Aida  Stamm

Aida Stamm

1589640039

Go Application Performance Monitoring

Golang Application Monitoring is a component which provides you end-to-end visibility into the performance of your applications. It allows us the following functionalities:

  • Troubleshoot problems like slow response times and application errors.
  • Measure end-to-end transaction performance with the health of individual application and infrastructure nodes.
  • Analyze applications at the code execution level with snapshots.
  • Automatically discover application topology and how components in the application environment fulfill transactions for its users.
  • Receive alerts based on custom or built-in health rules, including rules against dynamic performance baselines which alert you to issues.

#insights #application #monitoring #golang

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Go Application Performance Monitoring
Gerhard  Brink

Gerhard Brink

1624006278

The Rising Value of Big Data in Application Monitoring

In an ecosystem that has become increasingly integrated with huge chunks of data and information traveling through the airwaves, Big Data has become irreplaceable for establishments.

From day-to-day business operations to detailed customer interactions, many ventures heavily invest in data sciences and data analysis  to find breakthroughs and marketable insights.

Plus, surviving in the current era, mandates taking informed decisions and surgical precision based on the projected forecast of current trends to retain profitability. Hence these days, data is revered as the most valuable resource.

According to a recent study by Sigma Computing , the world of Big Data is only projected to grow bigger, and by 2025 it is estimated that the global data-sphere will grow to reach 17.5 Zettabytes. FYI one Zettabyte is equal to 1 million Petabytes.

Moreover, the Big Data industry will be worth an estimate of $77 billion by 2023. Furthermore, the Banking sector generates unparalleled quantities of data, with the amount of data generated by the financial industry each second growing by 700% in 2021.

In light of this information, let’s take a quick look at some of the ways application monitoring can use Big Data, along with its growing importance and impact.

#ai in business #ai application #application monitoring #big data #the rising value of big data in application monitoring #application monitoring

Carmen  Grimes

Carmen Grimes

1598959140

How to Monitor Third Party API Integrations

Many enterprises and SaaS companies depend on a variety of external API integrations in order to build an awesome customer experience. Some integrations may outsource certain business functionality such as handling payments or search to companies like Stripe and Algolia. You may have integrated other partners which expand the functionality of your product offering, For example, if you want to add real-time alerts to an analytics tool, you might want to integrate the PagerDuty and Slack APIs into your application.

If you’re like most companies though, you’ll soon realize you’re integrating hundreds of different vendors and partners into your app. Any one of them could have performance or functional issues impacting your customer experience. Worst yet, the reliability of an integration may be less visible than your own APIs and backend. If the login functionality is broken, you’ll have many customers complaining they cannot log into your website. However, if your Slack integration is broken, only the customers who added Slack to their account will be impacted. On top of that, since the integration is asynchronous, your customers may not realize the integration is broken until after a few days when they haven’t received any alerts for some time.

How do you ensure your API integrations are reliable and high performing? After all, if you’re selling a feature real-time alerting, you’re alerts better well be real-time and have at least once guaranteed delivery. Dropping alerts because your Slack or PagerDuty integration is unacceptable from a customer experience perspective.

What to monitor

Latency

Specific API integrations that have an exceedingly high latency could be a signal that your integration is about to fail. Maybe your pagination scheme is incorrect or the vendor has not indexed your data in the best way for you to efficiently query.

Latency best practices

Average latency only tells you half the story. An API that consistently takes one second to complete is usually better than an API with high variance. For example if an API only takes 30 milliseconds on average, but 1 out of 10 API calls take up to five seconds, then you have high variance in your customer experience. This is makes it much harder to track down bugs and harder to handle in your customer experience. This is why 90th percentile and 95th percentiles are important to look at.

Reliability

Reliability is a key metric to monitor especially since your integrating APIs that you don’t have control over. What percent of API calls are failing? In order to track reliability, you should have a rigid definition on what constitutes a failure.

Reliability best practices

While any API call that has a response status code in the 4xx or 5xx family may be considered an error, you might have specific business cases where the API appears to successfully complete yet the API call should still be considered a failure. For example, a data API integration that returns no matches or no content consistently could be considered failing even though the status code is always 200 OK. Another API could be returning bogus or incomplete data. Data validation is critical for measuring where the data returned is correct and up to date.

Not every API provider and integration partner follows suggested status code mapping

Availability

While reliability is specific to errors and functional correctness, availability and uptime is a pure infrastructure metric that measures how often a service has an outage, even if temporary. Availability is usually measured as a percentage of uptime per year or number of 9’s.

