An award-winning AWS Serverless Appliction that produces filtered Socrata datasets. NOT affiliated with or endorsed by Socrata.


Hundreds of local, state and federal government organizations use Socrata to host open data portals to share data sets with the public. The public can interact with these datasets in various ways, but these ways may not be intuitive for the general public. And people who understand how to manipulate these datasets don't want to repeat the same manual tasks every day, week or month.

nephridium addresses the use case where a dynamically filtered Socrata dataset is desired over time. You specify the Socrata dataset, tell nephridium what the date attribute is and you get an HTML table of the data! nephridium offers additional parameters you can use to choose which attributes you want to display and to further filter your dataset.

A use case

A repetative task

A Saint Paul District Council Executive Director wants to know all of the resident service requests that created in her district the past week. And she wants this information every week.

Saint Paul provides the data, but only in a way where the Executive Director has to enter filter information every single time she visits the site.

A one-click solution...

Since nephridium uses a look-back date filter, one URL will work in perpetuity. For example, https://your_aws_url/?district_council=8&time_column=request_date&url= will produce the previous 7 days' results for service requests in District 8.

...that can become a no-click solution

Once an URL is created it can be used with IFTTT, Zapier, cron or any other automation tool to automatically send an email with the requested data.

The name

Nephridium is like a kidney for an invertabrate. It filters things, just like this code.

Build an URL for nephridium

You will need

Whew! Now that you have the ingredients it's time to craft the URL you'll be using.

For an example we'll use

  • TIME_COLUMN: deadline_date

to build our minimal URL (must include a time_column and a url)

Make sure DATASET_URL is last!

If we want to further filter the dataset we can do that, too.

Excluding columns

In our example, let's say we don't want to see the county column. We'll add a to_remove parameter for that.

And if we want to remove more than one column, we just separate them with a comma (no spaces!). Let's also remove the column that shows whether there's a GPA requirement.,gpa_req_y_n&url=

Filtering data

Let's retrieve only records where you don't have to have graduated to be eligible. We do this by specifying the column name (grad_y_n) and a value in the URL.


See the Socrata API documentation for more options, especially

Host your own instance of nephridium


Setup process

Installing dependencies

In this example we use npm but you can use yarn if you prefer to manage NodeJS dependencies:

cd nephridium
npm install
cd ../

Local development

You can modify nephridium to return JSON instead of HTML, internallly filter your dataset(s) to minimize the number of parameters in your users' URLs, format the output (e.g., custom CSS), or restrict who can access the API.

Invoking function locally through local API Gateway

sam local start-api

If the previous command ran successfully you should now be able to hit the following local endpoint to invoke your function. Note that CloudFront (but not your local api) returns a 403 when it receives a GET with a body, so you must use query parameters instead.

SAM CLI is used to emulate both Lambda and API Gateway locally and uses our template.yaml.

Packaging and deployment

AWS Lambda NodeJS runtime requires a flat folder with all dependencies including the application. SAM will use CodeUri property to know where to look up for both application and dependencies:

        Type: AWS::Serverless::Function
            CodeUri: nephridium/

Firstly, we need a S3 bucket where we can upload our Lambda functions packaged as ZIP before we deploy anything - If you don't have a S3 bucket to store code artifacts then this is a good time to create one:

aws s3 mb s3://BUCKET_NAME

Next, run the following command to package our Lambda function to S3:

sam package \
    --template-file template.yaml \
    --output-template-file packaged.yaml \
    --s3-bucket BUCKET_NAME

Next, the following command will create a Cloudformation Stack and deploy your SAM resources. I've only been able to do this using an IAM user with full administrator rights.

sam deploy \
    --template-file packaged.yaml \
    --stack-name BUCKET_NAME \
    --capabilities CAPABILITY_IAM

Return from package call recommends the following, so you could try it as well:

aws cloudformation deploy --template-file /private/tmp/sam/nephridium/packaged.yaml --stack-name BUCKET_NAME

See Serverless Application Model (SAM) HOWTO Guide for more details in how to get started.

After deployment is complete you can run the following command to retrieve the API Gateway Endpoint URL:

aws cloudformation describe-stacks \
    --stack-name BUCKET_NAME \
    --query 'Stacks[].Outputs'

Unit testing

We use mocha for testing our code and it is already added in package.json under scripts, so that we can simply run the following command to run our tests:

cd nephridium
npm run test


AWS CLI commands

AWS CLI commands to package, deploy and describe outputs defined within the cloudformation stack:

sam package \
    --template-file template.yaml \
    --output-template-file packaged.yaml \
    --s3-bucket BUCKET_NAME

sam deploy \
    --template-file packaged.yaml \
    --stack-name BUCKET_NAME \
    --capabilities CAPABILITY_IAM

aws cloudformation describe-stacks \
    --stack-name BUCKET_NAME --query 'Stacks[].Outputs'

NOTE: Alternatively this could be part of package.json scripts section.

Bringing to the next level

API testing

Providing test examples for local and deployed endpoints

Bare minimum -- time_column and url

curl -vvv ''

curl -vvv ''

Custom parameters in query

curl -vvv ''

curl -vvv ''

Custom parameters in query with filtered attributes

curl -vvv -X GET ',map_location&url='

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