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 tree_inventory table in this repository, by referencing it like:
"citydata-mesaaz-gov/tree-inventory-jcys-68y7:latest"."tree_inventory"or in a full query, like:
SELECT
":id", -- Socrata column ID
"longitude", -- Longitude of tree
"location", -- Identifies the vegetation location by roadway and roadway boundaries (i.e., "roadway ~ N/E boundary street - S/W boundary street").
":@computed_region_v3p2_n653", -- This column was automatically created in order to record in what polygon from the dataset 'Arizona Postal Code Boundaries v1.0' (v3p2-n653) the point in column 'geolocation' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"com_name",
"locdesc", -- Identifies the general location of the identified vegetation using roadway names and direction as a point of reference.
":@computed_region_by5m_u9f6", -- This column was automatically created in order to record in what polygon from the dataset 'Council District vJun2022' (by5m-u9f6) the point in column 'geolocation' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_b7fy_h722", -- This column was automatically created in order to record in what polygon from the dataset 'City Boundary' (b7fy-h722) the point in column 'geolocation' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"comment", -- Provides additional information regarding the identified vegetation, if applicable.
"legacyid", -- Unique ID created by the City of Mesa for each vegetation site in the landscape areas maintained by the City of Mesa.
"inventoryid", -- Unique Tree ID assigned by source system
":@computed_region_5spd_7gy6",
":@computed_region_c54k_jm6w",
":@computed_region_yvgd_jnii", -- This column was automatically created in order to record in what polygon from the dataset 'Hexagon 8th Square Mile Project' (yvgd-jnii) the point in column 'geolocation_2' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"modified_by", -- Computer-generated unique ID that identifies the person who edited or modified the attribute data
"symbol_type", -- Indicates the attribute symbol used to identify the vegetation on the map based on owner and vegetation category.
"rowid", -- Unique row identifier
"created_by", -- Computer-generated unique ID that identifies the person who added the attribute data
"created_year", -- Year associated with created date
"created_month", -- Month name associated with created date
"geolocation_2", -- Geocoded location of tree
"facilityid", -- A computer-generated unique identifier used for Cityworks, the City's work management system.
"action_date", -- Indicates the date of the action performed on the identified vegetation, if applicable.
"isactive", -- Identifies the active validity of the vegetation location (i.e., No or Yes).
"action_user",
"action", -- Indicates if any action was performed on the identified vegetation (i.e., Construction, None, Remove, Repair, Replace).
"landkey", -- A unique ID created by the City of Mesa for each area of landscape that is maintained by the City of Mesa.
"objectid",
"tree_dbh", -- The "diameter at breast height" (DBH) measurement of the tree at approximately 54 inches from above ground level and is provided in units of inches.
"owner", -- Identifies the owner of the identified vegetation (i.e., PRCF, Trans, Transit, Private, Unknown).
":@computed_region_c83p_wm8i", -- This column was automatically created in order to record in what polygon from the dataset 'Mesa Census Tracts To City Boundary v1.2' (c83p-wm8i) the point in column 'geolocation' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
"veg_height", -- The measured height of the identified vegetation and is provided in 15' increments (i.e., 1' to 15', 15' to 30', '31' to 45', 46' to 60', 60'+).
"veg_category", -- Identifies the category of the vegetation (i.e., Accent, Amenity, Cactus, Groundcover, Shrub, Tree).
"street", -- Frontage address street name
"latitude", -- Latitude of tree
"modified_date", -- Indicates the date that the identified vegetation was either edited or modified in the source system.
"est_value", -- Estimated replacement value of tree
"common_name", -- The common name of the identified vegetation.
"created_date", -- Indicates the date that the identified vegetation was added into the source system.
"botanical_name" -- The name given to the tree, which is identified as "genus - species."
FROM
"citydata-mesaaz-gov/tree-inventory-jcys-68y7:latest"."tree_inventory"
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 citydata-mesaaz-gov/tree-inventory-jcys-68y7 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 citydata-mesaaz-gov/tree-inventory-jcys-68y7: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 citydata-mesaaz-gov/tree-inventory-jcys-68y7Checkout 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 citydata-mesaaz-gov/tree-inventory-jcys-68y7:latestThis will download all the objects for the latest tag of citydata-mesaaz-gov/tree-inventory-jcys-68y7 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 citydata-mesaaz-gov/tree-inventory-jcys-68y7: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 citydata-mesaaz-gov/tree-inventory-jcys-68y7: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, citydata-mesaaz-gov/tree-inventory-jcys-68y7 is just another Postgres schema.