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 routine_marine_sediment_chemistry table in this repository, by referencing it like:
"kingcounty-gov/routine-marine-sediment-chemistry-s9uc-py3h:latest"."routine_marine_sediment_chemistry"or in a full query, like:
SELECT
":id", -- Socrata column ID
"sed_depth_range", -- Depth that sediment was collected from (e.g., top 10 cm)
"aet_csl", -- Apparent Effects Threshold - dry weight equivalent Cleanup Screening Level Equivalent
"dw_value", -- Dry weight normalized concentration of analyte
"dw_toc", -- Dry weight normalized total organic carbon (TOC) of the sample
"toc_qual", -- Qualifier applied to total organic carbon value
"oc_units", -- Units of organic carbon normalized measurements
"nondetect_flag", -- Was this sample analyte a non-detect (i.e., below the method detection limit or lower limit of quantification)
"sms_units", -- Sediment Management Standard units
"data_quality", -- Summary of quality of the data
"site_type", -- Indicates if sample was subtidal or intertidal
"latitude", -- Latitude of sample collection location
"time", -- Time the sample was collected in 24 hr format
"sample_depth_m", -- Depth that sample was collected from
"parmname", -- What was analyzed
"dw_units", -- Units of dry weight normalized measurements
"dw_rdl", -- Dry weight reporting detection limit (see DataReadMeFile_Sed for more details)
"to_oc_norm", -- Whether the sample value should be normalized by organic carbon to compare to sediment criteria
"method", -- Method used for analysis
"source", -- Who the data were analyzed by
"locator", -- King County alpha-numeric site name
"site_name", -- Descriptive site name
"longitude", -- Longitude of sample collection location
"date", -- Date the sample was collected
"total_solids", -- Percentage of total solids from a sample
"group", -- Type of parameter or parameter group
"qualifier", -- Qualifier applied by laboratory analysts (see DataReadMeFile_Sed for more details)
"dw_mdl", -- Dry weight method detection limit (see DataReadMeFile_Sed for more details)
"criteria_for_comparison", -- Indicates which criteria are appropriate for comparison based on the total organic carbon content (TOC) of the sample: if TOC ≥ 0.5, ≤3.5% - compare to Sediment Management Standards (SMS), if TOC < 0.5 or > 3.5% compare to the Apparent Effects Thresholds (AETs, dry-weight equivalent SMS)
"oc_norm", -- Total organic carbon normalized concentration of analyte
"sms_csl", -- Sediment Management Standard Cleanup Screening Level for the parameter if applicable
"aet_sqs", -- Apparent Effects Threshold - dry weight equivalent Sediment Quality Standard Equivalent
"aet_units", -- Apparent Effects Threshold units
"date_analyzed", -- Date the sample was analyzed for a particular parameter
"labsamplenum", -- Unique identifier applied by the laboratory for composite samples collected from the same locator.
"textvalue", -- Text entered by laboratory analysts, where non-measured parameter details are noted
"sms_sqs" -- Whether the sample value should be normalized by organic carbon to compare to sediment criteria
FROM
"kingcounty-gov/routine-marine-sediment-chemistry-s9uc-py3h:latest"."routine_marine_sediment_chemistry"
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 kingcounty-gov/routine-marine-sediment-chemistry-s9uc-py3h 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 kingcounty-gov/routine-marine-sediment-chemistry-s9uc-py3h: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 kingcounty-gov/routine-marine-sediment-chemistry-s9uc-py3hCheckout 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 kingcounty-gov/routine-marine-sediment-chemistry-s9uc-py3h:latestThis will download all the objects for the latest tag of kingcounty-gov/routine-marine-sediment-chemistry-s9uc-py3h 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 kingcounty-gov/routine-marine-sediment-chemistry-s9uc-py3h: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 kingcounty-gov/routine-marine-sediment-chemistry-s9uc-py3h: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, kingcounty-gov/routine-marine-sediment-chemistry-s9uc-py3h is just another Postgres schema.