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 fish_data_for_juvenile_pacific_salmonid_habitat table in this repository, by referencing it like:
"kingcounty-gov/fish-data-for-juvenile-pacific-salmonid-habitat-hznk-dan4:latest"."fish_data_for_juvenile_pacific_salmonid_habitat"or in a full query, like:
SELECT
":id", -- Socrata column ID
"fish_notes", -- Notes regarding individual sampled fish.
"length", -- The fork length or total length of a sampled fish, in millimeters.
"species", -- Three letter codes representing the fishes caught at each transect. A blank cell indicates that no fish were caught in that transect. Species codes: BBH = brown bullhead catfish (Ameiurus nebulosus); BCP = black crappie (Pomoxis nigromaculatus); BGS = bluegill sunfish (Lepomis macrochirus); CHK = chinook salmon (Oncorhynchus tshawytscha); CHM = chum salmon (Oncorhynchus keta); COH = coho salmon (Oncorhynchus kisutch); COT = cottids (Cottus sp); CUT = coastal cutthroat trout (Oncorhynchus clarkii clarkii); DAC = unknown dace (Rhinichthys sp); GSF = green sunfish (Lepomis cyanellus); LMB = largemouth bass (Micropterus salmoides); LMP = unknown lamprey; LND = long nose dace (Rhinichthys cataractae); LSS = large scale sucker (Catostomus macrocheilus); MIN = unknown minnow (Cyprinid); MWF = mountain whitefish (Prosopium williamsoni); NPM = northern pike minnow (Ptychocheilus oregonensis); PIN = pink salmon (Oncorhynchus gorbuscha); PMM = peamouth minnow (Mylocheilus caurinus); PSS = pumpkinseed sunfish (Lepomis gibbosus); RBT = rainbow trout (Oncorhynchus mykiss); RKB = rock bass (Ambloplites rupestris); RSS = redside shiner (Richardsonius balteatus); SPD = speckled dace (Rhinichthys osculus); STH = steelhead (Oncorhynchus mykiss); SUN = unknown sunfish (Lepomis sp); TRT = unknown trout; TSB = three-spine stickleback (Gasterosteus aculeatus); YLP = yellow perch (Perca flavescens).
"time_surveyed", -- The number of seconds that a transect was electrofished.
"transect_notes", -- Notes about sampling of individual transects.
"transect_type", -- Classification of transects as either ‘control’, ‘reference’, or ‘treatment’ for sampling of habitat restoration and bioengineered bank protection projects.
"unit_id", -- An additional identifier for each sampled transect. This identifier is not unique across sampling events and may or may not be unique within a sampling event.
"event_notes", -- Notes about individual sampling events.
"flow", -- The river flow during sampling, in cubic feet per second. For Green River sampling events, flows are from the Green River near Auburn USGS gage (#12113000). For Snoqualmie River sampling events, flows are from the Snoqualmie River near Carnation USGS gage (#12149000).
"date", -- The date sampling took place (mm/dd/yyyy).
"river", -- The name of the river where the sampling took place (Snoqualmie or Green).
"project", -- The name of the project or sampling effort.
"transect_id", -- A unique numeric identifier for each sampled transect. This identifier can be used to join the habitat dataset with the fish dataset.
"edge_type", -- The edge habitat type sampled. Either ‘armored bank’, ‘biorevetment bank’, ‘unarmored bank’, ‘bar’, ‘backwater’, or ‘side channel’.
"age", -- The estimated age classification for salmonids. 0+ = sub-yearling juvenile; 1+ = yearling or older juvenile.
"hatchery_mark", -- Whether or not salmonids had marks or fin clips indicative of hatchery origin.
"number" -- The number of fish observed. 1 if a fish was measured or > 1 if the number represents a sum of multiple fish that were counted and not individually measured. A blank cell indicates that no fish were caught in that transect.
FROM
"kingcounty-gov/fish-data-for-juvenile-pacific-salmonid-habitat-hznk-dan4:latest"."fish_data_for_juvenile_pacific_salmonid_habitat"
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/fish-data-for-juvenile-pacific-salmonid-habitat-hznk-dan4 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/fish-data-for-juvenile-pacific-salmonid-habitat-hznk-dan4: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/fish-data-for-juvenile-pacific-salmonid-habitat-hznk-dan4Checkout 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/fish-data-for-juvenile-pacific-salmonid-habitat-hznk-dan4:latestThis will download all the objects for the latest tag of kingcounty-gov/fish-data-for-juvenile-pacific-salmonid-habitat-hznk-dan4 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/fish-data-for-juvenile-pacific-salmonid-habitat-hznk-dan4: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/fish-data-for-juvenile-pacific-salmonid-habitat-hznk-dan4: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/fish-data-for-juvenile-pacific-salmonid-habitat-hznk-dan4 is just another Postgres schema.