Archive for category Bash Scripting
Automating Multi-Environment Kubernetes Virtual Clusters with Google Cloud DNS, Auth0, and Istio 1.0
Posted by Gary A. Stafford in Bash Scripting, Build Automation, Cloud, DevOps, Enterprise Software Development, GCP, Java Development, Kubernetes, Software Development on January 19, 2019
Kubernetes supports multiple virtual clusters within the same physical cluster. These virtual clusters are called Namespaces. Namespaces are a way to divide cluster resources between multiple users. Many enterprises use Namespaces to divide the same physical Kubernetes cluster into different virtual software development environments as part of their overall Software Development Lifecycle (SDLC). This practice is commonly used in ‘lower environments’ or ‘non-prod’ (not Production) environments. These environments commonly include Continous Integration and Delivery (CI/CD), Development, Integration, Testing/Quality Assurance (QA), User Acceptance Testing (UAT), Staging, Demo, and Hotfix. Namespaces provide a basic form of what is referred to as soft multi-tenancy.
Generally, the security boundaries and performance requirements between non-prod environments, within the same enterprise, are less restrictive than Production or Disaster Recovery (DR) environments. This allows for multi-tenant environments, while Production and DR are normally single-tenant environments. In order to approximate the performance characteristics of Production, the Performance Testing environment is also often isolated to a single-tenant. A typical enterprise would minimally have a non-prod, performance, production, and DR environment.
Using Namespaces to create virtual separation on the same physical Kubernetes cluster provides enterprises with more efficient use of virtual compute resources, reduces Cloud costs, eases the management burden, and often expedites and simplifies the release process.
Demonstration
In this post, we will re-examine the topic of virtual clusters, similar to the recent post, Managing Applications Across Multiple Kubernetes Environments with Istio: Part 1 and Part 2. We will focus specifically on automating the creation of the virtual clusters on GKE with Istio 1.0, managing the Google Cloud DNS records associated with the cluster’s environments, and enabling both HTTPS and token-based OAuth access to each environment. We will use the Storefront API for our demonstration, featured in the previous three posts, including Building a Microservices Platform with Confluent Cloud, MongoDB Atlas, Istio, and Google Kubernetes Engine.
Source Code
The source code for this post may be found on the gke branch of the storefront-kafka-docker GitHub repository.
git clone --branch gke --single-branch --depth 1 --no-tags \ https://github.com/garystafford/storefront-kafka-docker.git
Source code samples in this post are displayed as GitHub Gists, which may not display correctly on all mobile and social media browsers, such as LinkedIn.
This project contains all the code to deploy and configure the GKE cluster and Kubernetes resources.
To follow along, you will need to register your own domain, arrange for an Auth0, or alternative, authentication and authorization service, and obtain an SSL/TLS certificate.
SSL/TLS Wildcard Certificate
In the recent post, Securing Your Istio Ingress Gateway with HTTPS, we examined how to create and apply an SSL/TLS certificate to our GKE cluster, to secure communications. Although we are only creating a non-prod cluster, it is more and more common to use SSL/TLS everywhere, especially in the Cloud. For this post, I have registered a single wildcard certificate, *.api.storefront-demo.com. This certificate will cover the three second-level subdomains associated with the virtual clusters: dev.api.storefront-demo.com, test.api.storefront-demo.com, and uat.api.storefront-demo.com. Setting the environment name, such as dev.*, as the second-level subdomain of my storefront-demo domain, following the first level api.* subdomain, makes the use of a wildcard certificate much easier.
As shown below, my wildcard certificate contains the Subject Name and Subject Alternative Name (SAN) of *.api.storefront-demo.com. For Production, api.storefront-demo.com, I prefer to use a separate certificate.
Create GKE Cluster
With your certificate in hand, create the non-prod Kubernetes cluster. Below, the script creates a minimally-sized, three-node, multi-zone GKE cluster, running on GCP, with Kubernetes Engine cluster version 1.11.5-gke.5 and Istio on GKE version 1.0.3-gke.0. I have enabled the master authorized networks option to secure my GKE cluster master endpoint. For the demo, you can add your own IP address CIDR on line 9 (i.e. 1.2.3.4/32), or remove lines 30 – 31 to remove the restriction (gist).
- Lines 16–39: Create a 3-node, multi-zone GKE cluster with Istio;
- Line 48: Creates three non-prod Namespaces: dev, test, and uat;
- Lines 51–53: Enable Istio automatic sidecar injection within each Namespace;
If successful, the results should look similar to the output, below.
The cluster will contain a pool of three minimally-sized VMs, the Kubernetes nodes.
Deploying Resources
The Istio Gateway and three ServiceEntry resources are the primary resources responsible for routing the traffic from the ingress router to the Services, within the multiple Namespaces. Both of these resource types are new to Istio 1.0 (gist).
- Lines 9–16: Port config that only accepts HTTPS traffic on port 443 using TLS;
- Lines 18–20: The three subdomains being routed to the non-prod GKE cluster;
- Lines 28, 63, 98: The three subdomains being routed to the non-prod GKE cluster;
- Lines 39, 47, 65, 74, 82, 90, 109, 117, 125: Routing to FQDN of Storefront API Services within the three Namespaces;
Next, deploy the Istio and Kubernetes resources to the new GKE cluster. For the sake of brevity, we will deploy the same number of instances and the same version of each the three Storefront API services (Accounts, Orders, Fulfillment) to each of the three non-prod environments (dev, test, uat). In reality, you would have varying numbers of instances of each service, and each environment would contain progressive versions of each service, as part of the SDLC of each microservice (gist).
- Lines 13–14: Deploy the SSL/TLS certificate and the private key;
- Line 17: Deploy the Istio Gateway and three ServiceEntry resources;
- Lines 20–22: Deploy the Istio Authentication Policy resources each Namespace;
- Lines 26–37: Deploy the same set of resources to the dev, test, and uat Namespaces;
The deployed Storefront API Services should look as follows.
Google Cloud DNS
Next, we need to enable DNS access to the GKE cluster using Google Cloud DNS. According to Google, Cloud DNS is a scalable, reliable and managed authoritative Domain Name System (DNS) service running on the same infrastructure as Google. It has low latency, high availability, and is a cost-effective way to make your applications and services available to your users.
Whenever a new GKE cluster is created, a new Network Load Balancer is also created. By default, the load balancer’s front-end is an external IP address.
Using a forwarding rule, traffic directed at the external IP address is redirected to the load balancer’s back-end. The load balancer’s back-end is comprised of three VM instances, which are the three Kubernete nodes in the GKE cluster.
If you are following along with this post’s demonstration, we will assume you have a domain registered and configured with Google Cloud DNS. I am using the storefront-demo.com domain, which I have used in the last three posts to demonstrate Istio and GKE.
Google Cloud DNS has a fully functional web console, part of the Google Cloud Console. However, using the Cloud DNS web console is impractical in a DevOps CI/CD workflow, where Kubernetes clusters, Namespaces, and Workloads are ephemeral. Therefore we will use the following script. Within the script, we reset the IP address associated with the A records for each non-prod subdomains associated with storefront-demo.com domain (gist).
- Lines 23–25: Find the previous load balancer’s front-end IP address;
- Lines 27–29: Find the new load balancer’s front-end IP address;
- Line 35: Start the Cloud DNS transaction;
- Lines 37–47: Add the DNS record changes to the transaction;
- Line 49: Execute the Cloud DNS transaction;
The outcome of the script is shown below. Note how changes are executed as part of a transaction, by automatically creating a transaction.yaml file. The file contains the six DNS changes, three additions and three deletions. The command executes the transaction and then deletes the transaction.yaml file.
> sh ./part3_set_cloud_dns.sh
Old LB IP Address: 35.193.208.115 New LB IP Address: 35.238.196.231 Transaction started [transaction.yaml]. dev.api.storefront-demo.com. Record removal appended to transaction at [transaction.yaml]. Record addition appended to transaction at [transaction.yaml]. test.api.storefront-demo.com. Record removal appended to transaction at [transaction.yaml]. Record addition appended to transaction at [transaction.yaml]. uat.api.storefront-demo.com. Record removal appended to transaction at [transaction.yaml]. Record addition appended to transaction at [transaction.yaml]. Executed transaction [transaction.yaml] for managed-zone [storefront-demo-com-zone]. Created [https://www.googleapis.com/dns/v1/projects/gke-confluent-atlas/managedZones/storefront-demo-com-zone/changes/53]. ID START_TIME STATUS 55 2019-01-16T04:54:14.984Z pending
Based on my own domain and cluster details, the transaction.yaml file looks as follows. Again, note the six DNS changes, three additions, followed by three deletions (gist).
Confirm DNS Changes
Use the dig command to confirm the DNS records are now correct and that DNS propagation has occurred. The IP address returned by dig should be the external IP address assigned to the front-end of the Google Cloud Load Balancer.
> dig dev.api.storefront-demo.com +short 35.238.196.231
Or, all the three records.
echo \ "dev.api.storefront-demo.com\n" \ "test.api.storefront-demo.com\n" \ "uat.api.storefront-demo.com" \ > records.txt | dig -f records.txt +short 35.238.196.231 35.238.196.231 35.238.196.231
Optionally, more verbosely by removing the +short option.
> dig +nocmd dev.api.storefront-demo.com ;; Got answer: ;; ->>HEADER<<- opcode: QUERY, status: NOERROR, id: 30763 ;; flags: qr rd ra; QUERY: 1, ANSWER: 1, AUTHORITY: 0, ADDITIONAL: 1 ;; OPT PSEUDOSECTION: ; EDNS: version: 0, flags:; udp: 512 ;; QUESTION SECTION: ;dev.api.storefront-demo.com. IN A ;; ANSWER SECTION: dev.api.storefront-demo.com. 299 IN A 35.238.196.231 ;; Query time: 27 msec ;; SERVER: 8.8.8.8#53(8.8.8.8) ;; WHEN: Wed Jan 16 18:00:49 EST 2019 ;; MSG SIZE rcvd: 72
The resulting records in the Google Cloud DNS management console should look as follows.
JWT-based Authentication
As discussed in the previous post, Istio End-User Authentication for Kubernetes using JSON Web Tokens (JWT) and Auth0, it is typical to limit restrict access to the Kubernetes cluster, Namespaces within the cluster, or Services running within Namespaces to end-users, whether they are humans or other applications. In that previous post, we saw an example of applying a machine-to-machine (M2M) Istio Authentication Policy to only the uat Namespace. This scenario is common when you want to control access to resources in non-production environments, such as UAT, to outside test teams, accessing the uat Namespace through an external application. To simulate this scenario, we will apply the following Istio Authentication Policy to the uat Namespace. (gist).
For the dev and test Namespaces, we will apply an additional, different Istio Authentication Policy. This policy will protect against the possibility of dev and test M2M API consumers interfering with uat M2M API consumers and vice-versa. Below is the dev and test version of the Policy (gist).
Testing Authentication
Using Postman, with the ‘Bearer Token’ type authentication method, as detailed in the previous post, a call a Storefront API resource in the uat Namespace should succeed. This also confirms DNS and HTTPS are working properly.
The dev and test Namespaces require different authentication. Trying to use no Authentication, or authenticating as a UAT API consumer, will result in a 401 Unauthorized HTTP status, along with the Origin authentication failed. error message.
Conclusion
In this brief post, we demonstrated how to create a GKE cluster with Istio 1.0.x, containing three virtual clusters, or Namespaces. Each Namespace represents an environment, which is part of an application’s SDLC. We enforced HTTP over TLS (HTTPS) using a wildcard SSL/TLS certificate. We also enforced end-user authentication using JWT-based OAuth 2.0 with Auth0. Lastly, we provided user-friendly DNS routing to each environment, using Google Cloud DNS. Short of a fully managed API Gateway, like Apigee, and automating the execution of the scripts with Jenkins or Spinnaker, this cluster is ready to provide a functional path to Production for developing our Storefront API.
All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.
Istio End-User Authentication for Kubernetes using JSON Web Tokens (JWT) and Auth0
Posted by Gary A. Stafford in Bash Scripting, Cloud, DevOps, Enterprise Software Development, GCP, Kubernetes, Software Development on January 6, 2019
In the recent post, Building a Microservices Platform with Confluent Cloud, MongoDB Atlas, Istio, and Google Kubernetes Engine, we built and deployed a microservice-based, cloud-native API to Google Kubernetes Engine, with Istio 1.0.x, on Google Cloud Platform. For brevity, we intentionally omitted a few key features required to operationalize and secure the API. These missing features included HTTPS, user authentication, request quotas, request throttling, and the integration of a full lifecycle API management tool, like Google Apigee.
In a follow-up post, Securing Your Istio Ingress Gateway with HTTPS, we disabled HTTP access to the API running on the GKE cluster. We then enabled bidirectional encryption of communications between a client and GKE cluster with HTTPS.
In this post, we will further enhance the security of the Storefront Demo API by enabling Istio end-user authentication using JSON Web Token-based credentials. Using JSON Web Tokens (JWT), pronounced ‘jot’, will allow Istio to authenticate end-users calling the Storefront Demo API. We will use Auth0, an Authentication-as-a-Service provider, to generate JWT tokens for registered Storefront Demo API consumers, and to validate JWT tokens from Istio, as part of an OAuth 2.0 token-based authorization flow.
