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 child_victims_trend table in this repository, by referencing it like:
"healthdata-gov/child-victims-trend-qwij-f3kq:latest"."child_victims_trend"or in a full query, like:
SELECT
":id", -- Socrata column ID
"_2019_rate_per_1000_children", -- The frequency for each state of children determined to be victims of maltreatment per 1,000 children younger than 18 years in the population for federal fiscal year 2019. The state population estimates are from the U.S. Census Bureau. For the definitions of child victims and the population estimates used to calculate the rates, see the Glossary and State Characteristics appendixes of Child Maltreatment 2019.
"_2015", -- The number of child maltreatment victims (unique count) reported to NCANDS for federal fiscal year 2015. In NCANDS, a victim is defined as a child for whom the state determined at least one maltreatment was substantiated or indicated. This includes a child who died of child abuse and neglect. For more information and definitions of victims, federal fiscal year, and unique and duplicate counts, see the Glossary in the Child Maltreatment 2019 report.
"_2015_rate_per_1000_children", -- The frequency for each state of children determined to be victims of maltreatment per 1,000 children younger than 18 years in the population for federal fiscal year 2015. The state population estimates are from the U.S. Census Bureau. For the definitions of child victims and the population estimates used to calculate the rates, see the Glossary and State Characteristics appendixes of Child Maltreatment 2019.
"state", -- The primary unit from which child maltreatment data are collected. This includes all 50 states, the Commonwealth of Puerto Rico, and the District of Columbia. The state column also includes a national row which displays the total sum, percentage, or rate for only those states included in the analysis.
"_2016", -- The number of child maltreatment victims (unique count) reported to NCANDS for federal fiscal year 2016. In NCANDS, a victim is defined as a child for whom the state determined at least one maltreatment was substantiated or indicated. This includes a child who died of child abuse and neglect. For more information and definitions of victims, federal fiscal year, and unique and duplicate counts, see the Glossary in the Child Maltreatment 2019 report.
"_2017", -- The number of child maltreatment victims (unique count) reported to NCANDS for federal fiscal year 2017. In NCANDS, a victim is defined as a child for whom the state determined at least one maltreatment was substantiated or indicated. This includes a child who died of child abuse and neglect. For more information and definitions of victims, federal fiscal year, and unique and duplicate counts, see the Glossary in the Child Maltreatment 2019 report.
"_2018", -- The number of child maltreatment victims (unique count) reported to NCANDS for federal fiscal year 2018. In NCANDS, a victim is defined as a child for whom the state determined at least one maltreatment was substantiated or indicated. This includes a child who died of child abuse and neglect. For more information and definitions of victims, federal fiscal year, and unique and duplicate counts, see the Glossary in the Child Maltreatment 2019 report.
"_2019", -- The number of child maltreatment victims (unique count) reported to NCANDS for federal fiscal year 2019. In NCANDS, a victim is defined as a child for whom the state determined at least one maltreatment was substantiated or indicated. This includes a child who died of child abuse and neglect. For more information and definitions of victims, federal fiscal year, and unique and duplicate counts, see the Glossary in the Child Maltreatment 2019 report.
"percent_change_from_2015_2019", -- The percent change was calculated by subtracting 2015 data from 2019 data, dividing the result by 2015 data, and multiplying by 100. A state must have reported data for both 2015 and 2019 to have a percent change calculated.
"_2016_rate_per_1000_children", -- The frequency for each state of children determined to be victims of maltreatment per 1,000 children younger than 18 years in the population for federal fiscal year 2016. The state population estimates are from the U.S. Census Bureau. For the definitions of child victims and the population estimates used to calculate the rates, see the Glossary and State Characteristics appendixes of Child Maltreatment 2019.
"_2017_rate_per_1000_children", -- The frequency for each state of children determined to be victims of maltreatment per 1,000 children younger than 18 years in the population for federal fiscal year 2017. The state population estimates are from the U.S. Census Bureau. For the definitions of child victims and the population estimates used to calculate the rates, see the Glossary and State Characteristics appendixes of Child Maltreatment 2019.
"_2018_rate_per_1000_children" -- The frequency for each state of children determined to be victims of maltreatment per 1,000 children younger than 18 years in the population for federal fiscal year 2018. The state population estimates are from the U.S. Census Bureau. For the definitions of child victims and the population estimates used to calculate the rates, see the Glossary and State Characteristics appendixes of Child Maltreatment 2019.
FROM
"healthdata-gov/child-victims-trend-qwij-f3kq:latest"."child_victims_trend"
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 healthdata-gov/child-victims-trend-qwij-f3kq 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 healthdata-gov/child-victims-trend-qwij-f3kq: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 healthdata-gov/child-victims-trend-qwij-f3kqCheckout 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 healthdata-gov/child-victims-trend-qwij-f3kq:latestThis will download all the objects for the latest tag of healthdata-gov/child-victims-trend-qwij-f3kq 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 healthdata-gov/child-victims-trend-qwij-f3kq: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 healthdata-gov/child-victims-trend-qwij-f3kq: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, healthdata-gov/child-victims-trend-qwij-f3kq is just another Postgres schema.