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 marine_benthic_biomass_data table in this repository, by referencing it like:
"kingcounty-gov/marine-benthic-biomass-data-h6jg-f45t:latest"."marine_benthic_biomass_data"or in a full query, like:
SELECT
":id", -- Socrata column ID
"year", -- Year that sample was collected
"crustacea_qualifier", -- Qualifier code with additional information about the biomass measurement specific to crustaceans. See above for definitions.
"crustacea_grams", -- Total wet weight mass (in grams) of all crustaceans
"annelida_grams", -- Total wet weight mass (in grams) of all annelids
"notes", -- Additional comments about the sample from taxonomist or data reviewer
"collection_date", -- Date that the sample was collected. In the format of MM/DD/YYYY
"misc_grams", -- Total wet weight mass (in grams) of all miscellaneous taxa
"sample_id", -- Unique sample identifier applied by the King County Environmental Laboratory. Note: A sample ID was not always applied to before 2002.
"sample_type", -- Indicates if sample was collected as part of a particular project or monitoring event. Some samples were collected near wastewater treatment plant outfalls (“Outfall”), while some are part of routine monitoring events to better understand conditions in the Central Basin as a whole (“Routine”).
"mollusca_grams", -- Total wet weight mass (in grams) of all molluscs
"project", -- Project number used by the King County Environmental Lab.
"approx_depth_m", -- Approximate station depth in meters
"latitude", -- Latitude of target sample coordinate in decimal degrees
"site", -- Descriptive location of sample location
"locator", -- Alphanumeric station identifier
"sample_rep_id", -- Unique identifier that combines the Locator, Year, and Field Replicate fields to identify a unique sample. Can be used to match biomass measurements to abundance data.
"longitude", -- Longitude of target sample coordinate in decimal degrees
"fieldreplicate", -- Grab number of a series of samples collected from the same date and location
"annelida_qualifier", -- Qualifier code with additional information about the biomass measurement specific to annelids. See above for definitions.
"mollusca_qualifier", -- Qualifier code with additional information about the biomass measurement specific to molluscs. See above for definitions.
"misc_qualifier" -- Qualifier code with additional information about the biomass measurement specific to miscellaneous taxa. See above for definitions.
FROM
"kingcounty-gov/marine-benthic-biomass-data-h6jg-f45t:latest"."marine_benthic_biomass_data"
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 kingcounty-gov/marine-benthic-biomass-data-h6jg-f45t 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 kingcounty-gov/marine-benthic-biomass-data-h6jg-f45t: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 kingcounty-gov/marine-benthic-biomass-data-h6jg-f45tCheckout 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 kingcounty-gov/marine-benthic-biomass-data-h6jg-f45t:latestThis will download all the objects for the latest tag of kingcounty-gov/marine-benthic-biomass-data-h6jg-f45t 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 kingcounty-gov/marine-benthic-biomass-data-h6jg-f45t: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 kingcounty-gov/marine-benthic-biomass-data-h6jg-f45t: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, kingcounty-gov/marine-benthic-biomass-data-h6jg-f45t is just another Postgres schema.