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 predict_event_crop_production table in this repository, by referencing it like:
"usaid-gov/predict-event-crop-production-ssbk-prpd:latest"."predict_event_crop_production"or in a full query, like:
SELECT
":id", -- Socrata column ID
"nhpincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of non-human primates feeding on or visiting crop fields or stored crops.
"cropsstorednotobserved", -- The types of harvested crops that are not observed being stored.
"cropsstoredoutsidesacks", -- The types of harvested crops stored outside in sacks.
"cropsstoredindwellings", -- The types of crops that are stored in the dwellings.
"goatfertilizer", -- The types of goat/sheep fertilizer used.
"cropraidingpestprevention", -- The observed practices or signs of practices used for preventing crop raiding and pest damage.
"purposelargescaleproduction", -- The types of crops grown for large scale production.
"country", -- The name of the country where the sampling occurred. Is auto assigned by system by linking to the event ID.
"cattleincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of cattle/buffalo feeding on or visiting crop fields or stored crops?
"goatsincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of goats/sheep feeding on or visiting crop fields or stored crops.
"batsincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of bats feeding on or visiting crop fields or stored crops.
"rodentsincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of rodents/shrews feeding on or visiting crop fields or stored crops.
"cropsstoredsilogranary", -- The types of harvested crops that are stored in a silo or granary.
"cropstorageatsite", -- Are crops stored at this site after harvest?
"batguanofertilizer", -- The types of bat guano fertilizer used.
"cattlefertilizer", -- The types of cattle/buffalo fertilizer used.
"camelfertilizer", -- The types of camel fertilizer used.
"fertilizersused", -- The types of fertilizers used on the crops.
"purposevillagelevel", -- The types of crops grown for village use.
"purposehouseholdlevel", -- The types of crops grown for household use.
"predict_eventid", -- The numeric key to the event which the animal crop data belongs to. This can be used to link this dataset to the Site/EventCharacterization dataset.
"birdguanofertilizer", -- The types of bird guano fertilizer used.
"swineincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of rodents/shrews feeding on or visiting crop fields or stored crops.
"poultryincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of poultry/other fowl feeding on or visiting crop fields or stored crops.
"cropsplanted", -- Which crops are planted on the site?
"elephantsincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of elephants feeding on or visiting crop fields or stored crops.
"ungulatesincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of ungulates feeding on or visiting crop fields or stored crops.
"cropsstoredother", -- The types of harvested crops that are stored in other ways.
"swinefertilizer", -- The types of swine fertilizer used.
"poultyfertilizer", -- The types of poultry/other fowl fertilizer used.
"catsincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of cats feeding on or visiting crop fields or stored crops?
"carnivoresincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of carnivores feeding on or visiting crop fields or stored crops.
"camelsincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of camels feeding on or visiting crop fields or stored crops.
"typeofsystem", -- The types of crop production systems.
"dogsincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of dogs feeding on or visiting crop fields or stored crops?
"horsesincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of Horses feeding on or visiting crop fields or stored crops?
"pangolinsincrops", -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of pangolins feeding on or visiting crop fields or stored crops.
"birdsincrops" -- The location(s) where there is evidence (direct observation, signs, scat, tracks) of birds feeding on or visiting crop fields or stored crops.
FROM
"usaid-gov/predict-event-crop-production-ssbk-prpd:latest"."predict_event_crop_production"
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 usaid-gov/predict-event-crop-production-ssbk-prpd 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 usaid-gov/predict-event-crop-production-ssbk-prpd: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 usaid-gov/predict-event-crop-production-ssbk-prpdCheckout 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 usaid-gov/predict-event-crop-production-ssbk-prpd:latestThis will download all the objects for the latest tag of usaid-gov/predict-event-crop-production-ssbk-prpd 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 usaid-gov/predict-event-crop-production-ssbk-prpd: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 usaid-gov/predict-event-crop-production-ssbk-prpd: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, usaid-gov/predict-event-crop-production-ssbk-prpd is just another Postgres schema.