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 nypd_complaint_data_current_year_to_date table in this repository, by referencing it like:
"cityofnewyork-us/nypd-complaint-data-current-year-to-date-5uac-w243:latest"."nypd_complaint_data_current_year_to_date"or in a full query, like:
SELECT
":id", -- Socrata column ID
"susp_race", -- Suspect’s Race Description
"transit_district", -- Transit district in which the offense occurred.
"vic_age_group", -- Victim’s Age Group
"vic_race", -- Victim’s Race Description
"latitude", -- Midblock Latitude coordinate for Global Coordinate System, WGS 1984, decimal degrees (EPSG 4326)
"lat_lon_zip",
"addr_pct_cd", -- The precinct in which the incident occurred
"housing_psa", -- Development Level Code
"ky_cd", -- Three digit offense classification code
"loc_of_occur_desc", -- Specific location of occurrence in or around the premises; inside, opposite of, front of, rear of
"patrol_boro", -- The name of the patrol borough in which the incident occurred
"rpt_dt", -- Date event was reported to police
"susp_age_group", -- Suspect’s Age Group
"vic_sex", -- Victim’s Sex Description
"y_coord_cd", -- Y-coordinate for New York State Plane Coordinate System, Long Island Zone, NAD 83, units feet (FIPS 3104)
"longitude", -- Midblock Longitude coordinate for Global Coordinate System, WGS 1984, decimal degrees (EPSG 4326)
"lat_lon",
"geocoded_column",
"lat_lon_city",
"station_name", -- Transit station name
"prem_typ_desc", -- Specific description of premises; grocery store, residence, street, etc.
"pd_desc", -- Description of internal classification corresponding with PD code (more granular than Offense Description)
"pd_cd", -- Three digit internal classification code (more granular than Key Code)
"parks_nm", -- Name of NYC park, playground or greenspace of occurrence, if applicable (state parks are not included)
"ofns_desc", -- Description of offense corresponding with key code
"juris_desc", -- Description of the jurisdiction code
"jurisdiction_code", -- Jurisdiction responsible for incident. Either internal, like Police(0), Transit(1), and Housing(2); or external(3), like Correction, Port Authority, etc.
"hadevelopt", -- Name of NYCHA housing development of occurrence, if applicable
"cmplnt_to_tm", -- Ending time of occurrence for the reported event, if exact time of occurrence is unknown
"cmplnt_to_dt", -- Ending date of occurrence for the reported event, if exact time of occurrence is unknown
"cmplnt_fr_dt", -- Exact date of occurrence for the reported event (or starting date of occurrence, if CMPLNT_TO_DT exists)
"cmplnt_num", -- Randomly generated persistent ID for each complaint
"cmplnt_fr_tm", -- Exact time of occurrence for the reported event (or starting time of occurrence, if CMPLNT_TO_TM exists)
"x_coord_cd", -- X-coordinate for New York State Plane Coordinate System, Long Island Zone, NAD 83, units feet (FIPS 3104)
"lat_lon_state",
"crm_atpt_cptd_cd", -- Indicator of whether crime was successfully completed or attempted, but failed or was interrupted prematurely
"boro_nm", -- The name of the borough in which the incident occurred
"lat_lon_address",
"law_cat_cd", -- Level of offense: felony, misdemeanor, violation
":@computed_region_sbqj_enih",
":@computed_region_92fq_4b7q",
":@computed_region_yeji_bk3q",
":@computed_region_f5dn_yrer",
":@computed_region_efsh_h5xi",
"susp_sex" -- Suspect’s Sex Description
FROM
"cityofnewyork-us/nypd-complaint-data-current-year-to-date-5uac-w243:latest"."nypd_complaint_data_current_year_to_date"
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/nypd-complaint-data-current-year-to-date-5uac-w243 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/nypd-complaint-data-current-year-to-date-5uac-w243: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/nypd-complaint-data-current-year-to-date-5uac-w243Checkout 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/nypd-complaint-data-current-year-to-date-5uac-w243:latestThis will download all the objects for the latest tag of cityofnewyork-us/nypd-complaint-data-current-year-to-date-5uac-w243 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/nypd-complaint-data-current-year-to-date-5uac-w243: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/nypd-complaint-data-current-year-to-date-5uac-w243: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/nypd-complaint-data-current-year-to-date-5uac-w243 is just another Postgres schema.