JSON Web Tokens
Token-based authentication, according to Auth0, works by ensuring that each request to a server is accompanied by a signed token which the server verifies for authenticity and only then responds to the request. JWT, according to JWT.io, is an open standard (RFC 7519) that defines a compact and self-contained way for securely transmitting information between parties as a JSON object. This information can be verified and trusted because it is digitally signed. Other common token types include Simple Web Tokens (SWT) and Security Assertion Markup Language Tokens (SAML).
JWTs can be signed using a secret with the Hash-based Message Authentication Code (HMAC) algorithm, or a public/private key pair using Rivest–Shamir–Adleman (RSA) or Elliptic Curve Digital Signature Algorithm (ECDSA). Authorization is the most common scenario for using JWT. Within the token payload, you can easily specify user roles and permissions as well as resources that the user can access.
A registered API consumer makes an initial request to the Authorization server, in which they exchange some form of credentials for a token. The JWT is associated with a set of specific user roles and permissions. Each subsequent request will include the token, allowing the user to access authorized routes, services, and resources that are permitted with that token.
Auth0
To use JWTs for end-user authentication with Istio, we need a way to authenticate credentials associated with specific users and exchange those credentials for a JWT. Further, we need a way to validate the JWTs from Istio. To meet these requirements, we will use Auth0. Auth0 provides a universal authentication and authorization platform for web, mobile, and legacy applications. According to G2 Crowd, competitors to Auth0 in the Customer Identity and Access Management (CIAM) Software category include Okta, Microsoft Azure Active Directory (AD) and AD B2C, Salesforce Platform: Identity, OneLogin, Idaptive, IBM Cloud Identity Service, and Bitium.
Auth0 currently offers four pricing plans: Free, Developer, Developer Pro, and Enterprise. Subscriptions to plans are on a monthly or discounted yearly basis. For this demo’s limited requirements, you need only use Auth0’s Free Plan.
Client Credentials Grant
The OAuth 2.0 protocol defines four flows, or grants types, to get an Access Token, depending on the application architecture and the type of end-user. We will be simulating a third-party, external application that needs to consume the Storefront API, using the Client Credentials grant type. According to Auth0, The Client Credentials Grant, defined in The OAuth 2.0 Authorization Framework RFC 6749, section 4.4, allows an application to request an Access Token using its Client Id and Client Secret. It is used for non-interactive applications, such as a CLI, a daemon, or a Service running on your backend, where the token is issued to the application itself, instead of an end user.
With Auth0, we need to create two types of entities, an Auth0 API and an Auth0 Application. First, we define an Auth0 API, which represents the Storefront API we are securing. Second, we define an Auth0 Application, a consumer of our API. The Application is associated with the API. This association allows the Application (consumer of the API) to authenticate with Auth0 and receive a JWT. Note there is no direct integration between Auth0 and Istio or the Storefront API. We are facilitating a decoupled, mutual trust relationship between Auth0, Istio, and the registered end-user application consuming the API.
Start by creating a new Auth0 API, the ‘Storefront Demo API’. For this demo, I used my domain’s URL as the Identifier. For use with Istio, choose RS256 (RSA Signature with SHA-256), an asymmetric algorithm that uses a public/private key pair, as opposed to the HS256 symmetric algorithm. With RS256, Auth0 will use the same private key to both create the signature and to validate it. Auth0 has published a good post on the use of RS256 vs. HS256 algorithms.
Scopes
Auth0 allows granular access control to your API through the use of Scopes. The permissions represented by the Access Token in OAuth 2.0 terms are known as scopes, According to Auth0. The scope parameter allows the application to express the desired scope of the access request. The scope parameter can also be used by the authorization server in the response to indicate which scopes were actually granted.
Although it is necessary to define and assign at least one scope to our Auth0 Application, we will not actually be using those scopes to control fine-grain authorization to resources within the Storefront API. In this demo, if an end-user is authenticated, they will be authorized to access all Storefront API resources.
Machine to Machine Applications
Next, define a new Auth0 Machine to Machine (M2M) Application, ‘Storefront Demo API Consumer 1’.
Next, authorize the new M2M Application to request access to the new Storefront Demo API. Again, we are not using scopes, but at least one scope is required, or you will not be able to authenticate, later.
Each M2M Application has a unique Client ID and Client Secret, which are used to authenticate with the Auth0 server and retrieve a JWT.
Multiple M2M Applications may be authorized to request access to APIs.
In the Endpoints tab of the Advanced Application Settings, there are a series of OAuth URLs. To authorize our new M2M Application to consume the Storefront Demo API, we need the ‘OAuth Authorization URL’.
Testing Auth0
To test the Auth0 JWT-based authentication and authorization workflow, I prefer to use Postman. Conveniently, Auth0 provides a Postman Collection with all the HTTP request you will need, already built. Use the Client Credentials POST request. The grant_type header value will always be client_credentials. You will need to supply the Auth0 Application’s Client ID and Client Secret as the client_id and client_secret header values. The audience header value will be the API Identifier you used to create the Auth0 API earlier.
If the HTTP request is successful, you should receive a JWT access_token in response, which will allow us to authenticate with the Storefront API, later. Note the scopes you defined with Auth0 are also part of the response, along with the token’s TTL.
jwt.io Debugger
For now, test the JWT using the jwt.io Debugger page. If everything is working correctly, the JWT should be successfully validated.
Istio Authentication Policy
To enable Istio end-user authentication using JWT with Auth0, we add an Istio Policy authentication resource to the existing set of deployed resources. You have a few choices for end-user authentication, such as:
- Applied globally, to all Services across all Namespaces via the Istio Ingress Gateway;
- Applied locally, to all Services within a specific Namespace (i.e.
uat); - Applied locally, to a single Service or Services within a specific Namespace (i.e
prod.accounts);
In reality, since you would likely have more than one registered consumer of the API, with different roles, you would have more than one Authentication Policy applied the cluster.
For this demo, we will enable global end-user authentication to the Storefront API, using JWTs, at the Istio Ingress Gateway. To create an Istio Authentication Policy resource, we use the Istio Authentication API version authentication.istio.io/v1alpha1(gist).
The single audiences YAML map value is the same Audience header value you used in your earlier Postman request, which was the API Identifier you used to create the Auth0 Storefront Demo API earlier. The issuer YAML scalar value is Auth0 M2M Application’s Domain value, found in the ‘Storefront Demo API Consumer 1’ Settings tab. The jwksUri YAML scalar value is the JSON Web Key Set URL value, found in the Endpoints tab of the Advanced Application Settings.
The JSON Web Key Set URL is a publicly accessible endpoint. This endpoint will be accessed by Istio to obtain the public key used to authenticate the JWT.
Assuming you have already have deployed the Storefront API to the GKE cluster, simply apply the new Istio Policy. We should now have end-user authentication enabled on the Istio Ingress Gateway using JSON Web Tokens.
kubectl apply -f ./resources/other/ingressgateway-jwt-policy.yaml
Finer-grain Authentication
If you need finer-grain authentication of resources, alternately, you can apply an Istio Authentication Policy across a Namespace and to a specific Service or Services. Below, we see an example of applying a Policy to only the uat Namespace. This scenario is common when you want to control access to resources in non-production environments, such as UAT, to outside test teams or a select set of external beta-testers. According to Istio, to apply Namespace-wide end-user authentication, across a single Namespace, it is necessary to name the Policy, default (gist).
Below, we see an even finer-grain Policy example, scoped to just the accounts Service within just the prod Namespace. This scenario is common when you have an API consumer whose role only requires access to a portion of the API. For example, a marketing application might only require access to the accounts Service, but not the orders or fulfillment Services (gist).
Test Authentication
To test end-user authentication, first, call any valid Storefront Demo API endpoint, without supplying a JWT for authorization. You should receive a ‘401 Unauthorized’ HTTP response code, along with an Origin authentication failed. message in the response body. This means the Storefront Demo API is now inaccessible unless the API consumer supplies a JWT, which can be successfully validated by Istio.
Next, add authorization to the Postman request by selecting the ‘Bearer Token’ type authentication method. Copy and paste the JWT (access_token) you received earlier from the Client Credentials request. This will add an Authorization request header. In curl, the request header would look as follows (gist).
Make the request with Postman. If the Istio Policy is applied correctly, the request should now receive a successful response from the Storefront API. A successful response indicates that Istio successfully validated the JWT, located in the Authorization header, against the Auth0 Authorization Server. Istio then allows the user, the ‘Storefront Demo API Consumer 1’ application, access to all Storefront API resources.
Troubleshooting
Istio has several pages of online documentation on troubleshooting authentication issues. One of the first places to look for errors, if your end-user authentication is not working, but the JWT is valid, is the Istio Pilot logs. The core component used for traffic management in Istio, Pilot, manages and configures all the Envoy proxy instances deployed in a particular Istio service mesh. Pilot distributes authentication policies, like our new end-user authentication policy, and secure naming information to the proxies.
Below, in Google Stackdriver Logging, we see typical log entries indicating the Pilot was unable to retrieve the JWT public key (recall we are using RS256 public/private key pair asymmetric algorithm). This particular error was due to a typo in the Istio Policy authentication resource YAML file.
Below we see an Istio Mixer log entry containing details of a Postman request to the Accounts Storefront service /accounts/customers/summary endpoint. According to Istio, Mixer is the Istio component responsible for providing policy controls and telemetry collection. Note the apiClaims section of the textPayload of the log entry, corresponds to the Payload Segment of the JWT passed in this request. The log entry clearly shows that the JWT was decoded and validated by Istio, before forwarding the request to the Accounts Service.
Conclusion
In this brief post, we added end-user authentication to our Storefront Demo API, running on GKE with Istio. Although still not Production-ready, we have secured the Storefront API with both HTTPS client-server encryption and JSON Web Token-based authorization. Next steps would be to add mutual TLS (mTLS) and a fully-managed API Gateway in front of the Storefront API GKE cluster, to provide advanced API features, like caching, quotas and rate limits.
All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.
Securing Your Istio Ingress Gateway with HTTPS
Posted by Gary A. Stafford in Bash Scripting, Cloud, Enterprise Software Development, GCP, Software Development on January 3, 2019
In the last post, Building a Microservices Platform with Confluent Cloud, MongoDB Atlas, Istio, and Google Kubernetes Engine, we built and deployed a microservice-based, cloud-native API to Google Kubernetes Engine (GKE), with Istio 1.0, on Google Cloud Platform (GCP). For brevity, we neglected a few key API features, required in Production, including HTTPS, OAuth for authentication, request quotas, request throttling, and the integration of a full lifecycle API management tool, like Google Apigee.
In this brief post, we will revisit the previous post’s project. We will disable HTTP, and secure the GKE cluster with HTTPS, using simple TLS, as opposed to mutual TLS authentication (mTLS). This post assumes you have created the GKE cluster and deployed the Storefront API and its associated resources, as explained in the previous post.
What is HTTPS?
According to Wikipedia, Hypertext Transfer Protocol Secure (HTTPS) is an extension of the Hypertext Transfer Protocol (HTTP) for securing communications over a computer network. In HTTPS, the communication protocol is encrypted using Transport Layer Security (TLS), or, formerly, its predecessor, Secure Sockets Layer (SSL). The protocol is therefore also often referred to as HTTP over TLS, or HTTP over SSL.
Further, according to Wikipedia, the principal motivation for HTTPS is authentication of the accessed website and protection of the privacy and integrity of the exchanged data while in transit. It protects against man-in-the-middle attacks. The bidirectional encryption of communications between a client and server provides a reasonable assurance that one is communicating without interference by attackers with the website that one intended to communicate with, as opposed to an impostor.
Public Key Infrastructure
According to Comodo, both the TLS and SSL protocols use what is known as an asymmetric Public Key Infrastructure (PKI) system. An asymmetric system uses two keys to encrypt communications, a public key and a private key. Anything encrypted with the public key can only be decrypted by the private key and vice-versa.
Again, according to Wikipedia, a PKI is an arrangement that binds public keys with respective identities of entities, like people and organizations. The binding is established through a process of registration and issuance of certificates at and by a certificate authority (CA).
SSL/TLS Digital Certificate
Again, according to Comodo, when you request an HTTPS connection to a webpage, the website will initially send its SSL certificate to your browser. This certificate contains the public key needed to begin the secure session. Based on this initial exchange, your browser and the website then initiate the SSL handshake (actually, TLS handshake). The handshake involves the generation of shared secrets to establish a uniquely secure connection between yourself and the website. When a trusted SSL digital certificate is used during an HTTPS connection, users will see the padlock icon in the browser’s address bar.
Registered Domain
In order to secure an SSL Digital Certificate, required to enable HTTPS with the GKE cluster, we must first have a registered domain name. For the last post, and this post, I am using my own personal domain, storefront-demo.com. The domain’s primary A record (‘@’) and all sub-domain A records, such as api.dev, are all resolve to the external IP address on the front-end of the GCP load balancer.
For DNS hosting, I happen to be using Azure DNS to host the domain, storefront-demo.com. All DNS hosting services basically work the same way, whether you chose Azure, AWS, GCP, or another third party provider.
Let’s Encrypt
If you have used Let’s Encrypt before, then you know how easy it is to get free SSL/TLS Certificates. Let’s Encrypt is the first free, automated, and open certificate authority (CA) brought to you by the non-profit Internet Security Research Group (ISRG).
