking station (dockless trip)
"display", -- Indicator =1 if system has data value for month; zero otherwise.
":@computed_region_wbq9_i9bc",
":@computed_region_nw2e_u85y"
FROM
"bts-gov/docked-bikeshare-ridership-by-system-year-and-cvai-skrf:latest"."docked_bikeshare_ridership_by_system_year_and"
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 bts-gov/docked-bikeshare-ridership-by-system-year-and-cvai-skrf 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 bts-gov/docked-bikeshare-ridership-by-system-year-and-cvai-skrf: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 bts-gov/docked-bikeshare-ridership-by-system-year-and-cvai-skrfCheckout 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 bts-gov/docked-bikeshare-ridership-by-system-year-and-cvai-skrf:latestThis will download all the objects for the latest tag of bts-gov/docked-bikeshare-ridership-by-system-year-and-cvai-skrf 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 bts-gov/docked-bikeshare-ridership-by-system-year-and-cvai-skrf: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 bts-gov/docked-bikeshare-ridership-by-system-year-and-cvai-skrf: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, bts-gov/docked-bikeshare-ridership-by-system-year-and-cvai-skrf is just another Postgres schema.