Liran Katz

1601758980

The Roles of Service Mesh and API Gateways in Microservice Architecture

If you’re into microservices, then you might’ve heard about these two terms multiple times. Often people get confused between the two. In this article, I’m going to talk about service meshes and API gateways in detail and discuss when to use what.

Network Layers Refresher

Before diving into service meshes and API gateways, let’s revisit the network layers. The following is the OSI network layer model:

#software-development #programming #microservices #api

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The Roles of Service Mesh and API Gateways in Microservice Architecture

The Service Mesh in the Microservices World - DZone Microservices

The software industry has come a long journey and throughout this journey, Software Architecture has evolved a lot. Starting with 1-tier (Single-node), 2-tier (Client/ Server), 3-tier, and Distributed are some of the Software Architectural patterns we saw in this journey.

The Problem

The majority of software companies are moving from Monolithic architecture to Microservices architecture, and Microservices architecture is taking over the software industry day-by-day. While monolithic architecture has many benefits, it also has so many shortcomings when catering to modern software development needs. With those shortcomings of monolithic architecture, it is very difficult to meet the demand of the modern-world software requirements and as a result, microservices architecture is taking control of the software development aggressively. The Microservices architecture enables us to deploy our applications more frequently, independently, and reliably meeting modern-day software application development requirements.

#microservice architecture #istio #microservice best practices #linkerd #microservice communication #microservice design #envoy proxy #kubernetes architecture #api gateways #service mesh architecture

Deciphering the Difference Between a Service Mesh and API Gateway

In a microservices architecture, apps trade the rigidity and stability of the call stack for the flexibility and chaos of the network. Concerns such as latency, outage retries, security, and traceability that were not a concern with a call stack become a concern with a service call. Service mesh is a pattern that has arisen to take these concerns out of the hands of coders so that they can stay focused on coding business solutions.

There is much overlap between an API gateway and a service mesh. This article explores what a service mesh is, its benefits to your organization, how it differs from an API gateway, and provides recommendations for service mesh’s use.

Executive Summary of Recommendations

Any application team building a large distributed componentized application running on containers should use a service mesh to manage, secure, and monitor their services. The traffic between these intra-application services is what a service mesh is best suited for. API gateways should, in contrast, be used to manage interactions between your business and your partners or between one internal business unit and another.

A service mesh comes in a variety of patterns, but the ideal pattern you should utilize is a sidecar proxy running in containers. Although Istio is the most common service mesh product, Consul, Linkerd, the service mesh Red Hat bundles with OpenShift (a fork of Istio), and more are also options for Kubernetes-based containers. Before investing in a service mesh, you should evaluate the landscape of service mesh products, their maturity, and if the industry has settled on a clear winner (as, for example, happened with in the container space with Kubernetes winning the de facto industry standard for containers).

Although a service mesh overlaps heavily with API management, security, resilience, and monitoring, it is best viewed as a cloud technology since it is so intertwined with containers and is meant to support cloud-native apps. Note, by “cloud native” I include apps designed to run on public cloud and also private (on-premises) cloud containers.

What Is a Service Mesh?

Moving from the call stack of function invocation to a network call introduces issues with security, instability, and debugging. A service mesh is a set of architectural patterns and supporting tools for handling those concerns. For one example, a function call knows the function being called is always available whereas a network call cannot. A service mesh will help the client endpoint handle this network instability by executing retries transparent to the client app. It will also help the server endpoint by routing the request to the server node best able to handle the based on configured policies of how to route traffic.

A service mesh is usually implemented with two layers: a data plane and a control plane. The data plane acts as a proxy for both client and server endpoints of a connection, enforcing the policies received from the control plane and reporting back runtime metrics to the control plane’s monitoring tool. The control plane manages the service policies and orchestration of the data plane.

Client through a proxy to another proxy fronting the service. Both proxies are managed by a service mesh control plane.

Client through a proxy to another proxy fronting the service. Both proxies are managed by a service mesh control plane.

Topology of a service mesh.

The most popular data plane is Envoy, an open source proxy created by Lyft that runs as a sidecar for cloud-native apps, including on-premises private cloud. The most popular control plane is Istio, an open source service mesh created jointly by Lyft, Google, and IBM to inject and manage Envoy instances into cloud-native apps as a container sidecar.

Below are some typical service mesh features, though not every service mesh implementation comes with all of these.

#api-gateway #service-mesh #microservice-architecture #application-architecture #microservices

Autumn  Blick

Autumn Blick

1601381326

Public ASX100 APIs: The Essential List

We’ve conducted some initial research into the public APIs of the ASX100 because we regularly have conversations about what others are doing with their APIs and what best practices look like. Being able to point to good local examples and explain what is happening in Australia is a key part of this conversation.

