Tag: api

Setup Postgres, and GraphQL API with Hasura on Azure

Key Technologies: Hasura, Postgres, Terraform, Docker, and Azure.

I created a data model to store railroad systems, services, scheduled, time points, and related information, detailing the schema “Beyond CRUD n’ Cruft Data-Modeling” with a few tweaks. The original I’d created for Apache Cassandra, and have since switched to Postgres giving the option of primary and foreign keys, relations, and the related connections for the model.

In this post I’ll use that schema to build out an infrastructure as code solution with Terraform, utilizing Postgres and Hasura (OSS).

Prerequisites

Docker Compose Development Environment

For the Docker Compose file I just placed them in the root of the repository. Add a docker-compose.yaml file and then added services. The first service I setup was the Postgres/PostgreSQL database. This is using the standard Postgres image on Docker Hub. I opted for version 12, I do want it to always restart if it gets shutdown or crashes, and then the last of the obvious settings is the port which maps from 5432 to 5432.

For the volume, since I might want to backup or tinker with the volume, I put the db_data location set to my own Codez directory. All my databases I tend to setup like this in case I need to debug things locally.

The POSTGRES_PASSWORD is an environment variable, thus the syntax ${PPASSWORD}. This way no passwords go into the repo. Then I can load the environment variable via a standard export POSTGRES_PASSWORD="theSecretPasswordHere!" line in my system startup script or via other means.

services:
  postgres:
    image: postgres:12
    restart: always
    volumes:
      - db_data:/Users/adron/Codez/databases
    environment:
      POSTGRES_PASSWORD: ${PPASSWORD}
    ports:
      - 5432:5432

For the db_data volume, toward the bottom I add the key value setting to reference it.

volumes:
  db_data:

Next I added the GraphQL solution with Hasura. The image for the v1.1.0 probably needs to be updated (I believe we’re on version 1.3.x now) so I’ll do that soon, but got the example working with v1.1.0. Next I’ve got the ports mapped to open 8080 to 8080. Next, this service will depend on the postgres service already detailed. Restart, also set on always just as the postgres service. Finally two evnironment variables for the container:

  • HASURA_GRAPHQL_DATABASE_URL – this variable is the base postgres URL connection string.
  • HASURA_GRAPHQL_ENABLE_CONSOLE – this is the variable that will set the console user interface to initiate. We’ll definitely want to have this for the development environment. However in production I’d likely want this turned off.
  graphql-engine:
    image: hasura/graphql-engine:v1.1.0
    ports:
      - "8080:8080"
    depends_on:
      - "postgres"
    restart: always
    environment:
      HASURA_GRAPHQL_DATABASE_URL: postgres://postgres:logistics@postgres:5432/postgres
      HASURA_GRAPHQL_ENABLE_CONSOLE: "true"

At this point the commands to start this are relatively minimal, but in spite of that I like to create a start and stop shell script. My start script and stop script simply look like this:

Starting the services.

docker-compose up -d

For the first execution of the services you may want to skip the -d and instead watch the startup just to become familiar with the events and connections as they start.

Stopping the services.

docker-compose down

🚀 That’s it for the basic development environment, we’re launched and ready for development. With the services started, navigate to https://localhost:8080/console to start working with the user interface, which I’ll have a more details on the “Beyond CRUD n’ Cruft Data-Modeling” swap to Hasura and Postgres in an upcoming blog post.

For full syntax of the docker-compose.yaml check out this gist: https://gist.github.com/Adron/0b2ea637b5e00681f4d62404805c3a00

Terraform Production Environment

For the production deployment of this stack I want to deploy to Azure, use Terraform for infrastructure as code, and the Azure database service for Postgres while running Hasura for my API GraphQL tier.

For the Terraform files I created a folder and added a main.tf file. I always create a folder to work in, generally, to keep the state files and initial prototyping of the infrastructre in a singular place. Eventually I’ll setup a location to store the state and fully automate the process through a continues integration (CI) and continuous delivery (CD) process. For now though, just a singular folder to keep it all in.

