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 medical_examiner_cases table in this repository, by referencing it like:
"internal-sandiegocounty-data-socrata/medical-examiner-cases-jkvb-n4p7:latest"."medical_examiner_cases"or in a full query, like:
SELECT
":id", -- Socrata column ID
"manner_type_standardized_", -- A standardized Manner Type of death to provide consistency due to categorizations that may have changed over time.
"age_in_years", -- The age of the decedent at time of death, represented in whole years; individuals under the age of 1 are listed at age 0 years.
"manner_sub_type_standardized_", -- A standardized Manner Sub Type of death to provide consistency due to categorizations that may have changed over time.
"contributing_conditions", -- This includes conditions that contributed to the cause of death.
"event_date", -- Date when the event causing death occurred.
"event_time", -- Time when the event causing death occurred.
"quarter", -- Calendar year quarter based on the date of death or the quarter in which the Medical Examiner took jurisdiction if outside of the current year.
"gender", -- Gender
"manner_sub_type", -- This is the sub-type or sub-method of death determined by the Medical Examiner.
"event_place", -- Place where the event causing death occurred.
"event_city", -- City where the event causing death occurred.
"manner", -- This is the manner of death category: Natural, Accident, Homicide, Suicide, or Undetermined.
"event_zip", -- Zip code where the event causing death occurred.
"death_date", -- Decedent's date of death.
"res_city", -- Decedent's city of residence.
"age_group_option_3", -- Includes a child age grouping from age 0-14 and then 10 increments beginning with 15-24.
"death_place", -- Place where death occurred.
"event_place_type", -- General categorization of Event Place.
"death_place_type", -- General categorization of Death Place.
"how_injury_occurred", -- A description as to how the injury leading to death occurred.
"manner_type", -- This is the type or method of death determined by the Medical Examiner.
"race", -- Race/Ethnicity as determined by the Medical Examiner.
"age_group_option_1", -- Includes age groupings in 10 year increments, beginning with 0-9
"opioid_related", -- Indicates if an Opioid is listed in the Cause of Death, Contributing Conditions, or How Injury Occurred column. Currently includes the following: opiate, opioid, U-47700, tramadol, oxymorphone, oxycodone, morphine, mitragynine, methadone, hydromorphone, hydrocodone, heroid, fentanyl, dihydrocodeine, codeine, buprenorphine, carfentanil
"ethnic_group", -- Ethnic Group categorization based upon the indicated race/ethnicity.
"death_city", -- City where death occurred.
"security_status", -- Indicates if record is sealed or access free.
"age_group_option_2", -- Includes child and young adult age groupings in 5 year increments through age 24, then in 10 year increments beginning with 25-34..
"death_zip", -- Zip code where death occurred.
"cod_string", -- A description of the cause of death.
"res_zip", -- Decedent's Zip code of residence.
"year", -- The year the Medical Examiner took jurisdiction of the case for investigation.
"coronado_bridge_related_suicide_cases_", -- Indicate if the event location was the Coronado Bay Bridge.
"row_number" -- A non-identifiable row number has been added to distinguish individual case records.
FROM
"internal-sandiegocounty-data-socrata/medical-examiner-cases-jkvb-n4p7:latest"."medical_examiner_cases"
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 internal-sandiegocounty-data-socrata/medical-examiner-cases-jkvb-n4p7 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 internal-sandiegocounty-data-socrata/medical-examiner-cases-jkvb-n4p7: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 internal-sandiegocounty-data-socrata/medical-examiner-cases-jkvb-n4p7Checkout 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 internal-sandiegocounty-data-socrata/medical-examiner-cases-jkvb-n4p7:latestThis will download all the objects for the latest tag of internal-sandiegocounty-data-socrata/medical-examiner-cases-jkvb-n4p7 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 internal-sandiegocounty-data-socrata/medical-examiner-cases-jkvb-n4p7: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 internal-sandiegocounty-data-socrata/medical-examiner-cases-jkvb-n4p7: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, internal-sandiegocounty-data-socrata/medical-examiner-cases-jkvb-n4p7 is just another Postgres schema.