According to Let’s Encrypt, to enable HTTPS on your website, you need to get a certificate from a Certificate Authority (CA); Let’s Encrypt is a CA. In order to get a certificate for your website’s domain from Let’s Encrypt, you have to demonstrate control over the domain. With Let’s Encrypt, you do this using software that uses the ACME protocol, which typically runs on your web host. If you have generated certificates with Let’s Encrypt, you also know the domain validation by installing the Certbot ACME client can be a bit daunting, depending on your level of access and technical expertise.
SSL For Free
This is where SSL For Free comes in. SSL For Free acts as a proxy of sorts to Let’s Encrypt. SSL For Free generates certificates using their ACME server by using domain validation. Private Keys are generated in your browser and never transmitted.
SSL For Free offers three domain validation methods:
- Automatic FTP Verification: Enter FTP information to automatically verify the domain;
- Manual Verification: Upload verification files manually to your domain to verify ownership;
- Manual Verification (DNS): Add
TXTrecords to your DNS server;
Using the third domain validation method, manual verification using DNS, is extremely easy, if you have access to your domain’s DNS recordset.
SSL For Free provides TXT records for each domain you are adding to the certificate. Below, I am adding a single domain to the certificate.
Add the TXT records to your domain’s recordset. Shown below is an example of a single TXT record that has been to my recordset using the Azure DNS service.
SSL For Free then uses the TXT record to validate your domain is actually yours.
With the TXT record in place and validation successful, you can download a ZIPped package containing the certificate, private key, and CA bundle. The CA bundle containing the end-entity root and intermediate certificates.
Decoding PEM Encoded SSL Certificate
Using a tool like SSL Shopper’s Certificate Decoder, we can decode our Privacy-Enhanced Mail (PEM) encoded SSL certificates and view all of the certificate’s information. Decoding the information contained in my certificate.crt, I see the following.
Certificate Information: Common Name: api.dev.storefront-demo.com Subject Alternative Names: api.dev.storefront-demo.com Valid From: December 26, 2018 Valid To: March 26, 2019 Issuer: Let's Encrypt Authority X3, Let's Encrypt Serial Number: 03a5ec86bf79de65fb679ee7741ba07df1e4
Decoding the information contained in my ca_bundle.crt, I see the following.
Certificate Information: Common Name: Let's Encrypt Authority X3 Organization: Let's Encrypt Country: US Valid From: March 17, 2016 Valid To: March 17, 2021 Issuer: DST Root CA X3, Digital Signature Trust Co. Serial Number: 0a0141420000015385736a0b85eca708
The Let’s Encrypt intermediate certificate is also cross-signed by another certificate authority, IdenTrust, whose root is already trusted in all major browsers. IdenTrust cross-signs the Let’s Encrypt intermediate certificate using their DST Root CA X3. Thus, the Issuer, shown above.
Configure Istio Ingress Gateway
Unzip the sslforfree.zip package and place the individual files in a location you have access to from the command line.
unzip -l ~/Downloads/sslforfree.zip
Archive: /Users/garystafford/Downloads/sslforfree.zip
Length Date Time Name
--------- ---------- ----- ----
1943 12-26-2018 18:35 certificate.crt
1707 12-26-2018 18:35 private.key
1646 12-26-2018 18:35 ca_bundle.crt
--------- -------
5296 3 files
Following the process outlined in the Istio documentation, Securing Gateways with HTTPS, run the following command. This will place the istio-ingressgateway-certs Secret in the istio-system namespace, on the GKE cluster.
kubectl create -n istio-system secret tls istio-ingressgateway-certs \ --key path_to_files/sslforfree/private.key \ --cert path_to_files/sslforfree/certificate.crt
Modify the existing Istio Gateway from the previous project, istio-gateway.yaml. Remove the HTTP port configuration item and replace with the HTTPS protocol item (gist). Redeploy the Istio Gateway to the GKE cluster.
By deploying the new istio-ingressgateway-certs Secret and redeploying the Gateway, the certificate and private key were deployed to the /etc/istio/ingressgateway-certs/ directory of the istio-proxy container, running on the istio-ingressgateway Pod. To confirm both the certificate and private key were deployed correctly, run the following command.
kubectl exec -it -n istio-system \
$(kubectl -n istio-system get pods \
-l istio=ingressgateway \
-o jsonpath='{.items[0].metadata.name}') \
-- ls -l /etc/istio/ingressgateway-certs/
lrwxrwxrwx 1 root root 14 Jan 2 17:53 tls.crt -> ..data/tls.crt
lrwxrwxrwx 1 root root 14 Jan 2 17:53 tls.key -> ..data/tls.key
That’s it. We should now have simple TLS enabled on the Istio Gateway, providing bidirectional encryption of communications between a client (Storefront API consumer) and server (Storefront API running on the GKE cluster). Users accessing the API will now have to use HTTPS.
Confirm HTTPS is Working
After completing the deployment, as outlined in the previous post, test the Storefront API by using HTTP, first. Since we removed the HTTP port item configuration in the Istio Gateway, the HTTP request should fail with a connection refused error. Insecure traffic is no longer allowed by the Storefront API.
Now try switching from HTTP to HTTPS. The page should be displayed and the black lock icon should appear in the browser’s address bar. Clicking on the lock icon, we will see the SSL certificate, used by the GKE cluster is valid.
By clicking on the valid certificate indicator, we may observe more details about the SSL certificate, used to secure the Storefront API. Observe the certificate is issued by Let’s Encrypt Authority X3. It is valid for 90 days from its time of issuance. Let’s Encrypt only issues certificates with a 90-day lifetime. Observe the public key uses SHA-256 with RSA (Rivest–Shamir–Adleman) encryption.
In Chrome, we can also use the Developer Tools Security tab to inspect the certificate. The certificate is recognized as valid and trusted. Also important, note the connection to this Storefront API is encrypted and authenticated using TLS 1.2 (a strong protocol), ECDHE_RSA with X25519 (a strong key exchange), and AES_128_GCM (a strong cipher). According to How’s My SSL?, TLS 1.2 is the latest version of TLS. The TLS 1.2 protocol provides access to advanced cipher suites that support elliptical curve cryptography and AEAD block cipher modes. TLS 1.2 is an improvement on previous TLS 1.1, 1.0, and SSLv3 or earlier.
Lastly, the best way to really understand what is happening with HTTPS, the Storefront API, and Istio, is verbosely curl an API endpoint.
curl -Iv https://api.dev.storefront-demo.com/accounts/
Using the above curl command, we can see exactly how the client successfully verifies the server, negotiates a secure HTTP/2 connection (HTTP/2 over TLS 1.2), and makes a request (gist).
- Line 3: DNS resolution of the URL to the external IP address of the GCP load-balancer
- Line 3: HTTPS traffic is routed to TCP port 443
- Lines 4 – 5: Application-Layer Protocol Negotiation (ALPN) starts to occur with the server
- Lines 7 – 9: Certificate to verify located
- Lines 10 – 20: TLS handshake is performed and is successful using TLS 1.2 protocol
- Line 20: CHACHA is the stream cipher and POLY1305 is the authenticator in the Transport Layer Security (TLS) 1.2 protocol ChaCha20-Poly1305 Cipher Suite
- Lines 22 – 27: SSL certificate details
- Line 28: Certificate verified
- Lines 29 – 38: Establishing HTTP/2 connection with the server
- Lines 33 – 36: Request headers
- Lines 39 – 46: Response headers containing the expected 204 HTTP return code
Mutual TLS
Istio also supports mutual authentication using the TLS protocol, known as mutual TLS authentication (mTLS), between external clients and the gateway, as outlined in the Istio 1.0 documentation. According to Wikipedia, mutual authentication or two-way authentication refers to two parties authenticating each other at the same time. Mutual authentication a default mode of authentication in some protocols (IKE, SSH), but optional in TLS.
Again, according to Wikipedia, by default, TLS only proves the identity of the server to the client using X.509 certificates. The authentication of the client to the server is left to the application layer. TLS also offers client-to-server authentication using client-side X.509 authentication. As it requires provisioning of the certificates to the clients and involves less user-friendly experience, it is rarely used in end-user applications. Mutual TLS is much more widespread in B2B applications, where a limited number of programmatic clients are connecting to specific web services. The operational burden is limited and security requirements are usually much higher as compared to consumer environments.
This form of mutual authentication would be beneficial if we had external applications or other services outside our GKE cluster, consuming our API. Using mTLS, we could further enhance the security of those types of interactions.
All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.
Building a Microservices Platform with Confluent Cloud, MongoDB Atlas, Istio, and Google Kubernetes Engine
Posted by Gary A. Stafford in Bash Scripting, Cloud, DevOps, Enterprise Software Development, GCP, Java Development, Python, Software Development on December 28, 2018
Leading SaaS providers have sufficiently matured the integration capabilities of their product offerings to a point where it is now reasonable for enterprises to architect multi-vendor, single- and multi-cloud Production platforms, without re-engineering existing cloud-native applications. In previous posts, we have integrated other SaaS products, including as MongoDB Atlas fully-managed MongoDB-as-a-service, ElephantSQL fully-manage PostgreSQL-as-a-service, and CloudAMQP RabbitMQ-as-a-service, into cloud-native applications on Azure, AWS, GCP, and PCF.
In this post, we will build and deploy an existing, Spring Framework, microservice-based, cloud-native API to Google Kubernetes Engine (GKE), replete with Istio 1.0, on Google Cloud Platform (GCP). The API will rely on Confluent Cloud to provide a fully-managed, Kafka-based messaging-as-a-service (MaaS). Similarly, the API will rely on MongoDB Atlas to provide a fully-managed, MongoDB-based Database-as-a-service (DBaaS).
Background
In a previous two-part post, Using Eventual Consistency and Spring for Kafka to Manage a Distributed Data Model: Part 1 and Part 2, we examined the role of Apache Kafka in an event-driven, eventually consistent, distributed system architecture. The system, an online storefront RESTful API simulation, was composed of multiple, Java Spring Boot microservices, each with their own MongoDB database. The microservices used a publish/subscribe model to communicate with each other using Kafka-based messaging. The Spring services were built using the Spring for Apache Kafka and Spring Data MongoDB projects.
Given the use case of placing an order through the Storefront API, we examined the interactions of three microservices, the Accounts, Fulfillment, and Orders service. We examined how the three services used Kafka to communicate state changes to each other, in a fully-decoupled manner.
The Storefront API’s microservices were managed behind an API Gateway, Netflix’s Zuul. Service discovery and load balancing were handled by Netflix’s Eureka. Both Zuul and Eureka are part of the Spring Cloud Netflix project. In that post, the entire containerized system was deployed to Docker Swarm.
Developing the services, not operationalizing the platform, was the primary objective of the previous post.
Featured Technologies
The following technologies are featured prominently in this post.
Confluent Cloud
In May 2018, Google announced a partnership with Confluence to provide Confluent Cloud on GCP, a managed Apache Kafka solution for the Google Cloud Platform. Confluent, founded by the creators of Kafka, Jay Kreps, Neha Narkhede, and Jun Rao, is known for their commercial, Kafka-based streaming platform for the Enterprise.
Confluent Cloud is a fully-managed, cloud-based streaming service based on Apache Kafka. Confluent Cloud delivers a low-latency, resilient, scalable streaming service, deployable in minutes. Confluent deploys, upgrades, and maintains your Kafka clusters. Confluent Cloud is currently available on both AWS and GCP.
Confluent Cloud offers two plans, Professional and Enterprise. The Professional plan is optimized for projects under development, and for smaller organizations and applications. Professional plan rates for Confluent Cloud start at $0.55/hour. The Enterprise plan adds full enterprise capabilities such as service-level agreements (SLAs) with a 99.95% uptime and virtual private cloud (VPC) peering. The limitations and supported features of both plans are detailed, here.
MongoDB Atlas
Similar to Confluent Cloud, MongoDB Atlas is a fully-managed MongoDB-as-a-Service, available on AWS, Azure, and GCP. Atlas, a mature SaaS product, offers high-availability, uptime SLAs, elastic scalability, cross-region replication, enterprise-grade security, LDAP integration, BI Connector, and much more.
MongoDB Atlas currently offers four pricing plans, Free, Basic, Pro, and Enterprise. Plans range from the smallest, M0-sized MongoDB cluster, with shared RAM and 512 MB storage, up to the massive M400 MongoDB cluster, with 488 GB of RAM and 3 TB of storage.
MongoDB Atlas has been featured in several past posts, including Deploying and Configuring Istio on Google Kubernetes Engine (GKE) and Developing Applications for the Cloud with Azure App Services and MongoDB Atlas.
Kubernetes Engine
According to Google, Google Kubernetes Engine (GKE) provides a fully-managed, production-ready Kubernetes environment for deploying, managing, and scaling your containerized applications using Google infrastructure. GKE consists of multiple Google Compute Engine instances, grouped together to form a cluster.
A forerunner to other managed Kubernetes platforms, like EKS (AWS), AKS (Azure), PKS (Pivotal), and IBM Cloud Kubernetes Service, GKE launched publicly in 2015. GKE was built on Google’s experience of running hyper-scale services like Gmail and YouTube in containers for over 12 years.