For this I know I’ll need a few variables and add those to the file. These are variables that I’ll use to provide values to multiple resources in the Terraform templating.

variable "database" {
  type = string
}

variable "server" {
  type = string
}

variable "username" {
  type = string
}

variable "password" {
  type = string
}

One other variable I’ll want so that it is a little easier to verify what my Hasura connection information is, will look like this.

output "hasura_url" {
  value = "postgres://${var.username}%40${azurerm_postgresql_server.logisticsserver.name}:${var.password}@${azurerm_postgresql_server.logisticsserver.fqdn}:5432/${var.database}"
}

Let’s take this one apart a bit. There is a lot of concatenated and interpolated variables being wedged together here. This is basically the Postgres connection string that Hasura will need to make a connection. It includes the username and password, and all of the pertinent parsed and string escaped values. Note specifically the %40 between the ${var.username} and ${azurerm_postgresql_server.logisticsserver.name} variables while elsewhere certain characters are not escaped, such as the @ sign. When constructing this connection string, it is very important to be prescient of all these specific values being connected together. But, I did the work for you so it’s a pretty easy copy and paste now!

Next I’ll need the Azure provider information.

provider "azurerm" {
  version = "=2.20.0"
  features {}
}

Note that there is a features array that is just empty, it is now required for the provider to designate this even if the array is empty.

Next up is the resource group that everything will be deployed to.

resource "azurerm_resource_group" "adronsrg" {
  name     = "adrons-rg"
  location = "westus2"
}

Now the Postgres Server itself. Note the location and resource_group_name simply map back to the resource group. Another thing I found a little confusing, as I wasn’t sure if it was a Terraform name or resource name tag or the server name itself, is the “name” key value pair in this resource. It is however the server name, which I’ve assigned var.server. The next value assigned “B_Gen5_2” is the Azure designator, which is a bit cryptic. More on that in a future post.

After that information the storage is set to, I believe if I RTFM’ed correctly to 5 gigs of storage. For what I’m doing this will be fine. The backup is setup for 7 days of retention. This means I’ll be able to fall back to a backup from any of the last seven days, but after 7 days the backups are rolled and the last day is deleted to make space for the newest backup. The geo_redundant_backup_enabled setting is set to false, because with Postgres’ excellent reliability and my desire to not pay for that extra reliability insurance, I don’t need geographic redundancy. Last I set auto_grow_enabled to true, albeit I do need to determine the exact flow of logic this takes for this particular implementation and deployment of Postgres.

The last chunk of details for this resource are simply the username and password, which are derived from variables, which are derived from environment variables to keep the actual username and passwords out of the repository. The last two bits set the ssl to enabled and the version of Postgres to v9.5.

resource "azurerm_postgresql_server" "logisticsserver" {
  name = var.server
  location = azurerm_resource_group.adronsrg.location
  resource_group_name = azurerm_resource_group.adronsrg.name
  sku_name = "B_Gen5_2"

  storage_mb                   = 5120
  backup_retention_days        = 7
  geo_redundant_backup_enabled = false
  auto_grow_enabled            = true

  administrator_login          = var.username
  administrator_login_password = var.password
  version                      = "9.5"
  ssl_enforcement_enabled      = true
}

Since the database server is all setup, now I can confidently add an actual database to that database. Here the resource_group_name pulls from the resource group resource and the server_name pulls from the server resource. The name, being the database name itself, I derive from a variable too. Then the character set is UTF8 and collation is set to US English, which are generally standard settings on Postgres being installed for use within the US.

resource "azurerm_postgresql_database" "logisticsdb" {
  name                = var.database
  resource_group_name = azurerm_resource_group.adronsrg.name
  server_name         = azurerm_postgresql_server.logisticsserver.name
  charset             = "UTF8"
  collation           = "English_United States.1252"
}

The next thing I discovered, after some trial and error and a good bit of searching, is the Postgres specific firewall rule. It appears this is related to the Postgres service in Azure specifically, as for a number of trials and many errors I attempted to use the standard available firewalls and firewall rules that are available in virtual networks. My understanding now is that the Postgres Servers exist outside of that paradigm and by relation to that have their own firewall rules.

This firewall rule basically attaches the firewall to the resource group, then the server itself, and allows internal access between the Postgres Server and the Hasura instance.

resource "azurerm_postgresql_firewall_rule" "pgfirewallrule" {
  name                = "allow-azure-internal"
  resource_group_name = azurerm_resource_group.adronsrg.name
  server_name         = azurerm_postgresql_server.logisticsserver.name
  start_ip_address    = "0.0.0.0"
  end_ip_address      = "0.0.0.0"
}

The last and final step is setting up the Hasura instance to work with the Postgres Server and the designated database now available.

