MASSDOT API Authentication

I’ve worked with a lot of DOT type data and I have to say Massachusetts takes the cake for a seemingly unnecessarily complicated authentication process. The processes is documented on their /auth-token API.

This endpoint is available to all authorized users. The content of the response is the same for all user groups. The Bearer signature generation that uses this token is performed as follows.


Ok, here is the six, yes six step process to authenticate MASSDOT APIs:

Step 1: Concatenate the user’s secret key with the token provided herein, separated by a colon. (‘SecretKey:token’)

Step 2: Create a SHA256 hash of the concatenated string. (i.e. SHA256(secretKey:token))

Step 3: Convert the generated hash to a hexadecimal string representation

Step 4: Concatenate the user’s username with the hexadecimal hash, separated by a colon. (‘username:hexadecimalHash’)

Step 5: Create the base-64 string representation of the concatenation. (Base64(‘username:hexadecimalHash’))

Step 6: The result is the signature required for Bearer type authorization. (Authorization Bearer ‘generated signature’)

Btw, I provided this gist to MASSDOT in the event they want to link/share for other developers.

Working with Apollo CLI

I’ve been exploring the Apollo stack for developing with GraphQL and find the documentation a bit outdated so I decided to make some notes for myself and start collecting them here. The first thing I wanted to do is experiment with the apollo client codegen for TypeScript and understand how this tool works and leverage it for creating a TypeScript Apollo client. I started by using this Starwars sample Apollo server so I could focus on the client-side code gen which was quick and easy to stand up.

$ git clone
$ cd starwars-server
$ yarn && yarn start
yarn run v1.15.2
$ nodemon ./server.js --exec babel-node
[nodemon] 1.19.0
[nodemon] to restart at any time, enter `rs`
[nodemon] watching: *.*
[nodemon] starting `babel-node ./server.js`
🚀 Server ready at http://localhost:8080/graphql
🚀 Subscriptions ready at ws://localhost:8080/websocket

Next, I tested a simple GraphQL query to make sure the server is working by browsing here:


I installed the Apollo CLI and started experimenting with codegen. Unfortunately, as of this writing the CLI documentation is outdated and refers to apollo-codegen and the parameters and configuration appear to have changed. To play with newer apollo CLI and client-side codegen I created a new “project” folder and just wanted to get some code generated without any other project dependencies/files etc. So, I created a folder to get started:

$ mkdir starwars-client
$ cd starwars-client

Next, I ran the apollo CLI to download the server’s schema with –endpoint parameter pointing to the running instance of the starwars-server sample:

$ ➜  starwars-client apollo client:download-schema --endpoint=http://localhost:8080/graphql
⚠️  It looks like there are 0 files associated with this Apollo Project. This may be because you don't have any files yet, or your includes/excludes fields are configured incorrectly, and Apollo can't find your files. For help configuring Apollo projects, see this guide:
  ✔ Loading Apollo Project
  ✔ Saving schema to schema.json
$ ls

As you can see, this created a schema.json file containing details from my starwars-server. The next step is generating TypeScript code for a single GraphQL query using the downloaded schema. For good measure I’ll include a few of the issues I ran into along the way as I didn’t fine a lot on Google related to the various error messages.

➜  starwars-client apollo client:codegen
 ›   Error: Missing required flag:
 ›     --target TARGET  Type of code generator to use (swift | typescript | flow | scala)
 ›   See more help with --help