Method

The method used for this initial research was to obtain a list of the ASX100 (as of 18 September 2020). Then work through each company looking at the following:

  1. Whether the company had a public API: this was found by googling “[company name] API” and “[company name] API developer” and “[company name] developer portal”. Sometimes the company’s website was navigated or searched.
  2. Some data points about the API were noted, such as the URL of the portal/documentation and the method they used to publish the API (portal, documentation, web page).
  3. Observations were recorded that piqued the interest of the researchers (you will find these below).
  4. Other notes were made to support future research.
  5. You will find a summary of the data in the infographic below.

Data

With regards to how the APIs are shared:

#api #api-development #api-analytics #apis #api-integration #api-testing #api-security #api-gateway

Top 10 API Security Threats Every API Team Should Know

As more and more data is exposed via APIs either as API-first companies or for the explosion of single page apps/JAMStack, API security can no longer be an afterthought. The hard part about APIs is that it provides direct access to large amounts of data while bypassing browser precautions. Instead of worrying about SQL injection and XSS issues, you should be concerned about the bad actor who was able to paginate through all your customer records and their data.

Typical prevention mechanisms like Captchas and browser fingerprinting won’t work since APIs by design need to handle a very large number of API accesses even by a single customer. So where do you start? The first thing is to put yourself in the shoes of a hacker and then instrument your APIs to detect and block common attacks along with unknown unknowns for zero-day exploits. Some of these are on the OWASP Security API list, but not all.

Insecure pagination and resource limits

Most APIs provide access to resources that are lists of entities such as /users or /widgets. A client such as a browser would typically filter and paginate through this list to limit the number items returned to a client like so:

First Call: GET /items?skip=0&take=10 
Second Call: GET /items?skip=10&take=10

However, if that entity has any PII or other information, then a hacker could scrape that endpoint to get a dump of all entities in your database. This could be most dangerous if those entities accidently exposed PII or other sensitive information, but could also be dangerous in providing competitors or others with adoption and usage stats for your business or provide scammers with a way to get large email lists. See how Venmo data was scraped

A naive protection mechanism would be to check the take count and throw an error if greater than 100 or 1000. The problem with this is two-fold:

  1. For data APIs, legitimate customers may need to fetch and sync a large number of records such as via cron jobs. Artificially small pagination limits can force your API to be very chatty decreasing overall throughput. Max limits are to ensure memory and scalability requirements are met (and prevent certain DDoS attacks), not to guarantee security.
  2. This offers zero protection to a hacker that writes a simple script that sleeps a random delay between repeated accesses.
skip = 0
while True:    response = requests.post('https://api.acmeinc.com/widgets?take=10&skip=' + skip),                      headers={'Authorization': 'Bearer' + ' ' + sys.argv[1]})    print("Fetched 10 items")    sleep(randint(100,1000))    skip += 10

How to secure against pagination attacks

To secure against pagination attacks, you should track how many items of a single resource are accessed within a certain time period for each user or API key rather than just at the request level. By tracking API resource access at the user level, you can block a user or API key once they hit a threshold such as “touched 1,000,000 items in a one hour period”. This is dependent on your API use case and can even be dependent on their subscription with you. Like a Captcha, this can slow down the speed that a hacker can exploit your API, like a Captcha if they have to create a new user account manually to create a new API key.

Insecure API key generation

Most APIs are protected by some sort of API key or JWT (JSON Web Token). This provides a natural way to track and protect your API as API security tools can detect abnormal API behavior and block access to an API key automatically. However, hackers will want to outsmart these mechanisms by generating and using a large pool of API keys from a large number of users just like a web hacker would use a large pool of IP addresses to circumvent DDoS protection.

How to secure against API key pools

The easiest way to secure against these types of attacks is by requiring a human to sign up for your service and generate API keys. Bot traffic can be prevented with things like Captcha and 2-Factor Authentication. Unless there is a legitimate business case, new users who sign up for your service should not have the ability to generate API keys programmatically. Instead, only trusted customers should have the ability to generate API keys programmatically. Go one step further and ensure any anomaly detection for abnormal behavior is done at the user and account level, not just for each API key.

Accidental key exposure

APIs are used in a way that increases the probability credentials are leaked:

  1. APIs are expected to be accessed over indefinite time periods, which increases the probability that a hacker obtains a valid API key that’s not expired. You save that API key in a server environment variable and forget about it. This is a drastic contrast to a user logging into an interactive website where the session expires after a short duration.
  2. The consumer of an API has direct access to the credentials such as when debugging via Postman or CURL. It only takes a single developer to accidently copy/pastes the CURL command containing the API key into a public forum like in GitHub Issues or Stack Overflow.
  3. API keys are usually bearer tokens without requiring any other identifying information. APIs cannot leverage things like one-time use tokens or 2-factor authentication.