To setup the Hasura instance I decided to go with the container service that Azure has. It provides a relatively inexpensive, easier to setup, and more concise way to setup the server than setting up an entire VM or full Kubernetes environment just to run a singular instance.

The first section sets up a public IP address, which of course I’ll need to change as the application is developed and I’ll need to provide an actual secured front end. But for now, to prove out the deployment, I’ve left it public, setup the DNS label, and set the OS type.

The next section in this resource I then outline the container details. The name of the container can be pretty much whatever you want it to be, it’s your designator. The image however is specifically hasura/graphql-engine. I’ve set the CPU and memory pretty low, at 0.5 and 1.5 respectively as I don’t suspect I’ll need a ton of horsepower just to test things out.

Next I set the port available to port 80. Then the environment variables HASURA_GRAPHQL_SERVER_PORT and HASURA_GRAPHQL_ENABLE_CONSOLE to that port to display the console there. Then finally that wild concatenated interpolated connection string that I have setup as an output variable – again specifically for testing – HASURA_GRAPHQL_DATABASE_URL.

resource "azurerm_container_group" "adronshasure" {
  name                = "adrons-hasura-logistics-data-layer"
  location            = azurerm_resource_group.adronsrg.location
  resource_group_name = azurerm_resource_group.adronsrg.name
  ip_address_type     = "public"
  dns_name_label      = "logisticsdatalayer"
  os_type             = "Linux"


  container {
    name   = "hasura-data-layer"
    image  = "hasura/graphql-engine"
    cpu    = "0.5"
    memory = "1.5"

    ports {
      port     = 80
      protocol = "TCP"
    }

    environment_variables = {
      HASURA_GRAPHQL_SERVER_PORT = 80
      HASURA_GRAPHQL_ENABLE_CONSOLE = true
    }
    secure_environment_variables = {
      HASURA_GRAPHQL_DATABASE_URL = "postgres://${var.username}%40${azurerm_postgresql_server.logisticsserver.name}:${var.password}@${azurerm_postgresql_server.logisticsserver.fqdn}:5432/${var.database}"
    }
  }

  tags = {
    environment = "datalayer"
  }
}

With all that setup it’s time to test. But first, just for clarity here’s the entire Terraform file contents.

provider "azurerm" {
  version = "=2.20.0"
  features {}
}

resource "azurerm_resource_group" "adronsrg" {
  name     = "adrons-rg"
  location = "westus2"
}

resource "azurerm_postgresql_server" "logisticsserver" {
  name = var.server
  location = azurerm_resource_group.adronsrg.location
  resource_group_name = azurerm_resource_group.adronsrg.name
  sku_name = "B_Gen5_2"

  storage_mb                   = 5120
  backup_retention_days        = 7
  geo_redundant_backup_enabled = false
  auto_grow_enabled            = true

  administrator_login          = var.username
  administrator_login_password = var.password
  version                      = "9.5"
  ssl_enforcement_enabled      = true
}

resource "azurerm_postgresql_database" "logisticsdb" {
  name                = var.database
  resource_group_name = azurerm_resource_group.adronsrg.name
  server_name         = azurerm_postgresql_server.logisticsserver.name
  charset             = "UTF8"
  collation           = "English_United States.1252"
}

resource "azurerm_postgresql_firewall_rule" "pgfirewallrule" {
  name                = "allow-azure-internal"
  resource_group_name = azurerm_resource_group.adronsrg.name
  server_name         = azurerm_postgresql_server.logisticsserver.name
  start_ip_address    = "0.0.0.0"
  end_ip_address      = "0.0.0.0"
}

resource "azurerm_container_group" "adronshasure" {
  name                = "adrons-hasura-logistics-data-layer"
  location            = azurerm_resource_group.adronsrg.location
  resource_group_name = azurerm_resource_group.adronsrg.name
  ip_address_type     = "public"
  dns_name_label      = "logisticsdatalayer"
  os_type             = "Linux"


  container {
    name   = "hasura-data-layer"
    image  = "hasura/graphql-engine"
    cpu    = "0.5"
    memory = "1.5"

