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 dob_job_application_filings table in this repository, by referencing it like:
"cityofnewyork-us/dob-job-application-filings-ic3t-wcy2:latest"."dob_job_application_filings"or in a full query, like:
SELECT
":id", -- Socrata column ID
"proposed_dwelling_units", -- Proposed Dwelling Units
"owner_sphone__", -- Owner's Phone #
"owner_s_house_number", -- House Number of Property Owner
"special_district_1", -- Special Distr 1
"special_action_status", -- Special Action Status
"existing_height", -- Existing Height
"job_type", -- Job Type, based on DOB Job Code (NB-New Building, A1, A2, A3- Alterations 1-3, SG-Sign, etc.)
"landmarked", -- L code indicates that the building has been assigned landmark status
"block", -- Tax block assigned by Department of Finance
"zoning_dist2", -- Zoning Distr 2
"doc__", -- Document Number
"house__", -- House Number of Residence or Commercial Property
"lot", -- Tax lot assigned by Department of Finance
"job_status", -- DOB Status code of job (A-Pre Filed, I-Sign Off, P- Approved, R-Permit Entire) Complete List - http://www.nyc.gov/html/dob/downloads/pdf/bisjobstatus.pdf
"efiling_filed", -- Application Filed electronically, rather than manually
"fee_status", -- Type of Fee
"state", -- State
"withdrawal_flag", -- Withdrawal Indicator
"latest_action_date", -- Latest status date
"pre__filing_date", -- Date when job is prefiled
"total_est__fee", -- Estimated fee of job
"existing_dwelling_units", -- Existing Dwelling Units
"site_fill", -- Site Fill
"owner_s_last_name", -- Last Name of property owner
"total_construction_floor_area", -- Total Construction Floor Area
"job_no_good_count", -- Job No Good Count
"gis_latitude", -- Latitude
"existingno_of_stories", -- ExistingNo. of Stories
"existing_occupancy", -- Existing Occupancy
"zoning_dist1", -- Zoning Distr 1
"pc_filed", -- Application Filed electronically, rather than manually
"standpipe", -- Standpipe Work Type? (X=Yes, Blank=No)
"sprinkler", -- Sprinkler Work Type? (X=Yes, Blank=No)
"horizontal_enlrgmt", -- Horizontal Enlrgmt
"building_type", -- 1-2-3 Family or Other
"equipment", -- Equipment Work Type? (X=Yes, Blank=No)
"fully_permitted", -- Date when job is fully permitted
"vertical_enlrgmt", -- Vertical Enlrgmt
"adult_estab", -- Adult Estab
"curb_cut", -- Curb Cut Work Type? (X=Yes, Blank=No)
"fire_alarm", -- Fire Alarm Work Type? (X=Yes, Blank=No)
"job_status_descrp", -- Status code description
"plumbing", -- Plumbing Work Type? (X=Yes, Blank=No)
"initial_cost", -- Estimated cost of job
"existing_zoning_sqft", -- Existing Zoning Sqft
"signoff_date", -- Sign-off Date
"gis_census_tract", -- Census Tract
"fuel_burning", -- Fuel Burning Work Type? (X=Yes, Blank=No)
"little_e", -- Hazardous
"mechanical", -- Mechanical Work Type? (X=Yes, Blank=No)
"professional_cert", -- Job is Professionally Certified by Licensed Professional instead of having it reviewed by Department of Building's Plan Examiners
"other", -- Other? (X=Yes, Blank=No)
"boiler", -- Boiler Work Type? (X=Yes, Blank=No)
"building_class", -- Building Class
"gis_longitude", -- Longitude
"gis_nta_name", -- NTA Name
"street_name", -- Street Name where Property is located
"bin__", -- Number assigned by City Planning to a specific building
"community___board", -- 3-digit identifier: Borough code = first position, last 2 = community board
"enlargement_sq_footage", -- Enlargement SQ Footage
"other_description", -- Other Description
"cluster", -- Cluster
"applicant_professional_title", -- Applicant's Professional Title
"fire_suppression", -- Fire Suppression Work Type? (X=Yes, Blank=No)
"fully_paid", -- Date when job is paid and entered
"approved", -- Date when job is approved
"applicant_license__", -- Number assigned to the skilled trade person/contractor or licensed professional
"assigned", -- Date when job is assigned to plan examiner
"proposed_zoning_sqft", -- Proposed Zoning Sqft
"loft_board", -- Loft Board
"street_frontage", -- Street Frontage
"applicant_s_first_name", -- First Name of Applicant
"owner_type", -- Owner Type
"non_profit", -- Non-Profit
"owner_s_first_name", -- First Name of property owner
"owner_s_business_name", -- Business Name of Property Owner
"city_", -- City
"zip", -- Zip
"dobrundate", -- Date when query is run and pushed to Open Data. Could be used to differentiate report dates.
"job_s1_no", -- JOB_S1_NO
"special_action_date", -- Special Action Date
"gis_bin", -- BIN
"proposed_occupancy", -- Proposed Occupancy
"job__", -- Number assigned by DOB to Job Filing
"gis_council_district", -- Council District
"owner_shouse_street_name", -- House Street Name of Property Owner
"zoning_dist3", -- Zoning Distr 3
"proposed_no_of_stories", -- Proposed No. of Stories
"applicant_s_last_name", -- Last Name of Applicant
"city_owned", -- City Owned
"special_district_2", -- Special District 2
"proposed_height", -- Proposed Height
"paid", -- Date when job is paid
"job_description", -- Job Description
"borough", -- 1= Manhattan, 2= Bronx, 3 = Brooklyn, 4 = Queens, 5 = Staten Island
"fuel_storage" -- Fuel Storage Work Type? (X=Yes, Blank=No)
FROM
"cityofnewyork-us/dob-job-application-filings-ic3t-wcy2:latest"."dob_job_application_filings"
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/dob-job-application-filings-ic3t-wcy2 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/dob-job-application-filings-ic3t-wcy2: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/dob-job-application-filings-ic3t-wcy2Checkout 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/dob-job-application-filings-ic3t-wcy2:latestThis will download all the objects for the latest tag of cityofnewyork-us/dob-job-application-filings-ic3t-wcy2 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/dob-job-application-filings-ic3t-wcy2: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/dob-job-application-filings-ic3t-wcy2: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/dob-job-application-filings-ic3t-wcy2 is just another Postgres schema.