GKE’s pricing is based on a pay-as-you-go, per-second-billing plan, with no up-front or termination fees, similar to Confluent Cloud and MongoDB Atlas. Cluster sizes range from 1 – 1,000 nodes. Node machine types may be optimized for standard workloads, CPU, memory, GPU, or high-availability. Compute power ranges from 1 – 96 vCPUs and memory from 1 – 624 GB of RAM.
Demonstration
In this post, we will deploy the three Storefront API microservices to a GKE cluster on GCP. Confluent Cloud on GCP will replace the previous Docker-based Kafka implementation. Similarly, MongoDB Atlas will replace the previous Docker-based MongoDB implementation.
Kubernetes and Istio 1.0 will replace Netflix’s Zuul and Eureka for API management, load-balancing, routing, and service discovery. Google Stackdriver will provide logging and monitoring. Docker Images for the services will be stored in Google Container Registry. Although not fully operationalized, the Storefront API will be closer to a Production-like platform, than previously demonstrated on Docker Swarm.
For brevity, we will not enable standard API security features like HTTPS, OAuth for authentication, and request quotas and throttling, all of which are essential in Production. Nor, will we integrate a full lifecycle API management tool, like Google Apigee.
Source Code
The source code for this demonstration is contained in four separate GitHub repositories, storefront-kafka-docker, storefront-demo-accounts, storefront-demo-orders, and, storefront-demo-fulfillment. However, since the Docker Images for the three storefront services are available on Docker Hub, it is only necessary to clone the storefront-kafka-docker project. This project contains all the code to deploy and configure the GKE cluster and Kubernetes resources (gist).
Source code samples in this post are displayed as GitHub Gists, which may not display correctly on all mobile and social media browsers.
Setup Process
The setup of the Storefront API platform is divided into a few logical steps:
- Create the MongoDB Atlas cluster;
- Create the Confluent Cloud Kafka cluster;
- Create Kafka topics;
- Modify the Kubernetes resources;
- Modify the microservices to support Confluent Cloud configuration;
- Create the GKE cluster with Istio on GCP;
- Apply the Kubernetes resources to the GKE cluster;
- Test the Storefront API, Kafka, and MongoDB are functioning properly;
MongoDB Atlas Cluster
This post assumes you already have a MongoDB Atlas account and an existing project created. MongoDB Atlas accounts are free to set up if you do not already have one. Account creation does require the use of a Credit Card.
For minimal latency, we will be creating the MongoDB Atlas, Confluent Cloud Kafka, and GKE clusters, all on the Google Cloud Platform’s us-central1 Region. Available GCP Regions and Zones for MongoDB Atlas, Confluent Cloud, and GKE, vary, based on multiple factors.
For this demo, I suggest creating a free, M0-sized MongoDB cluster. The M0-sized 3-data node cluster, with shared RAM and 512 MB of storage, and currently running MongoDB 4.0.4, is fine for individual development. The us-central1 Region is the only available US Region for the free-tier M0-cluster on GCP. An M0-sized Atlas cluster may take between 7-10 minutes to provision.
MongoDB Atlas’ Web-based management console provides convenient links to cluster details, metrics, alerts, and documentation.
Once the cluster is ready, you can review details about the cluster and each individual cluster node.
In addition to the account owner, create a demo_user account. This account will be used to authenticate and connect with the MongoDB databases from the storefront services. For this demo, we will use the same, single user account for all three services. In Production, you would most likely have individual users for each service.
Again, for security purposes, Atlas requires you to whitelist the IP address or CIDR block from which the storefront services will connect to the cluster. For now, open the access to your specific IP address using whatsmyip.com, or much less-securely, to all IP addresses (0.0.0.0/0). Once the GKE cluster and external static IP addresses are created, make sure to come back and update this value; do not leave this wide open to the Internet.
The Java Spring Boot storefront services use a Spring Profile, gke. According to Spring, Spring Profiles provide a way to segregate parts of your application configuration and make it available only in certain environments. The gke Spring Profile’s configuration values may be set in a number of ways. For this demo, the majority of the values will be set using Kubernetes Deployment, ConfigMap and Secret resources, shown later.
The first two Spring configuration values will need are the MongoDB Atlas cluster’s connection string and the demo_user account password. Note these both for later use.
Confluent Cloud Kafka Cluster
Similar to MongoDB Atlas, this post assumes you already have a Confluent Cloud account and an existing project. It is free to set up a Professional account and a new project if you do not already have one. Atlas account creation does require the use of a Credit Card.
The Confluent Cloud web-based management console is shown below. Experienced users of other SaaS platforms may find the Confluent Cloud web-based console a bit sparse on features. In my opinion, the console lacks some necessary features, like cluster observability, individual Kafka topic management, detailed billing history (always says $0?), and persistent history of cluster activities, which survives cluster deletion. It seems like Confluent prefers users to download and configure their Confluent Control Center to get the functionality you might normally expect from a web-based Saas management tool.
As explained earlier, for minimal latency, I suggest creating the MongoDB Atlas cluster, Confluent Cloud Kafka cluster, and the GKE cluster, all on the Google Cloud Platform’s us-central1 Region. For this demo, choose the smallest cluster size available on GCP, in the us-central1 Region, with 1 MB/s R/W throughput and 500 MB of storage. As shown below, the cost will be approximately $0.55/hour. Don’t forget to delete this cluster when you are done with the demonstration, or you will continue to be charged.
Cluster creation of the minimally-sized Confluent Cloud cluster is pretty quick.
Once the cluster is ready, Confluent provides instructions on how to interact with the cluster via the Confluent Cloud CLI. Install the Confluent Cloud CLI, locally, for use later.
As explained earlier, the Java Spring Boot storefront services use a Spring Profile, gke. Like MongoDB Atlas, the Confluent Cloud Kafka cluster configuration values will be set using Kubernetes ConfigMap and Secret resources, shown later. There are several Confluent Cloud Java configuration values shown in the Client Config Java tab; we will need these for later use.
SASL and JAAS
Some users may not be familiar with the terms, SASL and JAAS. According to Wikipedia, Simple Authentication and Security Layer (SASL) is a framework for authentication and data security in Internet protocols. According to Confluent, Kafka brokers support client authentication via SASL. SASL authentication can be enabled concurrently with SSL encryption (SSL client authentication will be disabled).
There are numerous SASL mechanisms. The PLAIN SASL mechanism (SASL/PLAIN), used by Confluent, is a simple username/password authentication mechanism that is typically used with TLS for encryption to implement secure authentication. Kafka supports a default implementation for SASL/PLAIN which can be extended for production use. The SASL/PLAIN mechanism should only be used with SSL as a transport layer to ensure that clear passwords are not transmitted on the wire without encryption.
According to Wikipedia, Java Authentication and Authorization Service (JAAS) is the Java implementation of the standard Pluggable Authentication Module (PAM) information security framework. According to Confluent, Kafka uses the JAAS for SASL configuration. You must provide JAAS configurations for all SASL authentication mechanisms.
Cluster Authentication
Similar to MongoDB Atlas, we need to authenticate with the Confluent Cloud cluster from the storefront services. The authentication to Confluent Cloud is done with an API Key. Create a new API Key, and note the Key and Secret; these two additional pieces of configuration will be needed later.
Confluent Cloud API Keys can be created and deleted as necessary. For security in Production, API Keys should be created for each service and regularly rotated.
Kafka Topics
With the cluster created, create the storefront service’s three Kafka topics manually, using the Confluent Cloud’s ccloud CLI tool. First, configure the Confluent Cloud CLI using the ccloud init command, using your new cluster’s Bootstrap Servers address, API Key, and API Secret. The instructions are shown above Clusters Client Config tab of the Confluent Cloud web-based management interface.
Create the storefront service’s three Kafka topics using the ccloud topic create command. Use the list command to confirm they are created.
# manually create kafka topics ccloud topic create accounts.customer.change ccloud topic create fulfillment.order.change ccloud topic create orders.order.fulfill # list kafka topics ccloud topic list accounts.customer.change fulfillment.order.change orders.order.fulfill
Another useful ccloud command, topic describe, displays topic replication details. The new topics will have a replication factor of 3 and a partition count of 12.
Adding the --verbose flag to the command, ccloud --verbose topic describe, displays low-level topic and cluster configuration details, as well as a log of all topic-related activities.
Kubernetes Resources
The deployment of the three storefront microservices to the dev Namespace will minimally require the following Kubernetes configuration resources.
- (1) Kubernetes Namespace;
- (3) Kubernetes Deployments;
- (3) Kubernetes Services;
- (1) Kubernetes ConfigMap;
- (2) Kubernetes Secrets;
- (1) Istio 1.0 Gateway;
- (1) Istio 1.0 VirtualService;
- (2) Istio 1.0 ServiceEntry;
The Istio networking.istio.io v1alpha3 API introduced the last three configuration resources in the list, to control traffic routing into, within, and out of the mesh. There are a total of four new io networking.istio.io v1alpha3 API routing resources: Gateway, VirtualService, DestinationRule, and ServiceEntry.
Creating and managing such a large number of resources is a common complaint regarding the complexity of Kubernetes. Imagine the resource sprawl when you have dozens of microservices replicated across several namespaces. Fortunately, all resource files for this post are included in the storefront-kafka-docker project’s gke directory.
To follow along with the demo, you will need to make minor modifications to a few of these resources, including the Istio Gateway, Istio VirtualService, two Istio ServiceEntry resources, and two Kubernetes Secret resources.
Istio Gateway & VirtualService
Both the Istio Gateway and VirtualService configuration resources are contained in a single file, istio-gateway.yaml. For the demo, I am using a personal domain, storefront-demo.com, along with the sub-domain, api.dev, to host the Storefront API. The domain’s primary A record (‘@’) and sub-domain A record are both associated with the external IP address on the frontend of the load balancer. In the file, this host is configured for the Gateway and VirtualService resources. You can choose to replace the host with your own domain, or simply remove the host block altogether on lines 13–14 and 21–22. Removing the host blocks, you would then use the external IP address on the frontend of the load balancer (explained later in the post) to access the Storefront API (gist).
Istio ServiceEntry
There are two Istio ServiceEntry configuration resources. Both ServiceEntry resources control egress traffic from the Storefront API services, both of their ServiceEntry Location items are set to MESH_INTERNAL. The first ServiceEntry, mongodb-atlas-external-mesh.yaml, defines MongoDB Atlas cluster egress traffic from the Storefront API (gist).
The other ServiceEntry, confluent-cloud-external-mesh.yaml, defines Confluent Cloud Kafka cluster egress traffic from the Storefront API (gist).
Both need to have their host items replaced with the appropriate Atlas and Confluent URLs.
Inspecting Istio Resources
The easiest way to view Istio resources is from the command line using the istioctl and kubectl CLI tools.
istioctl get gateway istioctl get virtualservices istioctl get serviceentry kubectl describe gateway kubectl describe virtualservices kubectl describe serviceentry
Multiple Namespaces
In this demo, we are only deploying to a single Kubernetes Namespace, dev. However, Istio will also support routing traffic to multiple namespaces. For example, a typical non-prod Kubernetes cluster might support dev, test, and uat, each associated with a different sub-domain. One way to support multiple Namespaces with Istio 1.0 is to add each host to the Istio Gateway (lines 14–16, below), then create a separate Istio VirtualService for each Namespace. All the VirtualServices are associated with the single Gateway. In the VirtualService, each service’s host address is the fully qualified domain name (FQDN) of the service. Part of the FQDN is the Namespace, which we change for each for each VirtualService (gist).
MongoDB Atlas Secret
There is one Kubernetes Secret for the sensitive MongoDB configuration and one Secret for the sensitive Confluent Cloud configuration. The Kubernetes Secret object type is intended to hold sensitive information, such as passwords, OAuth tokens, and SSH keys.
The mongodb-atlas-secret.yaml file contains the MongoDB Atlas cluster connection string, with the demo_user username and password, one for each of the storefront service’s databases (gist).
Kubernetes Secrets are Base64 encoded. The easiest way to encode the secret values is using the Linux base64 program. The base64 program encodes and decodes Base64 data, as specified in RFC 4648. Pass each MongoDB URI string to the base64 program using echo -n.
MONGODB_URI=mongodb+srv://demo_user:your_password@your_cluster_address/accounts?retryWrites=true echo -n $MONGODB_URI | base64 bW9uZ29kYitzcnY6Ly9kZW1vX3VzZXI6eW91cl9wYXNzd29yZEB5b3VyX2NsdXN0ZXJfYWRkcmVzcy9hY2NvdW50cz9yZXRyeVdyaXRlcz10cnVl
Repeat this process for the three MongoDB connection strings.
Confluent Cloud Secret
The confluent-cloud-kafka-secret.yaml file contains two data fields in the Secret’s data map, bootstrap.servers and sasl.jaas.config. These configuration items were both listed in the Client Config Java tab of the Confluent Cloud web-based management console, as shown previously. The sasl.jaas.config data field requires the Confluent Cloud cluster API Key and Secret you created earlier. Again, use the base64 encoding process for these two data fields (gist).
Confluent Cloud ConfigMap
The remaining five Confluent Cloud Kafka cluster configuration values are not sensitive, and therefore, may be placed in a Kubernetes ConfigMap, confluent-cloud-kafka-configmap.yaml (gist).