    ports {
      port     = 80
      protocol = "TCP"
    }

    environment_variables = {
      HASURA_GRAPHQL_SERVER_PORT = 80
      HASURA_GRAPHQL_ENABLE_CONSOLE = true
    }
    secure_environment_variables = {
      HASURA_GRAPHQL_DATABASE_URL = "postgres://${var.username}%40${azurerm_postgresql_server.logisticsserver.name}:${var.password}@${azurerm_postgresql_server.logisticsserver.fqdn}:5432/${var.database}"
    }
  }

  tags = {
    environment = "datalayer"
  }
}

variable "database" {
  type = string
}

variable "server" {
  type = string
}

variable "username" {
  type = string
}

variable "password" {
  type = string
}

output "hasura_url" {
  value = "postgres://${var.username}%40${azurerm_postgresql_server.logisticsserver.name}:${var.password}@${azurerm_postgresql_server.logisticsserver.fqdn}:5432/${var.database}"
}

To run this, similarly to how I setup the dev environment, I’ve setup a startup and shutdown script. The startup script named prod-start.sh has the following commands. Note the $PUSERNAME and $PPASSWORD are derived from environment variables, where as the other two values are just inline.

cd terraform

terraform apply -auto-approve \
    -var 'server=logisticscoresystemsdb' \
    -var 'username='$PUSERNAME'' \
    -var 'password='$PPASSWORD'' \
    -var 'database=logistics'

For the full Terraform file check out this gist: https://gist.github.com/Adron/6d7cb4be3a22429d0ff8c8bd360f3ce2

Executing that script gives me results that, if everything goes right, looks similarly to this.

./prod-start.sh 
azurerm_resource_group.adronsrg: Creating...
azurerm_resource_group.adronsrg: Creation complete after 1s [id=/subscriptions/77ad15ff-226a-4aa9-bef3-648597374f9c/resourceGroups/adrons-rg]
azurerm_postgresql_server.logisticsserver: Creating...
azurerm_postgresql_server.logisticsserver: Still creating... [10s elapsed]
azurerm_postgresql_server.logisticsserver: Still creating... [20s elapsed]


...and it continues.

Do note that this process will take a different amount of time and is completely normal for it to take ~3 or more minutes. Once the server is done in the build process a lot of the other activities start to take place very quickly. Once it’s all done, toward the end of the output I get my hasura_url output variable so that I can confirm that it is indeed put together correctly! Now that this is preformed I can take next steps and remove that output variable, start to tighten security, and other steps. Which I’ll detail in a future blog post once more of the application is built.

... other output here ...


azurerm_container_group.adronshasure: Still creating... [40s elapsed]
azurerm_postgresql_database.logisticsdb: Still creating... [40s elapsed]
azurerm_postgresql_database.logisticsdb: Still creating... [50s elapsed]
azurerm_container_group.adronshasure: Still creating... [50s elapsed]
azurerm_postgresql_database.logisticsdb: Creation complete after 51s [id=/subscriptions/77ad15ff-226a-4aa9-bef3-648597374f9c/resourceGroups/adrons-rg/providers/Microsoft.DBforPostgreSQL/servers/logisticscoresystemsdb/databases/logistics]
azurerm_container_group.adronshasure: Still creating... [1m0s elapsed]
azurerm_container_group.adronshasure: Creation complete after 1m4s [id=/subscriptions/77ad15ff-226a-4aa9-bef3-648597374f9c/resourceGroups/adrons-rg/providers/Microsoft.ContainerInstance/containerGroups/adrons-hasura-logistics-data-layer]

Apply complete! Resources: 5 added, 0 changed, 0 destroyed.

Outputs:

hasura_url = postgres://postgres%40logisticscoresystemsdb:theSecretPassword!@logisticscoresystemsdb.postgres.database.azure.com:5432/logistics

Now if I navigate over to logisticsdatalayer.westus2.azurecontainer.io I can view the Hasura console! But where in the world is this fully qualified domain name (FQDN)? Well, the quickest way to find it is to navigate to the Azure portal and take a look at the details page of the container itself. In the upper right the FQDN is available as well as the IP that has been assigned to the container!

Navigating to that FQDN URI will bring up the Hasura console!

Next Steps

From here I’ll take up next steps in a subsequent post. I’ll get the container secured, map the user interface or CLI or whatever the application is that I build lined up to the API end points, and more!