Ok, so I’m missing –target, that’s easy enough to add…

➜  starwars-client apollo client:codegen --target typescript
Error: No schema provider was created, because the project type was unable to be resolved from your config. Please add either a client or service config. For more information, please refer to
    at Object.schemaProviderFromConfig (~/.nvm/versions/node/v10.15.3/lib/node_modules/apollo/node_modules/apollo-language-server/lib/providers/schema/index.js:29:11)
    at new GraphQLProject (~/.nvm/versions/node/v10.15.3/lib/node_modules/apollo/node_modules/apollo-language-server/lib/project/base.js:31:40)
    at new GraphQLClientProject (~/.nvm/versions/node/v10.15.3/lib/node_modules/apollo/node_modules/apollo-language-server/lib/project/client.js:33:9)
    at Generate.createService (~/.nvm/versions/node/v10.15.3/lib/node_modules/apollo/lib/Command.js:114:28)
    at Generate.init (~/.nvm/versions/node/v10.15.3/lib/node_modules/apollo/lib/Command.js:37:14)
➜  starwars-client

Again, unfortunately the bitly short link provided by the tool points back to the outdated apollo-codegen documentation which is inaccurate. So I added –localSchemaFile pointing to my newly downloaded schema.json:

➜  starwars-client apollo client:codegen --localSchemaFile=schema.json --target=typescript
⚠️  It looks like there are 0 files associated with this Apollo Project. This may be because you don't have any files yet, or your includes/excludes fields are configured incorrectly, and Apollo can't find your files. For help configuring Apollo projects, see this guide:
  ✔ Loading Apollo Project
  ✖ Generating query files with 'typescript' target
    → No operations or fragments found to generate code for.
Error: No operations or fragments found to generate code for.
    at write (~/.nvm/versions/node/v10.15.3/lib/node_modules/apollo/lib/commands/client/codegen.js:61:39)
    at Task.task (~/.nvm/versions/node/v10.15.3/lib/node_modules/apollo/lib/commands/client/codegen.js:86:46)
➜  starwars-client

What this error is actually saying is that the tool is expecting to find either .graphql or .ts files that have GraphQL “operations” aka queries, or mutations defined within my project folder which I haven’t created yet. Turns out there are a few options, 1) create .ts files with gql constants or 2) create a .graphql file(s) that contain named queries. I started with a simple query.graphql file for testing like this:

query {
  heros(episode: NEWHOPE) {

I then ran the command again:

➜  starwars-client apollo client:codegen --localSchemaFile=schema.json --target=typescript

…and this yielded the same error as above because the CLI defaults to looking in ./src although you can change this using the –includes parameter. So I created the folder, moved the query.graphql file and re-ran the tool:

➜  starwars-client apollo client:codegen --localSchemaFile=schema.json --target=typescript
  ✔ Loading Apollo Project
  ✖ Generating query files with 'typescript' target
    → Apollo does not support anonymous operations
GraphQLError: Apollo does not support anonymous operations

Basically, this is telling me didn’t “name” the query so back to editing the query.graphql file and adding “heros”:

query heros {
  hero(episode: NEWHOPE) {

Ok, now let’s try that again:

➜  starwars-client apollo client:codegen --localSchemaFile=schema.json --target=typescript
  ✔ Loading Apollo Project
  ✔ Generating query files with 'typescript' target - wrote 2 files

Success! I now have a few new folders and files added to my “project”:

➜  starwars-client tree
| |____globalTypes.ts
| |____query.graphql
| |______generated__
| | |____heros.ts

Btw, here’s an example .ts file with a gql constant declared that would also work to generate code:

const heros = gql`
query heros {
  hero(episode: NEWHOPE) {

In the above example I use command-line options although the apollo CLI supports a config file which looks like the following located in apollo.config.js which points to a remote schema from my starwars-server instance:

module.exports = {
    client: {
        service: {
            url: "http://localhost:8080/graphql"

Using the config file you can change the command-line as follows for pulling the schema:

➜  starwars-client apollo client:download-schema --config=apollo.client.js
  ✔ Loading Apollo Project
  ✔ Saving schema to schema.json

Next, I’ll start making use of the generated code and save that for another post.

