Query the Data Delivery Network

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 fallecidos_2021_2022 table in this repository, by referencing it like:

"datos-gov-co/fallecidos-2021-2022-9wnh-ux8s:latest"."fallecidos_2021_2022"

or in a full query, like:

SELECT
    ":id", -- Socrata column ID
    "ocupaci_n_fallecido", -- Ocupación de la persona fallecida 
    "nombre_instituci_n", -- Nombre de la institución donde falleció 
    "recibi_asistencia_medica", -- Recibió asistencia medica 
    "pa_s_residencia", -- País de residencia de la persona fallecida
    "n_mero_certificado", -- Número del certificado 
    "estado_conyugal_fallecido", -- Estado conyugal de la persona fallecida 
    "tipo_defunci_n", -- Tipo de muerte 
    "inspecci_n_corregimiento", -- Inspección, corregimiento o caserío en que falleció
    "municipio", -- Municipio en que falleció
    "departamento", -- Departamento en que falleció
    "pertenencia_tnica", -- Pertenencia étnica de la persona fallecida 
    "edad_fallecido", -- Edad  de la persona fallecida
    "area_defunci_n", -- Área en que falleció
    "sitio_defunci_n", -- Lugar en que falleció
    "fecha_defunci_n", -- Fecha en que falleció 
    "hora_defunci_n", -- Hora en que falleció 
    "estados_patol_gicos", -- Estado patológico de la muerte 
    "causa_antecedentes_d", -- Causa de antecedentes d de la muerte 
    "departamento_residencia", -- Departamento de residencia de la persona fallecida
    "fecha_nacimiento_fallecido", -- Fecha de nacimiento de la persona fallecida 
    "causa_antecedentes_b", -- Causa de antecedentes b de la muerte 
    "causa_directa", -- Causa directa de la muerte 
    "nombres_fallecido", -- Nombres de la persona fallecida
    "tipo_documento_fallecido", -- Tipo de documento de la persona fallecida
    "n_mero_documento_fallecido", -- Numero de documento de la persona fallecida
    "expedido_por", -- Medico que expide 
    "nombre_administradora", -- Nombre de la administradora de la persona fallecida
    "tipo_administradora", -- Tipo de administradora de la persona fallecida
    "r_gimen_seguridad", -- Régimen de seguridad de la persona fallecida
    "municipio_residencia", -- Municipio de residencia de la persona fallecida 
    "direcci_n", -- Dirección de residencia de la persona fallecida
    "barrio", -- Barrio de residencia de la persona fallecida
    "localidad", -- Localidad de residencia de la persona fallecida
    "area_residencia", -- Área de residencia de la persona fallecida
    "apellidos_fallecido", -- Apellidos de la persona fallecida
    "causa_antecedentes_c", -- Causa de antecedentes c de la muerte 
    "probable_manera_muerte", -- Probable manera de muerte 
    "rural_disperso", -- Rural disperso de residencia de la persona fallecida
    "centro_poblado", -- Centro poblado de residencia de la persona fallecida
    "sexo_fallecido" -- Sexo de la persona fallecida
FROM
    "datos-gov-co/fallecidos-2021-2022-9wnh-ux8s:latest"."fallecidos_2021_2022"
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 datos-gov-co/fallecidos-2021-2022-9wnh-ux8s with SQL in under 60 seconds.

This repository is an "external" repository. That means it's hosted elsewhere, in this case at www.datos.gov.co. When you querydatos-gov-co/fallecidos-2021-2022-9wnh-ux8s:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).

We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.

Query Your Local Engine

bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"

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 (like this repository), 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, where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.

Mounting Data

This repository is an external repository. It's not hosted by Splitgraph. It is hosted by www.datos.gov.co, and Splitgraph indexes it. This means it is not an actual Splitgraph image, so you cannot use sgr clone to get the data. Instead, you can use the socrata adapter with the sgr mount command. Then, if you want, you can import the data and turn it into a Splitgraph image that others can clone.

First, install Splitgraph if you haven't already.

Mount the table with sgr mount

sgr mount socrata \
  "datos-gov-co/fallecidos-2021-2022-9wnh-ux8s" \
  --handler-options '{
    "domain": "www.datos.gov.co",
    "tables": {
        "fallecidos_2021_2022": "9wnh-ux8s"
    }
}'

That's it! Now you can query the data in the mounted table like any other Postgres table.

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, datos-gov-co/fallecidos-2021-2022-9wnh-ux8s is just another Postgres schema.