Accounts Deployment Resource
To see how the services consume the ConfigMap and Secret values, review the Accounts Deployment resource, shown below. Note the environment variables section, on lines 44–90, are a mix of hard-coded values and values referenced from the ConfigMap and two Secrets, shown above (gist).
Modify Microservices for Confluent Cloud
As explained earlier, Confluent Cloud’s Kafka cluster requires some very specific configuration, based largely on the security features of Confluent Cloud. Connecting to Confluent Cloud requires some minor modifications to the existing storefront service source code. The changes are identical for all three services. To understand the service’s code, I suggest reviewing the previous post, Using Eventual Consistency and Spring for Kafka to Manage a Distributed Data Model: Part 1. Note the following changes are already made to the source code in the gke git branch, and not necessary for this demo.
The previous Kafka SenderConfig and ReceiverConfig Java classes have been converted to Java interfaces. There are four new SenderConfigConfluent, SenderConfigNonConfluent, ReceiverConfigConfluent, and ReceiverConfigNonConfluent classes, which implement one of the new interfaces. The new classes contain the Spring Boot Profile class-level annotation. One set of Sender and Receiver classes are assigned the @Profile("gke") annotation, and the others, the @Profile("!gke") annotation. When the services start, one of the two class implementations are is loaded, depending on the Active Spring Profile, gke or not gke. To understand the changes better, examine the Account service’s SenderConfigConfluent.java file (gist).
Line 20: Designates this class as belonging to the gke Spring Profile.
Line 23: The class now implements an interface.
Lines 25–44: Reference the Confluent Cloud Kafka cluster configuration. The values for these variables will come from the Kubernetes ConfigMap and Secret, described previously, when the services are deployed to GKE.
Lines 55–59: Additional properties that have been added to the Kafka Sender configuration properties, specifically for Confluent Cloud.
Once code changes were completed and tested, the Docker Image for each service was rebuilt and uploaded to Docker Hub for public access. When recreating the images, the version of the Java Docker base image was upgraded from the previous post to Alpine OpenJDK 12 (openjdk:12-jdk-alpine).
Google Kubernetes Engine (GKE) with Istio
Having created the MongoDB Atlas and Confluent Cloud clusters, built the Kubernetes and Istio resources, modified the service’s source code, and pushed the new Docker Images to Docker Hub, the GKE cluster may now be built.
For the sake of brevity, we will manually create the cluster and deploy the resources, using the Google Cloud SDK gcloud and Kubernetes kubectl CLI tools, as opposed to automating with CI/CD tools, like Jenkins or Spinnaker. For this demonstration, I suggest a minimally-sized two-node GKE cluster using n1-standard-2 machine-type instances. The latest available release of Kubernetes on GKE at the time of this post was 1.11.5-gke.5 and Istio 1.03 (Istio on GKE still considered beta). Note Kubernetes and Istio are evolving rapidly, thus the configuration flags often change with newer versions. Check the GKE Clusters tab for the latest clusters create command format (gist).
Executing these commands successfully will build the cluster and the dev Namespace, into which all the resources will be deployed. The two-node cluster creation process takes about three minutes on average.
We can also observe the new GKE cluster from the GKE Clusters Details tab.
Creating the GKE cluster also creates several other GCP resources, including a TCP load balancer and three external IP addresses. Shown below in the VPC network External IP addresses tab, there is one IP address associated with each of the two GKE cluster’s VM instances, and one IP address associated with the frontend of the load balancer.
While the TCP load balancer’s frontend is associated with the external IP address, the load balancer’s backend is a target pool, containing the two GKE cluster node machine instances.
A forwarding rule associates the load balancer’s frontend IP address with the backend target pool. External requests to the frontend IP address will be routed to the GKE cluster. From there, requests will be routed by Kubernetes and Istio to the individual storefront service Pods, and through the Istio sidecar (Envoy) proxies. There is an Istio sidecar proxy deployed to each Storefront service Pod.
Below, we see the details of the load balancer’s target pool, containing the two GKE cluster’s VMs.
As shown at the start of the post, a simplified view of the GCP/GKE network routing looks as follows. For brevity, firewall rules and routes are not illustrated in the diagram.
Apply Kubernetes Resources
Again, using kubectl, deploy the three services and associated Kubernetes and Istio resources. Note the Istio Gateway and VirtualService(s) are not deployed to the dev Namespace since their role is to control ingress and route traffic to the dev Namespace and the services within it (gist).
Once these commands complete successfully, on the Workloads tab, we should observe two Pods of each of the three storefront service Kubernetes Deployments deployed to the dev Namespace, all six Pods with a Status of ‘OK’. A Deployment controller provides declarative updates for Pods and ReplicaSets.
On the Services tab, we should observe the three storefront service’s Kubernetes Services. A Service in Kubernetes is a REST object.
On the Configuration Tab, we should observe the Kubernetes ConfigMap and two Secrets also deployed to the dev Environment.
Below, we see the confluent-cloud-kafka ConfigMap resource with its data map of Confluent Cloud configuration.
Below, we see the confluent-cloud-kafka Secret with its data map of sensitive Confluent Cloud configuration.
Test the Storefront API
If you recall from part two of the previous post, there are a set of seven Storefront API endpoints that can be called to create sample data and test the API. The HTTP GET Requests hit each service, generate test data, populate the three MongoDB databases, and produce and consume Kafka messages across all three topics. Making these requests is the easiest way to confirm the Storefront API is working properly.
- Sample Customer: accounts/customers/sample
- Sample Orders: orders/customers/sample/orders
- Sample Fulfillment Requests: orders/customers/sample/fulfill
- Sample Processed Order Event: fulfillment/fulfillment/sample/process
- Sample Shipped Order Event: fulfillment/fulfillment/sample/ship
- Sample In-Transit Order Event: fulfillment/fulfillment/sample/in-transit
- Sample Received Order Event: fulfillment/fulfillment/sample/receive
Thee are a wide variety of tools to interact with the Storefront API. The project includes a simple Python script, sample_data.py, which will make HTTP GET requests to each of the above endpoints, after confirming their health, and return a success message.
Postman
Postman, my personal favorite, is also an excellent tool to explore the Storefront API resources. I have the above set of the HTTP GET requests saved in a Postman Collection. Using Postman, below, we see the response from an HTTP GET request to the /accounts/customers endpoint.
Postman also allows us to create integration tests and run Collections of Requests in batches using Postman’s Collection Runner. To test the Storefront API, below, I used Collection Runner to run a single series of integration tests, intended to confirm the API’s functionality, by checking for expected HTTP response codes and expected values in the response payloads. Postman also shows the response times from the Storefront API. Since this platform was not built to meet Production SLAs, measuring response times is less critical in the Development environment.
Google Stackdriver
If you recall, the GKE cluster had the Stackdriver Kubernetes option enabled, which gives us, amongst other observability features, access to all cluster, node, pod, and container logs. To confirm data is flowing to the MongoDB databases and Kafka topics, we can check the logs from any of the containers. Below we see the logs from the two Accounts Pod containers. Observe the AfterSaveListener handler firing on an onAfterSave event, which sends a CustomerChangeEvent payload to the accounts.customer.change Kafka topic, without error. These entries confirm that both Atlas and Confluent Cloud are reachable by the GKE-based workloads, and appear to be functioning properly.
MongoDB Atlas Collection View
Review the MongoDB Atlas Clusters Collections tab. In this Development environment, the MongoDB databases and collections are created the first time a service tries to connects to them. In Production, the databases would be created and secured in advance of deploying resources. Once the sample data requests are completed successfully, you should now observe the three Storefront API databases, each with collections of documents.
MongoDB Compass
In addition to the Atlas web-based management console, MongoDB Compass is an excellent desktop tool to explore and manage MongoDB databases. Compass is available for Mac, Linux, and Windows. One of the many great features of Compass is the ability to visualize collection schemas and interactively filter documents. Below we see the fulfillment.requests collection schema.
Confluent Control Center
Confluent Control Center is a downloadable, web browser-based tool for managing and monitoring Apache Kafka, including your Confluent Cloud clusters. Confluent Control Center provides rich functionality for building and monitoring production data pipelines and streaming applications. Confluent offers a free 30-day trial of Confluent Control Center. Since the Control Center is provided at an additional fee, and I found difficult to configure for Confluent Cloud clusters based on Confluent’s documentation, I chose not to cover it in detail, for this post.
Tear Down Cluster
Delete your Confluent Cloud and MongoDB clusters using their web-based management consoles. To delete the GKE cluster and all deployed Kubernetes resources, use the cluster delete command. Also, double-check that the external IP addresses and load balancer, associated with the cluster, were also deleted as part of the cluster deletion (gist).
Conclusion
In this post, we have seen how easy it is to integrate Cloud-based DBaaS and MaaS products with the managed Kubernetes services from GCP, AWS, and Azure. As this post demonstrated, leading SaaS providers have sufficiently matured the integration capabilities of their product offerings to a point where it is now reasonable for enterprises to architect multi-vendor, single- and multi-cloud Production platforms, without re-engineering existing cloud-native applications.
In future posts, we will revisit this Storefront API example, further demonstrating how to enable HTTPS (Securing Your Istio Ingress Gateway with HTTPS) and end-user authentication (Istio End-User Authentication for Kubernetes using JSON Web Tokens (JWT) and Auth0)
All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.
Using the Google Cloud Dataproc WorkflowTemplates API to Automate Spark and Hadoop Workloads on GCP
Posted by Gary A. Stafford in Bash Scripting, Big Data, Cloud, Continuous Delivery, DevOps, GCP, Java Development, Python, Software Development on December 16, 2018
In the previous post, Big Data Analytics with Java and Python, using Cloud Dataproc, Google’s Fully-Managed Spark and Hadoop Service, we explored Google Cloud Dataproc using the Google Cloud Console as well as the Google Cloud SDK and Cloud Dataproc API. We created clusters, then uploaded and ran Spark and PySpark jobs, then deleted clusters, each as discrete tasks. Although each task could be done via the Dataproc API and therefore automatable, they were independent tasks, without awareness of the previous task’s state.
In this brief follow-up post, we will examine the Cloud Dataproc WorkflowTemplates API to more efficiently and effectively automate Spark and Hadoop workloads. According to Google, the Cloud Dataproc WorkflowTemplates API provides a flexible and easy-to-use mechanism for managing and executing Dataproc workflows. A Workflow Template is a reusable workflow configuration. It defines a graph of jobs with information on where to run those jobs. A Workflow is an operation that runs a Directed Acyclic Graph (DAG) of jobs on a cluster. Shown below, we see one of the Workflows that will be demonstrated in this post, displayed in Spark History Server Web UI.
Here we see a four-stage DAG of one of the three jobs in the workflow, displayed in Spark History Server Web UI.
Workflows are ideal for automating large batches of dynamic Spark and Hadoop jobs, and for long-running and unattended job execution, such as overnight.
Demonstration
Using the Python and Java projects from the previous post, we will first create workflow templates using the just the WorkflowTemplates API. We will create the template, set a managed cluster, add jobs to the template, and instantiate the workflow. Next, we will further optimize and simplify our workflow by using a YAML-based workflow template file. The YAML-based template file eliminates the need to make API calls to set the template’s cluster and add the jobs to the template. Finally, to further enhance the workflow and promote re-use of the template, we will incorporate parameterization. Parameters will allow us to pass parameters (key/value) pairs from the command line to workflow template, and on to the Python script as input arguments.
It is not necessary to use the Google Cloud Console for this post. All steps will be done using Google Cloud SDK shell commands. This means all steps may be automated using CI/CD DevOps tools, like Jenkins and Spinnaker on GKE.
Source Code
All open-sourced code for this post can be found on GitHub within three repositories: dataproc-java-demo, dataproc-python-demo, and dataproc-workflow-templates. Source code samples are displayed as GitHub Gists, which may not display correctly on all mobile and social media browsers.
WorkflowTemplates API
Always start by ensuring you have the latest Google Cloud SDK updates and are working within the correct Google Cloud project.
gcloud components update export PROJECT_ID=your-project-id gcloud config set project $PROJECT
Set the following variables based on your Google environment. The variables will be reused throughout the post for multiple commands.
export REGION=your-region export ZONE=your-zone export BUCKET_NAME=your-bucket
The post assumes you still have the Cloud Storage bucket we created in the previous post. In the bucket, you will need the two Kaggle IBRD CSV files, available on Kaggle, the compiled Java JAR file from the dataproc-java-demo project, and a new Python script, international_loans_dataproc.py, from the dataproc-python-demo project.
Use gsutil with the copy (cp) command to upload the four files to your Storage bucket.
gsutil cp data/ibrd-statement-of-loans-*.csv $BUCKET_NAME gsutil cp build/libs/dataprocJavaDemo-1.0-SNAPSHOT.jar $BUCKET_NAME gsutil cp international_loans_dataproc.py $BUCKET_NAME
Following Google’s suggested process, we create a workflow template using the workflow-templates create command.
export TEMPLATE_ID=template-demo-1 gcloud dataproc workflow-templates create \ $TEMPLATE_ID --region $REGION
Adding a Cluster
Next, we need to set a cluster for the workflow to use, in order to run the jobs. Cloud Dataproc will create and use a Managed Cluster for your workflow or use an existing cluster. If the workflow uses a managed cluster, it creates the cluster, runs the jobs, and then deletes the cluster when the jobs are finished. This means, for many use cases, there is no need to maintain long-lived clusters, they become just an ephemeral part of the workflow.