Accessing VMware vcenter REST API Authentication from curl

Here’s a simple example of calling the vSphere REST API using curl. These commands first authenticate to the API which creates a vmware-api-session-id cookie which is stored to cookie-jar.txt then makes a request to get a list of VMs:

curl -k -i -u administrator@vsphere.local:password -X POST -c cookie-jar.txt https://vcenter/rest/com/vmware/cis/session<br>
curl -k -i -b cookie-jar.txt https://vcenter/rest/vcenter/vm<br>

NOTE: Use with caution as your credentials will likely be caught in your command line history!

Here’s a related post on vSphere SDKs.

Check out the new VMware portal!

Highway 17 Santa Cruz Mountains Traffic Commuter Resources

Resources for information on Highway 17 Traffic over the Santa Cruz mountains to/from Los Gatos.

React Native bundle loading failing on a physical device

I’m building a React Native application and recently updated to v0.31.0 and at first things were working well debugging on the device benefiting from a feature of the script. The script copies your Dev machine’s IP address to a text file called ip.txt which is used for establishing the connection to your machine from your device since localhost points to the wrong place. Here’s the line of code in ./node_modules/react-native/packager/

echo "$" &gt; "$DEST/ip.txt"

Continue reading React Native bundle loading failing on a physical device

Using Google Sheets, Pivot Tables and Charts as a Startup Dashboard – Part II

In my previous post I wrote about the process that lead me to build a dashboard but first I want to talk a bit about the structure of the data in the Google Sheet where the whole process started. I first started by looking to quickly create a few charts to visualize some of our KPI‘s. To source the data I created a text file containing the SQL statements and used psql to fetch Postgres data which I dumped it to data into .CSV files for import into separate “data” only tabs in Google Sheets.

psql -h pgserver -d mydb -U myuser -w -t -A -F $ '\t' -f ~/Campains.sql > campaign.csv

The first tab was the “primary” dataset which contained a wide (A to AX) set of columns with a blend of content from the various linked “data” tabs and is where I derived all of the pivot tables with a primary key in the first column and with this initial set of at I was able to start building charts to help visualize the data.

Of course, once you’ve answered one question it leads to follow-on questions which require more data leading to more questions. Before long I was querying a dozen tables from Postgres and MSSQL and importing the data into these “data” tabs. For data tabs with a 1-1 relationship based on primary key I would aggregate the data onto the main sheet with a formula like “Imported Data’!B4” or in cases where not all keys were present via a lookup like =IFERROR(VLOOKUP($A:$A,”Data Sheet”!$A:$E,3,FALSE),0) setting the result accordingly when the primary key wasn’t found.

Ultimately, flattening the data made it easy to construct pivot tables for aggregate totals, averages, counts, and median values etc. from which I could build a variety of charts a sampling of which I’ve included below.Here’s a small sample of the kinds of charts built from pivot tables. Yes, I’ve clipped/changed some of the legends knowingly obscuring the underlying meaning of the chart.

Here’s a small sample of the kinds of charts built from pivot tables. Yes, I’ve clipped/changed some of the legends knowingly obscuring the underlying meaning of the chart.

Monthly Totals

I built a variety of pivot tables for the Wanderful Marketing team (sans charts) for easy analysis of Cash Dash campaigns from a variety of angles such as by a given retailer by offer type, amount, reward, launch day of the week and a variety of campaign performance metrics that I’d calculated within the sheet. Ultimately, the usefulness of this data caught on and a number of teams were not only reviewing the data but asking for additional analysis and updates.

While I was able to automate some portions of updating this sheet, its associated tabs etc. Google Sheet’s charts and pivot tables don’t automatically expand as the size of your data grows which made it a laborious task to “re-scope” them as more data was added not to mention I knew the 2M cell limit was looming in the distance.

In a follow-on post I’ll talk about how I began the shift to automating this using R and a Shiny Dashboard running on an OSX Mac mini.

