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 2015_street_tree_census_tree_data table in this repository, by referencing it like:
"cityofnewyork-us/2015-street-tree-census-tree-data-uvpi-gqnh:latest"."2015_street_tree_census_tree_data"or in a full query, like:
SELECT
":id", -- Socrata column ID
"council_district",
"bin",
"boro_ct", -- This is the boro_ct identifyer for the census tract that the tree point falls into.
"cncldist", -- Council district in which tree point is located
"zipcode", -- Five-digit zipcode in which tree is located
"trnk_other", -- Indicates the presence of other trunk problems
"trnk_light", -- Indicates the presence of a trunk problem caused by lighting installed on the tree
"root_stone", -- Indicates the presence of a root problem caused by paving stones in tree bed
"steward", -- Indicates the number of unique signs of stewardship observed for this tree. Not recorded for stumps or dead trees.
"spc_latin", -- Scientific name for species, e.g. "Acer rubrum"
"tree_dbh", -- Diameter of the tree, measured at approximately 54" / 137cm above the ground. Data was collected for both living and dead trees; for stumps, use stump_diam
"created_at", -- The date tree points were collected in the census software.
"block_id", -- Identifier linking each tree to the block in the blockface table/shapefile that it is mapped on.
"tree_id", -- Unique identification number for each tree point.
"bbl",
"census_tract",
"y_sp", -- Y coordinate, in state plane. Units are feet
"x_sp", -- X coordinate, in state plane. Units are feet.
"longitude", -- Longitude of point, in decimal degrees
"latitude", -- Latitude of point, in decimal degrees
"nta_name", -- This is the NTA name corresponding to the neighborhood tabulation area from the 2010 US Census that the tree point falls into.
"nta", -- This is the NTA Code corresponding to the neighborhood tabulation area from the 2010 US Census that the tree point falls into.
"st_senate", -- State Senate District in which tree point is located
"st_assem", -- State Assembly District in which tree point is located
"cb_num", -- Community board in which tree point is located
"zip_city", -- City as derived from zipcode. This is often (but not always) the same as borough.
"address", -- Nearest estimated address to tree
"root_grate", -- Indicates the presence of a root problem caused by metal grates in tree bed
"problems",
"sidewalk", -- Indicates whether one of the sidewalk flags immediately adjacent to the tree was damaged, cracked, or lifted. Not recorded for dead trees and stumps.
"guards", -- Indicates whether a guard is present, and if the user felt it was a helpful or harmful guard. Not recorded for dead trees and stumps.
"spc_common", -- Common name for species, e.g. "red maple"
"health", -- Indicates the user's perception of tree health.
"stump_diam", -- Diameter of stump measured through the center, rounded to the nearest inch.
"state", -- All features given value 'New York'
"boroname", -- Name of borough in which tree point is located
"brch_other", -- Indicates the presence of other branch problems
"brch_shoe", -- Indicates the presence of a branch problem caused by sneakers in the branches
"brch_light", -- Indicates the presence of a branch problem caused by lights (usually string lights) or wires in the branches
"trunk_wire", -- Indicates the presence of a trunk problem caused by wires or rope wrapped around the trunk
"root_other", -- Indicates the presence of other root problems
"user_type", -- This field describes the category of user who collected this tree point's data.
"status", -- Indicates whether the tree is alive, standing dead, or a stump.
"curb_loc", -- Location of tree bed in relationship to the curb; trees are either along the curb (OnCurb) or offset from the curb (OffsetFromCurb)
"borocode" -- Code for borough in which tree point is located: 1 (Manhattan), 2 (Bronx), 3 (Brooklyn), 4 (Queens), 5 (Staten Island)
FROM
"cityofnewyork-us/2015-street-tree-census-tree-data-uvpi-gqnh:latest"."2015_street_tree_census_tree_data"
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 cityofnewyork-us/2015-street-tree-census-tree-data-uvpi-gqnh 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 cityofnewyork-us/2015-street-tree-census-tree-data-uvpi-gqnh: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 cityofnewyork-us/2015-street-tree-census-tree-data-uvpi-gqnhCheckout 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 cityofnewyork-us/2015-street-tree-census-tree-data-uvpi-gqnh:latestThis will download all the objects for the latest tag of cityofnewyork-us/2015-street-tree-census-tree-data-uvpi-gqnh 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 cityofnewyork-us/2015-street-tree-census-tree-data-uvpi-gqnh: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 cityofnewyork-us/2015-street-tree-census-tree-data-uvpi-gqnh: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, cityofnewyork-us/2015-street-tree-census-tree-data-uvpi-gqnh is just another Postgres schema.