References

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For JavaScript, Go, Python, Terraform, and more infrastructure, web dev, and coding in general I stream regularly on Twitch at https://twitch.tv/adronhall, post the VOD’s to YouTube along with entirely new tech and metal content at https://youtube.com/c/ThrashingCode.

WE DID IT! DataStax Astra is GA

Yesterday we finally went full GA (General Availability) with DataStax Astra. For the quick TLDR think of it as Apache Cassandra that you can spin up as a service and use in about a minute. I, as I wrote about some months ago, joined the engineering team to help build out the system! I quickly got to reconnoitering the role and working toward build out of features, which now are available to you!

With Astra, if you’ve used Apache Cassandra or DataStax Enterprise you can use the same drivers or CQL you’re familiar with. But with Astra there are two additional capabilities we’ve just released to use in connecting to and working with your databases:

  • Astra REST API
  • Astra GraphQL API

With the REST API there are a number of capabilities to add a table, return a list of all the tables, return content of a table, and delete a table. In addition to tables, there is functionality to retrieve, retrieve all, add, update, and delete columns. All of the standard CRUD (Create, Read, Update, and Delete) commands can also be performed.

For the GraphQL API it gives you the ability to perform CRUD actions and query with filters using the GraphQL syntax.

Authorization Token

To use either of these services, the first thing you’ll need is to create one of Astra’s time based authorization tokens. These tokens work until 30 minutes after the last call made with the token. Once expired a new token must be created. To create a token an HTTP POST to the API can be made, passing several header values, and username and password in the body of a POST request.

For an example of retrieving an authorization token I’ve put together a cURL request below. To get the URL for your database navigate to the Astra dashboard, and on the summary screen of any database the API Access URL’s are listed.

curl --request POST \
  --url https://12c3bb24-e2df-4db3-b993-14707303e57c-us-east1.apps.astra.datastax.com/api/rest/v1/auth \
  --header 'accept: */*' \
  --header 'content-type: application/json' \
  --header 'x-cassandra-request-id: 24cc6f6f-c1d9-4d4e-a4d3-e34c7d8b148a' \
  --data '{"username":"betterbot","password":"betterbot"}'

A successful request will return a result with the auth token that looks like this.

{"authToken":"9a38437f-7e03-49a8-bc5d-b4e305d7c1e8"}

With that authorization token we can now call actions against the REST, or GraphQL APIs.

Creating a Table via the Astra REST API

To create a table, we need a few key elements: The table name, whether it should create if a table exists or not, and column definitions with at least one column as a primary key. This is done by using JSON to pass this schema to the REST API. Here’s an example of some JSON that can be used to create a table.

'{"name":"products","ifNotExists":true,"columnDefinitions":
  [ {"name":"id","typeDefinition":"uuid","static":false},
    {"name":"name","typeDefinition":"text","static":false},
    {"name":"description","typeDefinition":"text","static":false},
    {"name":"price","typeDefinition":"decimal","static":false},
    {"name":"created","typeDefinition":"timestamp","static":false}],"primaryKey":
    {"partitionKey":["id"]},"tableOptions":{"defaultTimeToLive":0}}'

To use this JSON to create a table, just add the pertinent headers, insert your keyspace into the URL, and the x-cassandra-token and POST this data to the REST API end point. A cURL request to create the table would look like this.

curl --request POST \
  --url https://12c3bb24-e2df-4db3-b993-14707303e57c-us-east1.apps.astra.datastax.com/api/rest/v1/keyspaces/betterbotz/tables \
  --header 'accept: */*' \
  --header 'content-type: application/json' \
  --header 'x-cassandra-request-id: 07e37064-b265-4618-94ce-1c4606f584f9' \
  --header 'x-cassandra-token: ' \
  --data '{"name":"products","ifNotExists":true,"columnDefinitions":
  [ {"name":"id","typeDefinition":"uuid","static":false},
    {"name":"name","typeDefinition":"text","static":false},
    {"name":"description","typeDefinition":"text","static":false},
    {"name":"price","typeDefinition":"decimal","static":false},
    {"name":"created","typeDefinition":"timestamp","static":false}],"primaryKey":
    {"partitionKey":["id"]},"tableOptions":{"defaultTimeToLive":0}}'

