cityofnewyork-us/1995-street-tree-census-kyad-zm4j
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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 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 1995_street_tree_census table in this repository, by referencing it like:

"cityofnewyork-us/1995-street-tree-census-kyad-zm4j:latest"."1995_street_tree_census"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "street", -- Street where address for tree is located. 
    "zip_original", -- The zip code originally geocoded to the address using LION 02A 
    "cb_new", -- Community Board geocoded to the address using Geosupport 11.4 
    "latitude", -- Latitude of point in decimal degrees as geocoded using LION 02A 
    "zip_new", -- Zip Code geocoded to the address using Geosupport 11.4 
    "censustract_2010", -- 2010 Census Tract geocoded to the address using Geosupport 11.4 
    "longitude", -- Longitude of point in decimal degrees as geocoded using LION 02A 
    "y", -- Y coordinate of point in the NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet coordinate system, geocoded using LION 02A 
    "location",
    "x", -- X coordinate of point in the NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet coordinate system, geocoded using LION 02A 
    "species", -- Species of the tree using a four-letter code, comprised of the first two letters of the genus followed by the first two letters of the species. 
    ":@computed_region_f5dn_yrer",
    ":@computed_region_92fq_4b7q",
    ":@computed_region_sbqj_enih",
    "location_city",
    "site", -- This field clarifies the position relative to the address for trees not located in the front of buildings.   
    "censusblock_2010", -- 2010 Census Block geocoded to the address using Geosupport 11.4 
    "cb_original", -- The community board originally geocoded to the address using LION 02A 
    "spc_common", -- This is the common name for the species of this tree. 
    "spc_latin", -- This is the latin/scientific name for the species of this tree. 
    "council_district",
    "bin",
    "bbl",
    "condition", -- Overall tree condition. 
    "wires", -- Whether the tree is located under utility wires 
    "support_structure", -- Whether the tree has support structures installed. 
    "location_state",
    "location_address",
    "location_zip",
    ":@computed_region_efsh_h5xi",
    "sidewalk_condition", -- Whether the tree roots have lifted the adjacent sidewalk. 
    ":@computed_region_yeji_bk3q",
    "borough", -- Borough where tree was recorded. 
    "nta_2010", -- Neighborhood Tabulation Area geocoded to the address using Geosupport 11.4 
    "recordid", -- A unique identifier for each record in the table.  Does not correspond to other datasets or identify the tree itself. 
    "diameter", -- Diameter of the tree as measured at approximately 4.5 feet from the ground. 
    "segmentid", -- LION SegmentID geocoded to the address using Geosupport 11.4 
    "address", -- Address of the tree. 
    "house_number" -- Numerical portion of the address. 
FROM
    "cityofnewyork-us/1995-street-tree-census-kyad-zm4j:latest"."1995_street_tree_census"
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/1995-street-tree-census-kyad-zm4j with SQL in under 60 seconds.

Query Your Local Engine

Install Splitgraph Locally
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/1995-street-tree-census-kyad-zm4j: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/1995-street-tree-census-kyad-zm4j

Checkout 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/1995-street-tree-census-kyad-zm4j:latest

This will download all the objects for the latest tag of cityofnewyork-us/1995-street-tree-census-kyad-zm4j 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/1995-street-tree-census-kyad-zm4j: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/1995-street-tree-census-kyad-zm4j:latest

This 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/1995-street-tree-census-kyad-zm4j is just another Postgres schema.

Related Documentation:

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