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 police_sentiment_scores table in this repository, by referencing it like:
"cityofchicago/police-sentiment-scores-28me-84fj:latest"."police_sentiment_scores"or in a full query, like:
SELECT
":id", -- Socrata column ID
"t_age_high", -- Trust score for respondents age 55+.
"t_age_low", -- Trust score for respondents age 18-34.
"t_race_white", -- Trust score for Non-Hispanic White respondents.
"t_listen_education_high", -- Trust/Listen score for respondents with an advanced degree.
"trust", -- Overall score for the questions: "1. How much do you agree with this statement? The police in my neighborhood listen to and take into account the concerns of local residents. (Level of agreement from 0-10) and 2. How much do you agree with this statement? The police in my neighborhood treat local residents with respect. (Level of agreement from 0-10)
"s_income_low", -- Safety score for respondents with an annual household income up to $29,999.
"t_listen_income_low", -- Trust/Listen score for respondents with an annual household income up to $29,999.
"s_education_high", -- Safety score for respondents with an advanced degree.
"s_sex_male", -- Safety score for Male respondents.
"s_sex_female", -- Safety score for Female respondents.
"s_race_african_american", -- Safety score for Non-Hispanic Black/African American respondents.
"district",
"end_date", -- The end of the month the score object represents.
"t_respect_income_high", -- Trust/Respect score for respondents with an annual household income of $100,000 or more.
"t_listen_income_medium", -- Trust/Listen score for respondents with an annual household income of $30,000 to $99,999.
"t_respect_income_medium", -- Trust/Respect score for respondents with an annual household income of $30,000 to $99,999.
"t_respect_income_low", -- Trust/Respect score for respondents with an annual household income up to $29,999.
"t_respect_education_high", -- Trust/Respect score for respondents with an advanced degree.
"t_listen_income_high", -- Trust/Listen score for respondents with an annual household income of $100,000 or more.
"s_education_medium", -- Safety score for respondents with some college or a college degree.
"s_race_other", -- Safety score for Non-Hispanic respondents of other races.
"s_age_medium", -- Safety score for respondents age 35-54.
"s_age_low", -- Safety score for respondents age 18-34.
"s_age_high", -- Safety score for respondents age 55+.
"s_education_low", -- Safety score for respondents with an education level up to high school graduate.
"s_race_white", -- Safety score for Non-Hispanic White respondents.
"s_income_medium", -- Safety score for respondents with an annual household income of $30,000 to $99,999.
"sector",
"t_sex_female", -- Trust score for Female respondents.
"t_income_low", -- Trust score for respondents with an annual household income up to $29,999.
"t_income_high", -- Trust score for respondents with an annual household income of $100,000 or more.
"s_race_hispanic", -- Safety score for Hispanic respondents.
"t_listen_race_african_american", -- Trust/Listen score for Non-Hispanic Black/African American respondents.
"t_listen_race_white", -- Trust/Listen score for Non-Hispanic White respondents.
"t_respect_race_asian_american", -- Trust/Respect score for Non-Hispanic Asian American respondents.
"t_respect_race_hispanic", -- Trust/Respect score for Hispanic respondents.
"t_respect_sex_male", -- Trust/Respect score for Male respondents.
"t_respect_education_medium", -- Trust/Respect score for respondents with some college or a college degree.
"s_income_high", -- Safety score for respondents with an annual household income of $100,000 or more.
"t_respect_age_high", -- Trust/Respect score for respondents age 55+.
"t_respect_age_low", -- Trust/Respect score for respondents age 18-34.
"t_respect_race_other", -- Trust/Respect score for Non-Hispanic respondents of other races.
"t_respect", -- Overall score for the Trust/Respect question: "2. How much do you agree with this statement? The police in my neighborhood treat local residents with respect. (Level of agreement from 0-10)".
"t_respect_race_african_american", -- Trust/Respect score for Non-Hispanic Black/African American respondents.
"t_race_african_american", -- Trust score for Non-Hispanic Black/African American respondents.
"s_race_asian_american", -- Safety score for Non-Hispanic Asian American respondents.
"safety", -- Overall score for the question: "When it comes to the threat of crime, how safe do you feel in your neighborhood? (Level of safety from 0-10)"
"t_income_medium", -- Trust score for respondents with an annual household income of $30,000 to $99,999.
"city",
"t_respect_race_white", -- Trust/Respect score for Non-Hispanic White respondents.
"t_race_other", -- Trust score for Non-Hispanic respondents of other races.
"t_respect_age_medium", -- Trust/Respect score for respondents age 35-54.
"t_race_asian_american", -- Trust score for Non-Hispanic Asian American respondents.
"org_level", -- The type of organizational unit represented by the record.
"t_race_hispanic", -- Trust score for Hispanic respondents.
"area",
"t_age_medium", -- Trust score for respondents age 35-54.
"t_sex_male", -- Trust score for Male respondents.
"t_education_low", -- Trust score for respondents with an education level up to high school graduate.
"start_date", -- The beginning of the month the score represents.
"t_education_high", -- Trust score for respondents with an advanced degree.
"t_respect_education_low", -- Trust/Respect score for respondents with an education level up to high school graduate.
"t_respect_sex_female", -- Trust/Respect score for Female respondents.
"t_listen", -- Overall score for the Trust question: "1. How much do you agree with this statement? The police in my neighborhood listen to and take into account the concerns of local residents. (Level of agreement from 0-10)"
"t_listen_race_asian_american", -- Trust/Listen score for Non-Hispanic Asian American respondents.
"t_listen_sex_male", -- Trust/Listen score for Male respondents.
"t_listen_race_hispanic", -- Trust/Listen score for Hispanic respondents.
"t_listen_age_low", -- Trust/Listen score for respondents age 18-34.
"t_listen_age_medium", -- Trust/Listen score for respondents age 35-54.
"t_listen_age_high", -- Trust/Listen score for respondents age 55+.
"t_listen_sex_female", -- Trust/Listen score for Female respondents.
"t_listen_race_other", -- Trust/Listen score for Non-Hispanic respondents of other races.
"t_listen_education_low", -- Trust/Listen score for respondents with an education level up to high school graduate.
"t_education_medium", -- Trust score for respondents with some college or a college degree.
"t_listen_education_medium" -- Trust/Listen score for respondents with some college or a college degree.
FROM
"cityofchicago/police-sentiment-scores-28me-84fj:latest"."police_sentiment_scores"
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/police-sentiment-scores-28me-84fj 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/police-sentiment-scores-28me-84fj: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/police-sentiment-scores-28me-84fjCheckout 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/police-sentiment-scores-28me-84fj:latestThis will download all the objects for the latest tag of cityofchicago/police-sentiment-scores-28me-84fj 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/police-sentiment-scores-28me-84fj: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/police-sentiment-scores-28me-84fj: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/police-sentiment-scores-28me-84fj is just another Postgres schema.