Adding data via a GraphQL Mutation

At this point, with a data created, we can add, update, or delete data. The sample curl statement I’ve put together here is a sample GraphQL mutation to add a record to the products table.

curl --request POST \
  --url https://ba965c97-86f1-4d38-8cne-58qa1d2209a1-us-east1.apps.astra.datastax.com/api/rest/v1/keyspaces/betterbotz/tables/orders/rows \
  --header 'accept: application/json' \
  --header 'content-type: application/json' \
  --header 'x-cassandra-request-id: xyzaa27b-de8e-4afc-8431-8f06a326047d' \
  --header 'x-cassandra-token: 3ad1ca6a-62pq-4e1b-b273-4c08ea334909' \
  --data-raw '{"query":"mutation {superarms: insertProducts(value:{id:\"65cad0df-4fc8-42df-90e5-4effcd221ef7\"\n name:\"Arm Spec A1\" description:\"Powerful Robot Arm Spec A.\"price: \"9999.99\" created: \"2012-04-23T18:25:43.511Z\"}){value {name description price created}}}","variables":{}}'

For some other examples issuing a GraphQL mutation to add a record, just for good measure.

Go

package main

import (
  "fmt"
  "strings"
  "net/http"
  "io/ioutil"
)

func main() {

  url := "https://32c3bb24-e2df-4db3-b993-14707303e57c-us-east1.apps.astra.datastax.com/api/graphql"
  method := "POST"

  payload := strings.NewReader("{\"query\":\"mutation {superarms: updateProducts(value: {id:\\\"65cad0df-4fc8-42df-90e5-4effcd221ef7\\\" name:\\\"Arm Spec A3 [Newly Updated]\\\" description:\\\"Powerful Robot Arm Spec A3.\\\" price: \\\"19999.99\\\" created: \\\"2012-04-23T18:25:43.511Z\\\" }){value {id name description price created}}}\",\"variables\":{}}")

  client := &http.Client {
  }
  req, err := http.NewRequest(method, url, payload)

  if err != nil {
    fmt.Println(err)
  }
  req.Header.Add("accept", "*/*")
  req.Header.Add("content-type", "application/json")
  req.Header.Add("X-Cassandra-Token", "e85b3021-fb89-4f43-9ba6-a64a49ba5f68")
  req.Header.Add("Content-Type", "application/json")

  res, err := client.Do(req)
  defer res.Body.Close()
  body, err := ioutil.ReadAll(res.Body)

  fmt.Println(string(body))
}

Python

import requests

url = "https://32c3bb24-e2df-4db3-b993-14707303e57c-us-east1.apps.astra.datastax.com/api/graphql"

payload = "{\"query\":\"mutation {superarms: updateProducts(value: {id:\\\"65cad0df-4fc8-42df-90e5-4effcd221ef7\\\" name:\\\"Arm Spec A3 [Newly Updated]\\\" description:\\\"Powerful Robot Arm Spec A3.\\\" price: \\\"19999.99\\\" created: \\\"2012-04-23T18:25:43.511Z\\\" }){value {id name description price created}}}\",\"variables\":{}}"
headers = {
  'accept': '*/*',
  'content-type': 'application/json',
  'X-Cassandra-Token': 'e85b3021-fb89-4f43-9ba6-a64a49ba5f68',
  'Content-Type': 'application/json'
}

response = requests.request("POST", url, headers=headers, data = payload)

print(response.text.encode('utf8'))

Java

OkHttpClient client = new OkHttpClient().newBuilder()
  .build();
MediaType mediaType = MediaType.parse("application/json");
RequestBody body = RequestBody.create(mediaType, "{\"query\":\"mutation {superarms: updateProducts(value: {id:\\\"65cad0df-4fc8-42df-90e5-4effcd221ef7\\\" name:\\\"Arm Spec A3 [Newly Updated]\\\" description:\\\"Powerful Robot Arm Spec A3.\\\" price: \\\"19999.99\\\" created: \\\"2012-04-23T18:25:43.511Z\\\" }){value {id name description price created}}}\",\"variables\":{}}");
Request request = new Request.Builder()
  .url("https://32c3bb24-e2df-4db3-b993-14707303e57c-us-east1.apps.astra.datastax.com/api/graphql")
  .method("POST", body)
  .addHeader("accept", "*/*")
  .addHeader("content-type", "application/json")
  .addHeader("X-Cassandra-Token", "e85b3021-fb89-4f43-9ba6-a64a49ba5f68")
  .addHeader("Content-Type", "application/json")
  .build();
Response response = client.newCall(request).execute();

and C#!

