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
"s_age_low", -- Safety score for respondents age 18-34.
"t_income_medium", -- Trust score for respondents with an annual household income of $30,000 to $99,999.
"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)"
"s_race_asian_american", -- Safety score for Non-Hispanic Asian American respondents.
"s_race_hispanic", -- Safety score for Hispanic respondents.
"s_race_white", -- Safety score for Non-Hispanic White respondents.
"t_respect_income_high", -- Trust/Respect score for respondents with an annual household income of $100,000 or more.
"end_date", -- The end of the month the score object represents.
"t_race_hispanic", -- Trust score for Hispanic respondents.
"district",
"s_race_african_american", -- Safety score for Non-Hispanic Black/African American respondents.
"s_sex_female", -- Safety score for Female respondents.
"s_sex_male", -- Safety score for Male respondents.
"s_age_medium", -- Safety score for respondents age 35-54.
"s_education_medium", -- Safety 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_race_african_american", -- Trust score for Non-Hispanic Black/African American respondents.
"t_race_asian_american", -- Trust score for Non-Hispanic Asian American respondents.
"s_education_high", -- Safety score for respondents with an advanced degree.
"s_income_low", -- Safety score for respondents with an annual household income up to $29,999.
"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_race_other", -- Safety score for Non-Hispanic respondents of other races.
"s_age_high", -- Safety score for respondents age 55+.
"t_age_medium", -- Trust score for respondents age 35-54.
"s_education_low", -- Safety score for respondents with an education level up to high school graduate.
"t_respect_sex_male", -- Trust/Respect score for Male respondents.
"t_education_low", -- Trust score for respondents with an education level up to high school graduate.
"t_sex_male", -- Trust score for Male respondents.
"t_education_high", -- Trust score for respondents with an advanced degree.
"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_race_hispanic", -- Trust/Listen score for Hispanic respondents.
"t_listen_age_low", -- Trust/Listen score for respondents age 18-34.
"t_listen_age_high", -- Trust/Listen score for respondents age 55+.
"t_listen_sex_female", -- Trust/Listen score for Female respondents.
"t_listen_education_low", -- Trust/Listen score for respondents with an education level up to high school graduate.
"t_listen_education_medium", -- Trust/Listen score for respondents with some college or a college degree.
"t_listen_education_high", -- Trust/Listen score for respondents with an advanced degree.
"t_listen_income_low", -- Trust/Listen score for respondents with an annual household income up to $29,999.
"t_listen_income_medium", -- Trust/Listen score for respondents with an annual household income of $30,000 to $99,999.
"t_listen_income_high", -- Trust/Listen score for respondents with an annual household income of $100,000 or more.
"sector",
"t_listen_race_african_american", -- Trust/Listen score for Non-Hispanic Black/African American respondents.
"s_income_medium", -- Safety score for respondents with an annual household income of $30,000 to $99,999.
"area",
"start_date", -- The beginning of the month the score represents.
"t_respect_age_medium", -- Trust/Respect score for respondents age 35-54.
"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_sex_male", -- Trust/Listen score for Male respondents.
"t_listen_age_medium", -- Trust/Listen score for respondents age 35-54.
"t_listen_race_other", -- Trust/Listen score for Non-Hispanic respondents of other races.
"t_race_other", -- Trust score for Non-Hispanic respondents of other races.
"t_respect_race_hispanic", -- Trust/Respect score for Hispanic respondents.
"t_education_medium", -- Trust score for respondents with some college or a college degree.
"t_age_high", -- Trust score for respondents age 55+.
"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)".
"org_level", -- The type of organizational unit represented by the record.
"t_respect_race_african_american", -- Trust/Respect score for Non-Hispanic Black/African American respondents.
"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_respect_race_asian_american", -- Trust/Respect score for Non-Hispanic Asian American respondents.
"t_listen_race_white", -- Trust/Listen score for Non-Hispanic White respondents.
"t_age_low", -- Trust score for respondents age 18-34.
"t_respect_race_white", -- Trust/Respect score for Non-Hispanic White respondents.
"t_respect_race_other", -- Trust/Respect score for Non-Hispanic respondents of other races.
"t_income_high", -- Trust score for respondents with an annual household income of $100,000 or more.
"t_respect_age_low", -- Trust/Respect score for respondents age 18-34.
"t_respect_age_high", -- Trust/Respect score for respondents age 55+.
"t_respect_education_medium", -- Trust/Respect score for respondents with some college or a college degree.
"t_race_white", -- Trust score for Non-Hispanic White respondents.
"t_respect_education_high", -- Trust/Respect score for respondents with an advanced degree.
"t_respect_income_low", -- Trust/Respect score for respondents with an annual household income up to $29,999.
"t_respect_income_medium", -- Trust/Respect score for respondents with an annual household income of $30,000 to $99,999.
"city"
FROM
"cityofchicago/police-sentiment-scores-28me-84fj:latest"."police_sentiment_scores"
LIMIT 100;Connecting to the DDN is easy. All you need is an existing SQL client that can connect to Postgres. As long as you have a SQL client ready, you'll be able to query cityofchicago/police-sentiment-scores-28me-84fj with SQL in under 60 seconds.
This repository is an "external" repository. That means it's hosted elsewhere, in this case at data.cityofchicago.org. When you querycityofchicago/police-sentiment-scores-28me-84fj:latest on the DDN, we "mount" the repository using the socrata mount handler. The mount handler proxies your SQL query to the upstream data source, translating it from SQL to the relevant language (in this case SoQL).
We also cache query responses on the DDN, but we run the DDN on multiple nodes so a CACHE_HIT is only guaranteed for subsequent queries that land on the same node.
Query Your Local Engine
bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"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 (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 data.cityofchicago.org, 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 \
"cityofchicago/police-sentiment-scores-28me-84fj" \
--handler-options '{
"domain": "data.cityofchicago.org",
"tables": {
"police_sentiment_scores": "28me-84fj"
}
}'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, cityofchicago/police-sentiment-scores-28me-84fj is just another Postgres schema.