From Google Sheets to an Automated Shiny Dashboard (Part I)

Over the past several months I’ve been building what’s become a large Google Sheet (which is now at the 2M cell limit) tracking Find&Save Cash Dash offers. Like a lot of data projects this one started out small but over many months grew to a point where it no longer makes sense to cut/paste SQL data into a Google Sheet for pivot tables and charts. I wanted a solution that could pull data from REST APIs, MSSQL and Postgres and present data in an internal dashboard. Unfortunately, Google Sheet’s JDBC support doesn’t include Postgres otherwise Google Apps Script might have been a viable choice for automating data collection notwithstanding size limitations. Finally, another issue with Sheets was that I couldn’t seem to get pivot tables to resize automatically as more data was added. So, 3 strikes and Google Sheets was out.

Continue reading From Google Sheets to an Automated Shiny Dashboard (Part I)

Plotting Weekly Mobile Retention from the Localytics API using R and ggplot2

Part of building mobile web apps is understanding the myriad of mobile analytics and in part visualizing the data to shed light on trends that my otherwise be difficult to see in tabular data or even a colorful cohort table. I’ve been building a dashboard using R, RStudio, Shiny, and Shiny Dashboards aggregating data from MSSQL, Postgres, Google Analytics, and Localytics.

Within the Product section of the dashboard I’ve included a retention chart and found some great articles at R-Blogger like this one. The retention data comes from the Localytics API which I discussed previously though getting the data into the proper format took a few steps. Let’s start with the data, here’s an example of the REST response from Localytics looks like for a weekly retention cohort:

"results": [
"birth_week": "2014-09-08",
"users": 1,
"week": "2014-12-29"
"birth_week": "2014-09-29",
"users": 1640,
"week": "2014-12-29"
"birth_week": "2014-10-06",
"users": 2988,
"week": "2014-12-29"
"birth_week": "2014-10-13",
"users": 4747,
"week": "2014-12-29"
"birth_week": "2014-10-20",
"users": 2443,
"week": "2014-12-29"

Below is the main function to fetch the Localytics sample data and convert it into a data frame that’s suitable for plotting. Now, admittedly I’m not an R expert so there may well be better ways to slice this JSON response but this is a fairly straight forward approach. Essentially, this fetches the data, converts it from JSON to an R object, extracts the weeks, preallocates a matrix and then iterates over the data filling the matrix to build a data frame.

retentionDF <- function() {
  # Example data from:
  localyticsExampleJSON <- getURL('')
  cohort <- fromJSON(localyticsExampleJSON)
  weeks <- unique(cohort$results$week)
  numweeks <- length(weeks)
  # Take the JSON response and convert it to a retention matrix (all numeric for easy conversion to a dataframe) like so:
  # Weekly.Cohort Users Week.1
  # 1    2014-12-29  7187   4558
  # 2    2015-01-05  5066     NA
  i <- 1
  # Create a matrix big enough to hold all of the data
  m <- matrix(nrow=numweeks, ncol=numweeks + 1)
  for (week in weeks) {
    # Get data for all weeks of this cohort
    d <- cohort$results[cohort$results$birth_week==week,][,2]
    lencohort <- length(d)
    for (n in 1:lencohort) {
      # Skip the first column using "+ 1" below which will be Weekly.Cohort (date)
      m[i,n + 1] <- d[n]
    i <- i + 1
  # Convert matrix to a dataframe
  df <-
  # Set values of the first column to the cohort dates
  df$V1 <- weeks
  # Set the column names accordingly
  colnames(df) <- c("Weekly.Cohort", "Users", paste0("Week.", rep(1:(numweeks-1))))

To make things easy I put together a gist and if you’re using R you can runGist it yourself. It requires several other packages so be sure to check the sources in case you’re missing any.
Fair warning the Localytics API demo has very limited data so the chart, let’s just say simplistic however given many weeks worth of data it will fill out nicely (see example below).

> library(shiny)
> runGist("180efcc1ecda6a02b1351418e95d0a29")
Localytics Retention Plot Example
Retention Chart