We set a managed cluster for our Workflow using the workflow-templates set-managed-cluster command. We will re-use the same cluster specifications we used in the previous post, the Standard, 1 master node and 2 worker nodes, cluster type.
gcloud dataproc workflow-templates set-managed-cluster \ $TEMPLATE_ID \ --region $REGION \ --zone $ZONE \ --cluster-name three-node-cluster \ --master-machine-type n1-standard-4 \ --master-boot-disk-size 500 \ --worker-machine-type n1-standard-4 \ --worker-boot-disk-size 500 \ --num-workers 2 \ --image-version 1.3-deb9
Alternatively, if we already had an existing cluster, we would use the workflow-templates set-cluster-selector command, to associate that cluster with the workflow template.
gcloud dataproc workflow-templates set-cluster-selector \ $TEMPLATE_ID \ --region $REGION \ --cluster-labels goog-dataproc-cluster-uuid=$CLUSTER_UUID
To get the existing cluster’s UUID label value, you could use a command similar to the following.
CLUSTER_UUID=$(gcloud dataproc clusters describe $CLUSTER_2 \ --region $REGION \ | grep 'goog-dataproc-cluster-uuid:' \ | sed 's/.* //') echo $CLUSTER_UUID 1c27efd2-f296-466e-b14e-c4263d0d7e19
Adding Jobs
Next, we add the jobs we want to run to the template. Each job is considered a step in the template, each step requires a unique step id. We will add three jobs to the template, two Java-based Spark jobs from the previous post, and a new Python-based PySpark job.
First, we add the two Java-based Spark jobs, using the workflow-templates add-job spark command. This command’s flags are nearly identical to the dataproc jobs submit spark command, used in the previous post.
export STEP_ID=ibrd-small-spark gcloud dataproc workflow-templates add-job spark \ --region $REGION \ --step-id $STEP_ID \ --workflow-template $TEMPLATE_ID \ --class org.example.dataproc.InternationalLoansAppDataprocSmall \ --jars $BUCKET_NAME/dataprocJavaDemo-1.0-SNAPSHOT.jar export STEP_ID=ibrd-large-spark gcloud dataproc workflow-templates add-job spark \ --region $REGION \ --step-id $STEP_ID \ --workflow-template $TEMPLATE_ID \ --class org.example.dataproc.InternationalLoansAppDataprocLarge \ --jars $BUCKET_NAME/dataprocJavaDemo-1.0-SNAPSHOT.jar
Next, we add the Python-based PySpark job, international_loans_dataproc.py, as the second job in the template. This Python script requires three input arguments, on lines 15–17, which are the bucket where the data is located and the and results are placed, the name of the data file, and the directory in the bucket where the results will be placed (gist).
We pass the arguments to the Python script as part of the PySpark job, using the workflow-templates add-job pyspark command.
export STEP_ID=ibrd-large-pyspark
gcloud dataproc workflow-templates add-job pyspark \
$BUCKET_NAME/international_loans_dataproc.py \
--step-id $STEP_ID \
--workflow-template $TEMPLATE_ID \
--region $REGION \
-- $BUCKET_NAME \
ibrd-statement-of-loans-historical-data.csv \
ibrd-summary-large-python
That’s it, we have created our first Cloud Dataproc Workflow Template using the Dataproc WorkflowTemplate API. To view our template we can use the following two commands. First, use the workflow-templates list command to display a list of available templates. The list command output displays the version of the workflow template and how many jobs are in the template.
gcloud dataproc workflow-templates list --region $REGION ID JOBS UPDATE_TIME VERSION template-demo-1 3 2018-12-15T16:32:06.508Z 5
Then, we use the workflow-templates describe command to show the details of a specific template.
gcloud dataproc workflow-templates describe \ $TEMPLATE_ID --region $REGION
Using the workflow-templates describe command, we should see output similar to the following (gist).
In the template description, notice the template’s id, the managed cluster in the placement section, and the three jobs, all which we added using the above series of workflow-templates commands. Also, notice the creation and update timestamps and version number, which were automatically generated by Dataproc. Lastly, notice the name, which refers to the GCP project and region where this copy of the template is located. Had we used an existing cluster with our workflow, as opposed to a managed cluster, the placement section would have looked as follows.
placement:
clusterSelector:
clusterLabels:
goog-dataproc-cluster-uuid: your_clusters_uuid_label_value
To instantiate the workflow, we use the workflow-templates instantiate command. This command will create the managed cluster, run all the steps (jobs), then delete the cluster. I have added the time command to see how fast the workflow will take to complete.
time gcloud dataproc workflow-templates instantiate \ $TEMPLATE_ID --region $REGION #--async
We can observe the progress from the Google Cloud Dataproc Console, or from the command line by omitting the --async flag. Below we see the three jobs completed successfully on the managed cluster.
Waiting on operation [projects/dataproc-demo-224523/regions/us-east1/operations/e720bb96-9c87-330e-b1cd-efa4612b3c57]. WorkflowTemplate [template-demo-1] RUNNING Creating cluster: Operation ID [projects/dataproc-demo-224523/regions/us-east1/operations/e1fe53de-92f2-4f8c-8b3a-fda5e13829b6]. Created cluster: three-node-cluster-ugdo4ygpl52bo. Job ID ibrd-small-spark-ugdo4ygpl52bo RUNNING Job ID ibrd-large-spark-ugdo4ygpl52bo RUNNING Job ID ibrd-large-pyspark-ugdo4ygpl52bo RUNNING Job ID ibrd-small-spark-ugdo4ygpl52bo COMPLETED Job ID ibrd-large-spark-ugdo4ygpl52bo COMPLETED Job ID ibrd-large-pyspark-ugdo4ygpl52bo COMPLETED Deleting cluster: Operation ID [projects/dataproc-demo-224523/regions/us-east1/operations/f2a40c33-3cdf-47f5-92d6-345463fbd404]. WorkflowTemplate [template-demo-1] DONE Deleted cluster: three-node-cluster-ugdo4ygpl52bo. 1.02s user 0.35s system 0% cpu 5:03.55 total
In the output, you see the creation of the cluster, the three jobs running and completing successfully, and finally the cluster deletion. The entire workflow took approximately 5 minutes to complete. Below is the view of the workflow’s results from the Dataproc Clusters Console Jobs tab.
Below we see the output from the PySpark job, run as part of the workflow template, shown in the Dataproc Clusters Console Output tab. Notice the three input arguments we passed to the Python script from the workflow template, listed in the output.
We see the arguments passed to the job, from the Jobs Configuration tab.
Examining the Google Cloud Dataproc Jobs Console, we will observe that the WorkflowTemplate API automatically adds a unique alphanumeric extension to both the name of the managed clusters we create, as well as to the name of each job that is run. The extension on the cluster name matches the extension on the jobs ran on that cluster.
YAML-based Workflow Template
Although, the above WorkflowTemplates API-based workflow was certainly more convenient than using the individual Cloud Dataproc API commands. At a minimum, we don’t have to remember to delete our cluster when the jobs are complete, as I often do. To further optimize the workflow, we will introduce YAML-based Workflow Template. According to Google, you can define a workflow template in a YAML file, then instantiate the template to run the workflow. You can also import and export a workflow template YAML file to create and update a Cloud Dataproc workflow template resource.
We can export our first workflow template to create our YAML-based template file.
gcloud dataproc workflow-templates export template-demo-1 \ --destination template-demo-2.yaml \ --region $REGION
Below is our first YAML-based template, template-demo-2.yaml. You will need to replace the values in the template with your own values, based on your environment (gist).
Note the template looks almost similar to the template we just created previously using the WorkflowTemplates API. The YAML-based template requires the placement and jobs fields. All the available fields are detailed, here.
To run the template we use the workflow-templates instantiate-from-file command. Again, I will use the time command to measure performance.
time gcloud dataproc workflow-templates instantiate-from-file \ --file template-demo-2.yaml \ --region $REGION
Running the workflow-templates instantiate-from-file command will run a workflow, nearly identical to the workflow we ran in the previous example, with a similar timing. Below we see the three jobs completed successfully on the managed cluster, in approximately the same time as the previous workflow.
Waiting on operation [projects/dataproc-demo-224523/regions/us-east1/operations/7ba3c28e-ebfa-32e7-9dd6-d938a1cfe23b]. WorkflowTemplate RUNNING Creating cluster: Operation ID [projects/dataproc-demo-224523/regions/us-east1/operations/8d05199f-ed36-4787-8a28-ae784c5bc8ae]. Created cluster: three-node-cluster-5k3bdmmvnna2y. Job ID ibrd-small-spark-5k3bdmmvnna2y RUNNING Job ID ibrd-large-spark-5k3bdmmvnna2y RUNNING Job ID ibrd-large-pyspark-5k3bdmmvnna2y RUNNING Job ID ibrd-small-spark-5k3bdmmvnna2y COMPLETED Job ID ibrd-large-spark-5k3bdmmvnna2y COMPLETED Job ID ibrd-large-pyspark-5k3bdmmvnna2y COMPLETED Deleting cluster: Operation ID [projects/dataproc-demo-224523/regions/us-east1/operations/a436ae82-f171-4b0a-9b36-5e16406c75d5]. WorkflowTemplate DONE Deleted cluster: three-node-cluster-5k3bdmmvnna2y. 1.16s user 0.44s system 0% cpu 4:48.84 total
Parameterization of Templates
To further optimize the workflow template process for re-use, we have the option of passing parameters to our template. Imagine you now receive new loan snapshot data files every night. Imagine you need to run the same data analysis on the financial transactions of thousands of your customers, nightly. Parameterizing templates makes it more flexible and reusable. By removing hard-codes values, such as Storage bucket paths and data file names, a single template may be re-used for multiple variations of the same job. Parameterization allows you to automate hundreds or thousands of Spark and Hadoop jobs in a workflow or workflows, each with different parameters, programmatically.
To demonstrate the parameterization of a workflow template, we create another YAML-based template with just the Python/PySpark job, template-demo-3.yaml. If you recall from our first example, the Python script, international_loans_dataproc.py, requires three input arguments: the bucket where the data is located and the and results are placed, the name of the data file, and the directory in the bucket, where the results will be placed.
We will replace four of the values in the template with parameters. We will inject those parameter’s values when we instantiate the workflow. Below is the new parameterized template. The template now has a parameters section from lines 26–46. They define parameters that will be used to replace the four values on lines 3–7 (gist).
Note the PySpark job’s three arguments and the location of the Python script have been parameterized. Parameters may include validation. As an example of validation, the template uses regex to validate the format of the Storage bucket path. The regex follows Google’s RE2 regular expression library syntax. If you need help with regex, the Regex Tester – Golang website is a convenient way to test your parameter’s regex validations.
First, we import the new parameterized YAML-based workflow template, using the workflow-templates import command. Then, we instantiate the template using the workflow-templates instantiate command. The workflow-templates instantiate command will run the single PySpark job, analyzing the smaller IBRD data file, and placing the resulting Parquet-format file in a directory within the Storage bucket. We pass the Python script location, bucket link, smaller IBRD data file name, and output directory, as parameters to the template, and therefore indirectly, three of these, as input arguments to the Python script.
export TEMPLATE_ID=template-demo-3 gcloud dataproc workflow-templates import $TEMPLATE_ID \ --region $REGION --source template-demo-3.yaml gcloud dataproc workflow-templates instantiate \ $TEMPLATE_ID --region $REGION --async \ --parameters MAIN_PYTHON_FILE="$BUCKET_NAME/international_loans_dataproc.py",STORAGE_BUCKET=$BUCKET_NAME,IBRD_DATA_FILE="ibrd-statement-of-loans-latest-available-snapshot.csv",RESULTS_DIRECTORY="ibrd-summary-small-python"
Next, we will analyze the larger historic data file, using the same parameterized YAML-based workflow template, but changing two of the four parameters we are passing to the template with the workflow-templates instantiate command. This will run a single PySpark job on the larger IBRD data file and place the resulting Parquet-format file in a different directory within the Storage bucket.
time gcloud dataproc workflow-templates instantiate \ $TEMPLATE_ID --region $REGION \ --parameters MAIN_PYTHON_FILE="$BUCKET_NAME/international_loans_dataproc.py",STORAGE_BUCKET=$BUCKET_NAME,IBRD_DATA_FILE="ibrd-statement-of-loans-historical-data.csv",RESULTS_DIRECTORY="ibrd-summary-large-python"
This is the power of parameterization—one workflow template and one job script, but two different datasets and two different results.
Below we see the single PySpark job ran on the managed cluster.