If a key is exposed due to user error, one may think you as the API provider has any blame. However, security is all about reducing surface area and risk. Treat your customer data as if it’s your own and help them by adding guards that prevent accidental key exposure.

How to prevent accidental key exposure

The easiest way to prevent key exposure is by leveraging two tokens rather than one. A refresh token is stored as an environment variable and can only be used to generate short lived access tokens. Unlike the refresh token, these short lived tokens can access the resources, but are time limited such as in hours or days.

The customer will store the refresh token with other API keys. Then your SDK will generate access tokens on SDK init or when the last access token expires. If a CURL command gets pasted into a GitHub issue, then a hacker would need to use it within hours reducing the attack vector (unless it was the actual refresh token which is low probability)

Exposure to DDoS attacks

APIs open up entirely new business models where customers can access your API platform programmatically. However, this can make DDoS protection tricky. Most DDoS protection is designed to absorb and reject a large number of requests from bad actors during DDoS attacks but still need to let the good ones through. This requires fingerprinting the HTTP requests to check against what looks like bot traffic. This is much harder for API products as all traffic looks like bot traffic and is not coming from a browser where things like cookies are present.

Stopping DDoS attacks

The magical part about APIs is almost every access requires an API Key. If a request doesn’t have an API key, you can automatically reject it which is lightweight on your servers (Ensure authentication is short circuited very early before later middleware like request JSON parsing). So then how do you handle authenticated requests? The easiest is to leverage rate limit counters for each API key such as to handle X requests per minute and reject those above the threshold with a 429 HTTP response. There are a variety of algorithms to do this such as leaky bucket and fixed window counters.

Incorrect server security

APIs are no different than web servers when it comes to good server hygiene. Data can be leaked due to misconfigured SSL certificate or allowing non-HTTPS traffic. For modern applications, there is very little reason to accept non-HTTPS requests, but a customer could mistakenly issue a non HTTP request from their application or CURL exposing the API key. APIs do not have the protection of a browser so things like HSTS or redirect to HTTPS offer no protection.

How to ensure proper SSL

Test your SSL implementation over at Qualys SSL Test or similar tool. You should also block all non-HTTP requests which can be done within your load balancer. You should also remove any HTTP headers scrub any error messages that leak implementation details. If your API is used only by your own apps or can only be accessed server-side, then review Authoritative guide to Cross-Origin Resource Sharing for REST APIs

Incorrect caching headers

APIs provide access to dynamic data that’s scoped to each API key. Any caching implementation should have the ability to scope to an API key to prevent cross-pollution. Even if you don’t cache anything in your infrastructure, you could expose your customers to security holes. If a customer with a proxy server was using multiple API keys such as one for development and one for production, then they could see cross-pollinated data.

#api management #api security #api best practices #api providers #security analytics #api management policies #api access tokens #api access #api security risks #api access keys

Roberta  Ward

Roberta Ward

1598169240

From Service Mess to Service Mesh

Introduction

Over the last 10 years, the rapid adoption of microservices architecture has resulted in enterprises with hundreds or (sometimes even thousands) of services. With the growth of containerization technologies like Docker and Kubernetes, microservice patterns have seen the strongest growth; resulting in a complex dependency matrix between these micro-services. For teams to monitor, support, and to maintain these services is becoming a challenge so most enterprises have invested in some kind of microservices management tool.

This article will explore some of the common aspects of microservice management. Then we’ll take a closer look at the centralized gateway pattern, as well as its limitations (most enterprises have started with or currently still use this pattern). Then we will look into a new pattern called “Service Mesh” which has gained a lot of attention in the last 3–4 years. Often this pattern is also referred to as the “Side Car Proxy”. So lets get started!

Micro-Services Management

As enterprises start building more and more microservices, it’s becoming clear that some of the aspects of microservices are common across all microservices. So it makes sense to provide a common platform for managing these common aspects. Below are some of the key common aspects:

Service Registration and Discovery: A commonplace to register, document, search and discover microservices

Service Version Management: Ability to run multiple versions of a microservice.

**Authentication and Authorization: **Handle authentication and authorization including Mutual TLS (MTLS) between services.

Service Observability: Ability to monitor end to end traffic between services, response times, and quickly identify failures and bottlenecks.

**Rate Limiting: **Define threshold limits that traffic services can handle.

Circuit Breaker: Ability to configure and introduce a circuit breaker in case of failure scenarios (to avoid flooding downstream services with requests).

**Retry Logic: **Ability to configure and introduce retry logic dynamically in services.

So it’s a good idea to build these concerns as part of a common framework or service management tool. As a result, micro-service development teams don’t have to build these aspects in the service itself.

#service-mesh #istio-service-mesh #microservices #gateway-service #envoy-proxy