var client = new RestClient("https://32c3bb24-e2df-4db3-b993-14707303e57c-us-east1.apps.astra.datastax.com/api/graphql");
client.Timeout = -1;
var request = new RestRequest(Method.POST);
request.AddHeader("accept", "*/*");
request.AddHeader("content-type", "application/json");
request.AddHeader("X-Cassandra-Token", "e85b3021-fb89-4f43-9ba6-a64a49ba5f68");
request.AddHeader("Content-Type", "application/json");
request.AddParameter("application/json", "{\"query\":\"mutation {superarms: updateProducts(value: {id:\\\"65cad0df-4fc8-42df-90e5-4effcd221ef7\\\" name:\\\"Arm Spec A3 [Newly Updated]\\\" description:\\\"Powerful Robot Arm Spec A3.\\\" price: \\\"19999.99\\\" created: \\\"2012-04-23T18:25:43.511Z\\\" }){value {id name description price created}}}\",\"variables\":{}}",
           ParameterType.RequestBody);
IRestResponse response = client.Execute(request);
Console.WriteLine(response.Content);

With that short tour, check out your free database today @ https://astra.datastax.com/register! Feel free to ping me on Twitter @Adron or here in comments, I’m open to and would love to discuss your experience!

Using Python’s Flask to Build a Basic API, Creating the didactic-engine-flask

I’ve worked with Python almost entirely from the maintenance programmer perspective. That is, I take other code written already, make edits, add features, and then redeploy it. I’ve created exactly zero greenfield applications in Python. That changes today however, with the creation of the didactic-engine-flask app!

Prerequisites

The only prereq to understanding this article is knowing git if you want to get the code, but it isn’t necessary. I’m starting this from ground zero. If you’re just getting started with Python, be sure to read “Getting Started With Python Right!” and “Unbreaking Python Through Virtual Environments” about setting up your environment. These two entries cover enough to ensure you won’t end up with broken, conflicted, and convoluted Python environments.

Mission: Build a Flask based API.

This post is about a singular thing, building an API with Flask. It won’t be about data modeling, databases, or wrapping middleware into the mix. It’s pure and simple Flask, with just the bare necessities needed to get an API working and responding appropriately requests. Continue reading “Using Python’s Flask to Build a Basic API, Creating the didactic-engine-flask” →

Cedrick Lunven on Creating an API for your database with Rest, GraphQL, gRPC

Here’s a talk Cedrick Lunven (who I have the fortune of working with!) about creating API’s for your database, your distributed database. He starts out with a few objectives for the talk:

  1. Provide you a working API implementing Rest, gRPC, and GraphQL.
  2. Give implementation details through Demo.
  3. Reveal hints to choose and WHY, (specifically to work with Databases)

Other topics include specific criteria around conceptual data models, shifting from relational to distributed columnar store, with differentiation between entities, relationships, queries, and their respective behaviors. All of this is pertinent to our Killrvideo reference application we have too.

Enjoy!

Framework: Strongloop’s Loopback

Recently I did a series for New Relic on three frameworks, both for APIs and web apps. I titled it “Evaluating Node.js Frameworks: hapi.js, Restify, and Geddy” and it is available via the New Relic Blog. To check out those frameworks give that blog entry a read, then below I’ve added one more framework to the list, Strongloop’s Loopback.

Strength: Very feature-rich generation of models, data structures and related enterprise-type needs. Solid enterprise-style API framework library.
Weakness: Complexity could be cumbersome unless it is needed. Not an immediate first choice for a startup going after lean and clean.
Great for: Enterprise API Services.

When I dove into StrongLoop, I immediately got the feel that I was using a fairly polished package of software. When installing with ‘sudo npm install -g strongloop’ I could easily see the other packages that are installed. But instead of the normal Node.js display of additional dependencies that are installed, the StrongLoop install displayed a number of additional options with a shiny ASCII logo.
Continue reading “Framework: Strongloop’s Loopback” →