wa-gov/watershed-health-monitoring-excess-sediment-in-63h7-zpny
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Query the Data Delivery Network

Query the DDN

The 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 procotol. 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 watershed_health_monitoring_excess_sediment_in table in this repository, by referencing it like:

"wa-gov/watershed-health-monitoring-excess-sediment-in-63h7-zpny:latest"."watershed_health_monitoring_excess_sediment_in"

or in a full query, like:

SELECT 
    ":id", -- Socrata column ID
    "survey", -- Designates which monitoring pass through the state. 1 = 2009 to 2012; 2 = 2013 to 2019
    "abv_18_5", -- This value was calculated as follows: PCT SandFines minus 18.5. Calculation of PCT_SandFines is described in the Habitat Data Dictionary: https://fortress.wa.gov/ecy/publications/SummaryPages/1303033.html
    "site_id", -- Unique site identifier from the Washington Master Sample: https://fortress.wa.gov/ecy/gispublic/DataDownload/EAP_ENV_MasterSample.htm
    "salmonopt", -- This field designates whether the observed percentage of sand and fines exceeds the optimum (18.5%) for sediment-sensitive salmonids. See Table 2 of Bryce et al: https://www.pnamp.org/sites/default/files/BryceSAFN-JNABS-4-12-2010.pdf . We have added 5.5 %  to Bryce's 13% optimum becuase Watershed Health measures the bankfull channel rather than the wetted channel that the Bryce values describe. Watershed Health data from the bankfull channel show (in a linear, 1:1 fashion)  5.5 % more sand and fines when compared to wetted stations from the same dataset.
    "asw", -- Adjusted spatial weights calculated using the US EPA algorithm spsurvey. Weights indicate how many kiometers of stream that the random sample site respresents within the given survey. Weights were calculated for all radnom sites in the Watershed Health Monitoring surveys, regardless of whether salmonid habitat or not.
    "x_pct_sandfines", -- This is the average (for 1 or 2 values) of PCT SandFines as reported by the Watershed Health Monitoring Database: https://ecology.wa.gov/Research-Data/Monitoring-assessment/River-stream-monitoring/Habitat-monitoring/Watershed-health .  PCT_SandFines is described in the Habitat Data Dictionary: https://fortress.wa.gov/ecy/publications/SummaryPages/1303033.html
    "col", -- Color = suggested color for charts
    "salmon_rr", -- Salmon Revovery Region from the Washington Master Sample: https://fortress.wa.gov/ecy/gispublic/DataDownload/EAP_ENV_MasterSample.htm
    "salmonid_habitat", -- 1 indicates that the stream site is occupied by salmon or trout at some time during the year. This is based on Watershed Health Moniroring sampling results or Washington Department of Fish and Wildlife maps: see http://apps.wdfw.wa.gov/phsontheweb/
    "year", -- Year of sampling
    "ppn_km_salmsed" -- Proportion of all salmonid km in the survey (excluding those without sediment data)
FROM
    "wa-gov/watershed-health-monitoring-excess-sediment-in-63h7-zpny:latest"."watershed_health_monitoring_excess_sediment_in"
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/watershed-health-monitoring-excess-sediment-in-63h7-zpny with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.wa.gov. When you querywa-gov/watershed-health-monitoring-excess-sediment-in-63h7-zpny:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).

We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.

Query Your Local Engine

Install Splitgraph Locally
bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"
 

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 (like this repository), 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, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.

Mounting Data

This repository is an external repository. It's not hosted by Splitgraph. It is hosted by data.wa.gov, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.

First, install Splitgraph if you haven't already.

Mount the table with sgr mount

sgr mount socrata \
  "wa-gov/watershed-health-monitoring-excess-sediment-in-63h7-zpny" \
  --handler-options '{
    "domain": "data.wa.gov",
    "tables": {
        "watershed_health_monitoring_excess_sediment_in": "63h7-zpny"
    }
}'

That's it! Now you can query the data in the mounted table like any other Postgres table.

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/watershed-health-monitoring-excess-sediment-in-63h7-zpny is just another Postgres schema.

Related Documentation: