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 crimes_2001_to_present table in this repository, by referencing it like:
"cityofchicago/crimes-2001-to-present-ijzp-q8t2:latest"."crimes_2001_to_present"or in a full query, like:
SELECT
":id", -- Socrata column ID
"beat", -- Indicates the beat where the incident occurred. A beat is the smallest police geographic area – each beat has a dedicated police beat car. Three to five beats make up a police sector, and three sectors make up a police district. The Chicago Police Department has 22 police districts. See the beats at https://data.cityofchicago.org/d/aerh-rz74.
":@computed_region_6mkv_f3dw",
"location", -- The location where the incident occurred in a format that allows for creation of maps and other geographic operations on this data portal. This location is shifted from the actual location for partial redaction but falls on the same block.
":@computed_region_awaf_s7ux",
"location_city",
"longitude", -- The longitude of the location where the incident occurred. This location is shifted from the actual location for partial redaction but falls on the same block.
"latitude", -- The latitude of the location where the incident occurred. This location is shifted from the actual location for partial redaction but falls on the same block.
"year", -- Year the incident occurred.
"y_coordinate", -- The y coordinate of the location where the incident occurred in State Plane Illinois East NAD 1983 projection. This location is shifted from the actual location for partial redaction but falls on the same block.
"fbi_code", -- Indicates the crime classification as outlined in the FBI's National Incident-Based Reporting System (NIBRS).See the Chicago Police Department listing of these classifications at https://gis.chicagopolice.org/pages/crime_details.
"ward", -- The ward (City Council district) where the incident occurred. See the wards at https://data.cityofchicago.org/d/sp34-6z76.
"iucr", -- The Illinois Unifrom Crime Reporting code. This is directly linked to the Primary Type and Description. See the list of IUCR codes at https://data.cityofchicago.org/d/c7ck-438e.
"updated_on", -- Date and time the record was last updated.
"community_area", -- Indicates the community area where the incident occurred. Chicago has 77 community areas. See the community areas at https://data.cityofchicago.org/d/cauq-8yn6.
"district", -- Indicates the police district where the incident occurred. See the districts at https://data.cityofchicago.org/d/fthy-xz3r.
"location_description", -- Description of the location where the incident occurred.
"date", -- Date when the incident occurred. this is sometimes a best estimate.
"x_coordinate", -- The x coordinate of the location where the incident occurred in State Plane Illinois East NAD 1983 projection. This location is shifted from the actual location for partial redaction but falls on the same block.
"description", -- The secondary description of the IUCR code, a subcategory of the primary description.
"primary_type", -- The primary description of the IUCR code.
"domestic", -- Indicates whether the incident was domestic-related as defined by the Illinois Domestic Violence Act.
"location_zip",
"location_state",
"arrest", -- Indicates whether an arrest was made.
"location_address",
"block", -- The partially redacted address where the incident occurred, placing it on the same block as the actual address.
"case_number", -- The Chicago Police Department RD Number (Records Division Number), which is unique to the incident.
"id", -- Unique identifier for the record.
":@computed_region_8hcu_yrd4", -- This column was automatically created in order to record in what polygon from the dataset 'Wards 2023-' (8hcu-yrd4) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.
":@computed_region_d3ds_rm58",
":@computed_region_d9mm_jgwp",
":@computed_region_rpca_8um6",
":@computed_region_43wa_7qmu",
":@computed_region_bdys_3d7i",
":@computed_region_vrxf_vc4k"
FROM
"cityofchicago/crimes-2001-to-present-ijzp-q8t2:latest"."crimes_2001_to_present"
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 cityofchicago/crimes-2001-to-present-ijzp-q8t2 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 cityofchicago/crimes-2001-to-present-ijzp-q8t2: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 cityofchicago/crimes-2001-to-present-ijzp-q8t2Checkout 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 cityofchicago/crimes-2001-to-present-ijzp-q8t2:latestThis will download all the objects for the latest tag of cityofchicago/crimes-2001-to-present-ijzp-q8t2 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 cityofchicago/crimes-2001-to-present-ijzp-q8t2: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 cityofchicago/crimes-2001-to-present-ijzp-q8t2: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, cityofchicago/crimes-2001-to-present-ijzp-q8t2 is just another Postgres schema.