Waiting on operation [projects/dataproc-demo-224523/regions/us-east1/operations/b3c5063f-e3cf-3833-b613-83db12b82f32]. WorkflowTemplate [template-demo-3] RUNNING Creating cluster: Operation ID [projects/dataproc-demo-224523/regions/us-east1/operations/896b7922-da8e-49a9-bd80-b1ac3fda5105]. Created cluster: three-node-cluster-j6q2al2mkkqck. Job ID ibrd-pyspark-j6q2al2mkkqck RUNNING Job ID ibrd-pyspark-j6q2al2mkkqck COMPLETED Deleting cluster: Operation ID [projects/dataproc-demo-224523/regions/us-east1/operations/fe4a263e-7c6d-466e-a6e2-52292cbbdc9b]. WorkflowTemplate [template-demo-3] DONE Deleted cluster: three-node-cluster-j6q2al2mkkqck. 0.98s user 0.40s system 0% cpu 4:19.42 total
Using the workflow-templates list command again, should display a list of two workflow templates.
gcloud dataproc workflow-templates list --region $REGION ID JOBS UPDATE_TIME VERSION template-demo-3 1 2018-12-15T17:04:39.064Z 2 template-demo-1 3 2018-12-15T16:32:06.508Z 5
Looking within the Google Cloud Storage bucket, we should now see four different folders, the results of the workflows.
Job Results and Testing
To check on the status of a job, we use the dataproc jobs wait command. This returns the standard output (stdout) and standard error (stderr) for that specific job.
export SET_ID=ibrd-large-dataset-pyspark-cxzzhr2ro3i54 gcloud dataproc jobs wait $SET_ID \ --project $PROJECT_ID \ --region $REGION
The dataproc jobs wait command is frequently used for automated testing of jobs, often within a CI/CD pipeline. Assume we have expected part of the job output that indicates success, such as a string, boolean, or numeric value. We could any number of test frameworks or other methods to confirm the existence of that expected value or values. Below is a simple example of using the grep command to check for the existence of the expected line ‘ state: FINISHED’ in the standard output of the dataproc jobs wait command.
command=$(gcloud dataproc jobs wait $SET_ID \ --project $PROJECT_ID \ --region $REGION) &>/dev/null if grep -Fqx " state: FINISHED" <<< $command &>/dev/null; then echo "Job Success!" else echo "Job Failure?" fi # single line alternative if grep -Fqx " state: FINISHED" <<< $command &>/dev/null;then echo "Job Success!";else echo "Job Failure?";fi Job Success!
Individual Operations
To view individual workflow operations, use the operations list and operations describe commands. The operations list command will list all operations.
Notice the three distinct series of operations within each workflow, shown with the operations list command: WORKFLOW, CREATE, and DELETE. In the example below, I’ve separated the operations by workflow, for better clarity.
gcloud dataproc operations list --region $REGION NAME TIMESTAMP TYPE STATE ERROR WARNINGS fe4a263e-7c6d-466e-a6e2-52292cbbdc9b 2018-12-15T17:11:45.178Z DELETE DONE 896b7922-da8e-49a9-bd80-b1ac3fda5105 2018-12-15T17:08:38.322Z CREATE DONE b3c5063f-e3cf-3833-b613-83db12b82f32 2018-12-15T17:08:37.497Z WORKFLOW DONE --- be0e5293-275f-46ad-b1f4-696ba44c222e 2018-12-15T17:07:26.305Z DELETE DONE 6784078c-cbe3-4c1e-a56e-217149f555a4 2018-12-15T17:04:40.613Z CREATE DONE fcd8039e-a260-3ab3-ad31-01abc1a524b4 2018-12-15T17:04:40.007Z WORKFLOW DONE --- b4b23ca6-9442-4ffb-8aaf-460bac144dd8 2018-12-15T17:02:16.744Z DELETE DONE 89ef9c7c-f3c9-4d01-9091-61ed9e1f085d 2018-12-15T17:01:45.514Z CREATE DONE 243fa7c1-502d-3d7a-aaee-b372fe317570 2018-12-15T17:01:44.895Z WORKFLOW DONE
We use the results of the operations list command to execute the operations describe command to describe a specific operation.
gcloud dataproc operations describe \ projects/$PROJECT_ID/regions/$REGION/operations/896b7922-da8e-49a9-bd80-b1ac3fda5105
Each type of operation contains different details. Note the fine-grain of detail we get from Dataproc using the operations describe command for a CREATE operation (gist).
Conclusion
In this brief, follow-up post to the previous post, Big Data Analytics with Java and Python, using Cloud Dataproc, Google’s Fully-Managed Spark and Hadoop Service, we have seen how easy the WorkflowTemplates API and YAML-based workflow templates make automating our analytics jobs. This post only scraped the surface of the complete functionality of the WorkflowTemplates API and parameterization of templates.
In a future post, we leverage the automation capabilities of the Google Cloud Platform, the WorkflowTemplates API, YAML-based workflow templates, and parameterization, to develop a fully-automated DevOps for Big Data workflow, capable of running hundreds of Spark and Hadoop jobs.
All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.
Streaming Docker Logs to the Elastic Stack (ELK Stack) using Fluentd
Posted by Gary A. Stafford in Bash Scripting, DevOps, Enterprise Software Development, Software Development on April 10, 2017
Introduction
Fluentd and Docker’s native logging driver for Fluentd makes it easy to stream Docker logs from multiple running containers to the Elastic Stack. In this post, we will use Fluentd to stream Docker logs from multiple instances of a Dockerized Spring Boot RESTful service and MongoDB, to the Elastic Stack (ELK).
In a recent post, Distributed Service Configuration with Consul, Spring Cloud, and Docker, we built a Consul cluster using Docker swarm mode, to host distributed configurations for a Spring Boot service. We will use the resulting swarm cluster from the previous post as a foundation for this post.
Fluentd
According to the Fluentd website, Fluentd is described as an open source data collector, which unifies data collection and consumption for a better use and understanding of data. Fluentd combines all facets of processing log data: collecting, filtering, buffering, and outputting logs across multiple sources and destinations. Fluentd structures data as JSON as much as possible.
Logging Drivers
Docker includes multiple logging mechanisms to get logs from running containers and services. These mechanisms are called logging drivers. Fluentd is one of the ten current Docker logging drivers. According to Docker, The fluentd logging driver sends container logs to the Fluentd collector as structured log data. Then, users can utilize any of the various output plugins, from Fluentd, to write these logs to various destinations.
Elastic Stack
The ELK Stack, now known as the Elastic Stack, is the combination of Elastic’s very popular products: Elasticsearch, Logstash, and Kibana. According to Elastic, the Elastic Stack provides real-time insights from almost any type of structured and unstructured data source.
Setup
All code for this post has been tested on both MacOS and Linux. For this post, I am provisioning and deploying to a Linux workstation, running the most recent release of Fedora and Oracle VirtualBox. If you want to use AWS or another infrastructure provider instead of VirtualBox to build your swarm, it is fairly easy to switch the Docker Machine driver and change a few configuration items in the vms_create.sh script (see Provisioning, below).
Required Software
If you want to follow along with this post, you will need the latest versions of git, Docker, Docker Machine, Docker Compose, and VirtualBox installed.
Source Code
All source code for this post is located in two GitHub repositories. The first repository contains scripts to provision the VMs, create an overlay network and persistent host-mounted volumes, build the Docker swarm, and deploy Consul, Registrator, Swarm Visualizer, Fluentd, and the Elastic Stack. The second repository contains scripts to deploy two instances of the Widget Spring Boot RESTful service and a single instance of MongoDB. You can execute all scripts manually, from the command-line, or from a CI/CD pipeline, using tools such as Jenkins.
Provisioning the Swarm
To start, clone the first repository, and execute the single run_all.sh script, or execute the seven individual scripts necessary to provision the VMs, create the overlay network and host volumes, build the swarm, and deploy Consul, Registrator, Swarm Visualizer, Fluentd, and the Elastic Stack. Follow the steps below to complete this part.
When the scripts have completed, the resulting swarm should be configured similarly to the diagram below. Consul, Registrator, Swarm Visualizer, Fluentd, and the Elastic Stack containers should be distributed across the three swarm manager nodes and the three swarm worker nodes (VirtualBox VMs).
Deploying the Application
Next, clone the second repository, and execute the single run_all.sh script, or execute the four scripts necessary to deploy the Widget Spring Boot RESTful service and a single instance of MongoDB. Follow the steps below to complete this part.
When the scripts have completed, the Widget service and MongoDB containers should be distributed across two of the three swarm worker nodes (VirtualBox VMs).
To confirm the final state of the swarm and the running container stacks, use the following Docker commands.
Open the Swarm Visualizer web UI, using any of the swarm manager node IPs, on port 5001, to confirm the swarm health, as well as the running container’s locations.
Lastly, open the Consul Web UI, using any of the swarm manager node IPs, on port 5601, to confirm the running container’s health, as well as their placement on the swarm nodes.
Streaming Logs
Elastic Stack
If you read the previous post, Distributed Service Configuration with Consul, Spring Cloud, and Docker, you will notice we deployed a few additional components this time. First, the Elastic Stack (aka ELK), is deployed to the worker3 swarm worker node, within a single container. I have increased the CPU count and RAM assigned to this VM, to minimally run the Elastic Stack. If you review the docker-compose.yml file, you will note I am using Sébastien Pujadas’ sebp/elk:latest Docker base image from Docker Hub to provision the Elastic Stack. At the time of the post, this was based on the 5.3.0 version of ELK.
Docker Logging Driver
The Widget stack’s docker-compose.yml file has been modified since the last post. The compose file now incorporates a Fluentd logging configuration section for each service. The logging configuration includes the address of the Fluentd instance, on the same swarm worker node. The logging configuration also includes a tag for each log message.
Fluentd
In addition to the Elastic Stack, we have deployed Fluentd to the worker1 and worker2 swarm nodes. This is also where the Widget and MongoDB containers are deployed. Again, looking at the docker-compose.yml file, you will note we are using a custom Fluentd Docker image, garystafford/custom-fluentd:latest, which I created. The custom image is available on Docker Hub.
The custom Fluentd Docker image is based on Fluentd’s official onbuild Docker image, fluent/fluentd:onbuild. Fluentd provides instructions for building your own custom images, from their onbuild base images.
There were two reasons I chose to create a custom Fluentd Docker image. First, I added the Uken Games’ Fluentd Elasticsearch Plugin, to the Docker Image. This highly configurable Fluentd Output Plugin allows us to push Docker logs, processed by Fluentd to the Elasticsearch. Adding additional plugins is a common reason for creating a custom Fluentd Docker image.
The second reason to create a custom Fluentd Docker image was configuration. Instead of bind-mounting host directories or volumes to the multiple Fluentd containers, to provide Fluentd’s configuration, I baked the configuration file into the immutable Docker image. The bare-bones, basicFluentd configuration file defines three processes, which are Input, Filter, and Output. These processes are accomplished using Fluentd plugins. Fluentd has 6 types of plugins: Input, Parser, Filter, Output, Formatter and Buffer. Fluentd is primarily written in Ruby, and its plugins are Ruby gems.
Fluentd listens for input on tcp port 24224, using the forward Input Plugin. Docker logs are streamed locally on each swarm node, from the Widget and MongoDB containers to the local Fluentd container, over tcp port 24224, using Docker’s fluentd logging driver, introduced earlier. Fluentd
Fluentd then filters all input using the stdout Filter Plugin. This plugin prints events to stdout, or logs if launched with daemon mode. This is the most basic method of filtering.
Lastly, Fluentd outputs the filtered input to two destinations, a local log file and Elasticsearch. First, the Docker logs are sent to a local Fluentd log file. This is only for demonstration purposes and debugging. Outputting log files is not recommended for production, nor does it meet the 12-factor application recommendations for logging. Second, Fluentd outputs the Docker logs to Elasticsearch, over tcp port 9200, using the Fluentd Elasticsearch Plugin, introduced above.
Additional Metadata
In addition to the log message itself, in JSON format, the fluentd log driver sends the following metadata in the structured log message: container_id, container_name, and source. This is helpful in identifying and categorizing log messages from multiple sources. Below is a sample of log messages from the raw Fluentd log file, with the metadata tags highlighted in yellow. At the bottom of the output is a log message parsed with jq, for better readability.
Using Elastic Stack
Now that our two Docker stacks are up and running on our swarm, we should be streaming logs to Elasticsearch. To confirm this, open the Kibana web console, which should be available at the IP address of the worker3 swarm worker node, on port 5601.
For the sake of this demonstration, I increased the verbosity of the Spring Boot Widget service’s log level, from INFO to DEBUG, in Consul. At this level of logging, the two Widget services and the single MongoDB instance were generating an average of 250-400 log messages every 30 seconds, according to Kibana.
If that seems like a lot, keep in mind, these are Docker logs, which are single-line log entries. We have not aggregated multi-line messages, such as Java exceptions and stack traces messages, into single entries. That is for another post. Also, the volume of debug-level log messages generated by the communications between the individual services and Consul is fairly verbose.
Inspecting log entries in Kibana, we find the metadata tags contained in the raw Fluentd log output are now searchable fields: container_id, container_name, and source, as well as log. Also, note the _type field, with a value of ‘fluentd’. We injected this field in the output section of our Fluentd configuration, using the Fluentd Elasticsearch Plugin. The _type fiel allows us to differentiate these log entries from other potential data sources.
References
All opinions in this post are my own and not necessarily the views of my current employer or their clients.
Using Weave to Network a Docker Multi-Container Java Application
Posted by Gary A. Stafford in Bash Scripting, Build Automation, Continuous Delivery, DevOps, Enterprise Software Development, Java Development, Software Development on September 17, 2015
Use the latest version of Weaveworks’ Weave Net to network a multi-container, Dockerized Java Spring web application.
