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 wdfw_lower_columbia_salmon_population_and_hatchery table in this repository, by referencing it like:
"wa-gov/wdfw-lower-columbia-salmon-population-and-hatchery-uud8-pyb3:latest"."wdfw_lower_columbia_salmon_population_and_hatchery"or in a full query, like:
SELECT
":id", -- Socrata column ID
"tsa", -- final total (hatchery and wild) spawner abundance used for this population and year (could be with or without jacks; look at TSAij and TSAej to determine)
"tsaij", -- Total spawner abundance, including jacks.
"popfit", -- Categorization of how the geographic extent of the NOSA/escapement estimate corresponds to the definition of the population. "Same"=Estimate represents the entire population, the whole population, and nothing but the population. "Portion"=Estimate represents a portion of the population. "Multiple"=Estimate is from more than just one population.
"tsaej", -- Total spawner abundance, excluding jacks.
"nosa", -- final natural origin spawner abundance used for this population and year (could be with or without jacks; look at NOSAij and NOSAej to determine)
"total_releases", -- total hatchery juveniles released in watershed
"phob", -- proportion of hatchery origin broodstock used to produce juveniles released (including jacks)
"species", -- Common name of the taxon of fish.
"nosaij", -- Including jacks, point estimate for NOSA or natural origin escapement. EstimateType column determines NOSA/escapement.
"pnos", -- final proportion natural origin spawners used for this population and year (could be with or without jacks; look at pHOSij and pHOSej to determine)
"nosaej", -- Excluding jacks, point estimate for NOSA or natural origin escapement. EstimateType column determines NOSA/escapement.
"brood_year", -- Year in which adults spawned (pHOS, NOSA,TSA) or egg takes occurred (pNOB,pHOB)
"phosij", -- Point estimate for the proportion of fish spawning naturally, including jacks, that are hatchery origin fish.
"esa_population_name", -- Population name used by NMFS.
"phosej", -- Point estimate for the proportion of fish spawning naturally, excluding jacks, that are hatchery origin fish.
"pni", -- proportion natural influence in broodstock and wild population for integrated programs (a proxy for fitness relative to an all-wild population
"best_value", -- When an organization provides > 1 estimate per year, this field identifies which is the org's preferred estimate.
"phos", -- final proportion hatchery origin spawners used for this population and year (could be with or without jacks; look at pHOSij and pHOSej to determine)
"pnob" -- proportion of natural origin broodstock used to produce juveniles released (including jacks)
FROM
"wa-gov/wdfw-lower-columbia-salmon-population-and-hatchery-uud8-pyb3:latest"."wdfw_lower_columbia_salmon_population_and_hatchery"
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 wa-gov/wdfw-lower-columbia-salmon-population-and-hatchery-uud8-pyb3 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 wa-gov/wdfw-lower-columbia-salmon-population-and-hatchery-uud8-pyb3: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 wa-gov/wdfw-lower-columbia-salmon-population-and-hatchery-uud8-pyb3Checkout 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 wa-gov/wdfw-lower-columbia-salmon-population-and-hatchery-uud8-pyb3:latestThis will download all the objects for the latest tag of wa-gov/wdfw-lower-columbia-salmon-population-and-hatchery-uud8-pyb3 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 wa-gov/wdfw-lower-columbia-salmon-population-and-hatchery-uud8-pyb3: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 wa-gov/wdfw-lower-columbia-salmon-population-and-hatchery-uud8-pyb3: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, wa-gov/wdfw-lower-columbia-salmon-population-and-hatchery-uud8-pyb3 is just another Postgres schema.