Introduction
The last post demonstrated how to build and deploy the Java Spring Music application to a VirtualBox, multi-container test environment. The environment contained (1) NGINX container, (2) load-balanced Tomcat containers, (1) MongoDB container, (1) ELK Stack container, and (1) Logspout container, all on one VM.
In that post, we used Docker’s links option. The links options, which modifies the container’s /etc/hosts file, allows two Docker containers to communicate with each other. For example, the NGINX container is linked to both Tomcat containers:
proxy: build: nginx/ ports: "80:80" links: - app01 - app02
Although container linking works, links are not very practical beyond a small number of static containers or a single container host. With linking, you must explicitly define each service-to-container relationship you want Docker to configure. Linking is not an option with Docker Swarm to link containers across multiple virtual machine container hosts. With Docker Networking in its early ‘experimental’ stages and the Swarm limitation, it’s hard to foresee the use of linking for any uses beyond limited development and test environments.
Weave Net
Weave Net, aka Weave, is one of a trio of products developed by Weaveworks. The other two members of the trio include Weave Run and Weave Scope. According to Weaveworks’ website, ‘Weave Net connects all your containers into a transparent, dynamic and resilient mesh. This is one of the easiest ways to set up clustered applications that run anywhere.‘ Weave allows us to eliminate the dependency on the links connect our containers. Weave does all the linking of containers for us automatically.
Weave v1.1.0
If you worked with previous editions of Weave, you will appreciate that Weave versions v1.0.x and v1.1.0 are significant steps forward in the evolution of Weave. Weaveworks’ GitHub Weave Release page details the many improvements. I also suggest reading Weave ‘Gossip’ DNS, on Weavework’s blog, before continuing. The post details the improvements of Weave v1.1.0. Some of those key new features include:
- Completely redesigned weaveDNS, dubbed ‘Gossip DNS’
- Registrations are broadcast to all weaveDNS instances
- Registered entries are stored in-memory and handle lookups locally
- Weave router’s gossip implementation periodically synchronizes DNS mappings between peers
- Ability to recover from network partitions and other transient failures
- Each peer is aware of the hostnames and IP address of all containers in the Weave network.
weave launchnow launches all weave components, including the router, weaveDNS and the proxy, greatly simplifying setup- weaveDNS is now embedded in the Weave router
Weave-based Network
In this post, we will reuse the Java Spring Music application from the last post. However, we will replace the project’s static dependencies on Docker links with Weave. This post will demonstrate the most basic features of Weave, using a single cluster. In a future post, we will demonstrate how easily Weave also integrates with multiple clusters.
All files for this post can be found in the swarm-weave branch of the GitHub Repository. Instructions to clone are below.
Configuration
If you recall from the previous post, the Docker Compose YAML file (docker-compose.yml) looked similar to this:
proxy: build: nginx/ ports: "80:80" links: - app01 - app02 hostname: "proxy" app01: build: tomcat/ expose: "8080" ports: "8180:8080" links: - nosqldb - elk hostname: "app01" app02: build: tomcat/ expose: "8080" ports: "8280:8080" links: - nosqldb - elk hostname: "app01" nosqldb: build: mongo/ hostname: "nosqldb" volumes: "/opt/mongodb:/data/db" elk: build: elk/ ports: - "8081:80" - "8082:9200" expose: "5000/upd" logspout: build: logspout/ volumes: "/var/run/docker.sock:/tmp/docker.sock" links: elk ports: "8083:80" environment: ROUTE_URIS=logstash://elk:5000
Implementing Weave simplifies the docker-compose.yml, considerably. Below is the new Weave version of the docker-compose.yml. The links option have been removed from all containers. Additionally, the hostnames have been removed, as they serve no real purpose moving forward. The logspout service’s environment option has been modified to use the elk container’s full name as opposed to the hostname.
The only addition is the volumes_from option to the proxy service. We must ensure that the two Tomcat containers start before the NGINX containers. The links option indirectly provided this functionality, previously.
proxy:
build: nginx/
ports:
- "80:80"
volumes_from:
- app01
- app02
app01:
build: tomcat/
expose:
- "8080"
ports:
- "8180:8080"
app02:
build: tomcat/
expose:
- "8080"
ports:
- "8280:8080"
nosqldb:
build: mongo/
volumes:
- "/opt/mongodb:/data/db"
elk:
build: elk/
ports:
- "8081:80"
- "8082:9200"
expose:
- "5000/upd"
logspout:
build: logspout/
volumes:
- "/var/run/docker.sock:/tmp/docker.sock"
ports:
- "8083:80"
environment:
- ROUTE_URIS=logstash://music_elk_1:5000
Next, we need to modify the NGINX configuration, slightly. In the previous post we referenced the Tomcat service names, as shown below.
upstream backend {
server app01:8080;
server app02:8080;
}
Weave will automatically add the two Tomcat container names to the NGINX container’s /etc/hosts file. We will add these Tomcat container names to NGINX’s configuration file.
upstream backend {
server music_app01_1:8080;
server music_app02_1:8080;
}
In an actual Production environment, we would use a template, along with a service discovery tool, such as Consul, to automatically populate the container names, as containers are dynamically created or destroyed.
Installing and Running Weave
After cloning this post’s GitHub repository, I recommend first installing and configuring Weave. Next, build the container host VM using Docker Machine. Lastly, build the containers using Docker Compose. The build_project.sh script below will take care of all the necessary steps.
#!/bin/sh
########################################################################
#
# title: Build Complete Project
# author: Gary A. Stafford (https://programmaticponderings.com)
# url: https://github.com/garystafford/sprint-music-docker
# description: Clone and build complete Spring Music Docker project
#
# to run: sh ./build_project.sh
#
########################################################################
# install latest weave
curl -L git.io/weave -o /usr/local/bin/weave &&
chmod a+x /usr/local/bin/weave &&
weave version
# clone project
git clone -b swarm-weave \
--single-branch --branch swarm-weave \
https://github.com/garystafford/spring-music-docker.git &&
cd spring-music-docker
# build VM
docker-machine create --driver virtualbox springmusic --debug
# create diectory to store mongo data on host
docker ssh springmusic mkdir /opt/mongodb
# set new environment
docker-machine env springmusic &&
eval "$(docker-machine env springmusic)"
# launch weave and weaveproxy/weaveDNS containers
weave launch &&
tlsargs=$(docker-machine ssh springmusic \
"cat /proc/\$(pgrep /usr/local/bin/docker)/cmdline | tr '\0' '\n' | grep ^--tls | tr '\n' ' '")
weave launch-proxy $tlsargs &&
eval "$(weave env)" &&
# test/confirm weave status
weave status &&
docker logs weaveproxy
# pull and build images and containers
# this step will take several minutes to pull images first time
docker-compose -f docker-compose.yml -p music up -d
# wait for container apps to fully start
sleep 15
# test weave (should list entries for all containers)
docker exec -it music_proxy_1 cat /etc/hosts
# run quick test of Spring Music application
for i in {1..10}
do
curl -I --url $(docker-machine ip springmusic)
done
One last test, to ensure that MongoDB is using the host’s volume, and not storing data in the MongoDB container’s /data/db directory, execute the following command: docker-machine ssh springmusic ls -Alh /opt/mongodb. You should see MongoDB-related content being stored here.
Testing Weave
Running the weave status command, we should observe that Weave returned a status similar to the example below:
gstafford@gstafford-X555LA:$ weave status
Version: v1.1.0
Service: router
Protocol: weave 1..2
Name: 6a:69:11:1b:b4:e3(springmusic)
Encryption: disabled
PeerDiscovery: enabled
Targets: 0
Connections: 0
Peers: 1
Service: ipam
Consensus: achieved
Range: [10.32.0.0-10.48.0.0)
DefaultSubnet: 10.32.0.0/12
Service: dns
Domain: weave.local.
TTL: 1
Entries: 2
Service: proxy
Address: tcp://192.168.99.100:12375
Running the docker exec -it music_proxy_1 cat /etc/hosts command, we should observe that WeaveDNS has automatically added entries for all containers to the music_proxy_1 container’s /etc/hosts file. WeaveDNS will also remove the addresses of any containers that die. This offers a simple way to implement redundancy.
gstafford@gstafford-X555LA:$ docker exec -it music_proxy_1 cat /etc/hosts # modified by weave 10.32.0.6 music_proxy_1 127.0.0.1 localhost 172.17.0.131 weave weave.bridge 172.17.0.133 music_elk_1 music_elk_1.bridge 172.17.0.134 music_nosqldb_1 music_nosqldb_1.bridge 172.17.0.138 music_app02_1 music_app02_1.bridge 172.17.0.139 music_logspout_1 music_logspout_1.bridge 172.17.0.140 music_app01_1 music_app01_1.bridge ::1 ip6-localhost ip6-loopback localhost fe00::0 ip6-localnet ff00::0 ip6-mcastprefix ff02::1 ip6-allnodes ff02::2 ip6-allrouters
Weave resolves the container’s name to eth0 IP address, created by Docker’s docker0 Ethernet bridge. Each container can now communicate with all other containers in the cluster.
Results
Resulting virtual machines, network, images, and containers:
gstafford@gstafford-X555LA:$ docker-machine ls NAME ACTIVE DRIVER STATE URL SWARM springmusic * virtualbox Running tcp://192.168.99.100:2376 gstafford@gstafford-X555LA:$ docker images REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE music_app02 latest 632c782010ac 3 days ago 370.4 MB music_app01 latest 632c782010ac 3 days ago 370.4 MB music_proxy latest 171624a31920 3 days ago 144.5 MB music_nosqldb latest 2b3b46af5ef3 3 days ago 260.8 MB music_elk latest 5c18dae84b26 3 days ago 1.05 GB weaveworks/weaveexec v1.1.0 69c6bfa7934f 5 days ago 58.18 MB weaveworks/weave v1.1.0 5dccf0533147 5 days ago 17.53 MB music_logspout latest fe64597ab0c4 8 days ago 24.36 MB gliderlabs/logspout master 40a52d6ca462 9 days ago 14.75 MB willdurand/elk latest 04cd7334eb5d 2 weeks ago 1.05 GB tomcat latest 6fe1972e6b08 2 weeks ago 347.7 MB mongo latest 5c9464760d54 2 weeks ago 260.8 MB nginx latest cd3cf76a61ee 2 weeks ago 132.9 MB gstafford@gstafford-X555LA:$ weave ps weave:expose 6a:69:11:1b:b4:e3 2bce66e3b33b fa:07:7e:85:37:1b 10.32.0.5/12 604dbbc4473f 6a:73:8d:54:cc:fe 10.32.0.4/12 ea64b42cf5a1 c2:69:73:84:67:69 10.32.0.3/12 85b1e8a9b8d0 aa:f7:12:cd:b7:13 10.32.0.6/12 81041fc97d1f 2e:1e:82:67:89:5d 10.32.0.2/12 e80c04bdbfaf 1e:95:a5:b2:9d:30 10.32.0.1/12 18c22e7f1c33 7e:43:54:db:8d:b8 gstafford@gstafford-X555LA:$ docker ps -a CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 2bce66e3b33b music_app01 "/w/w catalina.sh run" 3 days ago Up 3 days 0.0.0.0:8180->8080/tcp music_app01_1 604dbbc4473f music_logspout "/w/w /bin/logspout" 3 days ago Up 3 days 8000/tcp, 0.0.0.0:8083->80/tcp music_logspout_1 ea64b42cf5a1 music_app02 "/w/w catalina.sh run" 3 days ago Up 3 days 0.0.0.0:8280->8080/tcp music_app02_1 85b1e8a9b8d0 music_proxy "/w/w nginx -g 'daemo" 3 days ago Up 3 days 0.0.0.0:80->80/tcp, 443/tcp music_proxy_1 81041fc97d1f music_nosqldb "/w/w /entrypoint.sh " 3 days ago Up 3 days 27017/tcp music_nosqldb_1 e80c04bdbfaf music_elk "/w/w /usr/bin/superv" 3 days ago Up 3 days 5000/0, 0.0.0.0:8081->80/tcp, 0.0.0.0:8082->9200/tcp music_elk_1 8eafc6225fc1 weaveworks/weaveexec:v1.1.0 "/home/weave/weavepro" 3 days ago Up 3 days weaveproxy 18c22e7f1c33 weaveworks/weave:v1.1.0 "/home/weave/weaver -" 3 days ago Up 3 days 172.17.42.1:53->53/udp, 0.0.0.0:6783->6783/tcp, 0.0.0.0:6783->6783/udp, 172.17.42.1:53->53/tcp weave
Spring Music Application Links
Assuming springmusic VM is running at 192.168.99.100, these are the accessible URL for each of the environment’s major components:
- Spring Music: 192.168.99.100
- NGINX: 192.168.99.100/nginx_status
- Tomcat Node 1*: 192.168.99.100:8180/manager
- Tomcat Node 2*: 192.168.99.100:8280/manager
- Kibana: 192.168.99.100:8081
- Elasticsearch: 192.168.99.100:8082
- Elasticsearch: 192.168.99.100:8082/_status?pretty
- Logspout: 192.168.99.100:8083/logs
* The Tomcat user name is admin and the password is t0mcat53rv3r.
Helpful Links
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Gary Stafford
Enterprise Architect | AWS Certified Professional | DevOps | Azure | GCP | Containers | Serverless | Spring | .NET | Node.js