AVAILABILITY %DOWNTIME PER YEARDOWNTIME PER MONTHDOWNTIME PER WEEKDOWNTIME PER DAY90% (“one nine”)36.53 days73.05 hours16.80 hours2.40 hours99% (“two nines”)3.65 days7.31 hours1.68 hours14.40 minutes99.9% (“three nines”)8.77 hours43.83 minutes10.08 minutes1.44 minutes99.99% (“four nines”)52.60 minutes4.38 minutes1.01 minutes8.64 seconds99.999% (“five nines”)5.26 minutes26.30 seconds6.05 seconds864.00 milliseconds99.9999% (“six nines”)31.56 seconds2.63 seconds604.80 milliseconds86.40 milliseconds99.99999% (“seven nines”)3.16 seconds262.98 milliseconds60.48 milliseconds8.64 milliseconds99.999999% (“eight nines”)315.58 milliseconds26.30 milliseconds6.05 milliseconds864.00 microseconds99.9999999% (“nine nines”)31.56 milliseconds2.63 milliseconds604.80 microseconds86.40 microseconds

Usage

Many API providers are priced on API usage. Even if the API is free, they most likely have some sort of rate limiting implemented on the API to ensure bad actors are not starving out good clients. This means tracking your API usage with each integration partner is critical to understand when your current usage is close to the plan limits or their rate limits.

Usage best practices

It’s recommended to tie usage back to your end-users even if the API integration is quite downstream from your customer experience. This enables measuring the direct ROI of specific integrations and finding trends. For example, let’s say your product is a CRM, and you are paying Clearbit $199 dollars a month to enrich up to 2,500 companies. That is a direct cost you have and is tied to your customer’s usage. If you have a free tier and they are using the most of your Clearbit quota, you may want to reconsider your pricing strategy. Potentially, Clearbit enrichment should be on the paid tiers only to reduce your own cost.

How to monitor API integrations

Monitoring API integrations seems like the correct remedy to stay on top of these issues. However, traditional Application Performance Monitoring (APM) tools like New Relic and AppDynamics focus more on monitoring the health of your own websites and infrastructure. This includes infrastructure metrics like memory usage and requests per minute along with application level health such as appdex scores and latency. Of course, if you’re consuming an API that’s running in someone else’s infrastructure, you can’t just ask your third-party providers to install an APM agent that you have access to. This means you need a way to monitor the third-party APIs indirectly or via some other instrumentation methodology.

#monitoring #api integration #api monitoring #monitoring and alerting #monitoring strategies #monitoring tools #api integrations #monitoring microservices

Fannie  Zemlak

Fannie Zemlak

1599854400

What's new in the go 1.15

Go announced Go 1.15 version on 11 Aug 2020. Highlighted updates and features include Substantial improvements to the Go linker, Improved allocation for small objects at high core counts, X.509 CommonName deprecation, GOPROXY supports skipping proxies that return errors, New embedded tzdata package, Several Core Library improvements and more.

As Go promise for maintaining backward compatibility. After upgrading to the latest Go 1.15 version, almost all existing Golang applications or programs continue to compile and run as older Golang version.

#go #golang #go 1.15 #go features #go improvement #go package #go new features

Aida  Stamm

Aida Stamm

1589640039

Go Application Performance Monitoring

Golang Application Monitoring is a component which provides you end-to-end visibility into the performance of your applications. It allows us the following functionalities:

  • Troubleshoot problems like slow response times and application errors.
  • Measure end-to-end transaction performance with the health of individual application and infrastructure nodes.
  • Analyze applications at the code execution level with snapshots.
  • Automatically discover application topology and how components in the application environment fulfill transactions for its users.
  • Receive alerts based on custom or built-in health rules, including rules against dynamic performance baselines which alert you to issues.

#insights #application #monitoring #golang

The Ultimate Guide to Monitoring Serverless Applications

Serverless applications, more often than not, have logic distributed over multiple functions and services, which with growth and agents and wrappers attached, can get more complex and costly. This is where Serverless monitoring comes in to help. But what is Serverless monitoring?

Serverless monitoring allows developers to gain important insight on what happens during each execution and event, errors become more easily visible and measuring resource consumption for each invocation is possible. Simply put, there is no better way to optimize the costs and performance of your applications than using a serverless monitoring tool.

While the old tools for AWS logging and monitoring are obsolete here, the requirements for a good logging system remains:

information should be granular

data should be available on the shortest amount of time

log collection should not impact application performance

These are key elements to look out for when finding the most comprehensive serverless monitoring tool.

What is AWS Lambda?
Let’s go back to basics first and remind ourselves exactly what AWS Lambda is and its purpose.

Serverless architectures are an extension of the principles of the Service Oriented Architectures (SOA), where services (functions) communicate using messages (events). When using this approach correctly, serverless architectures can reduce code complexity and provide easier management of an application.

AWS Lambda is a service which runs your code deployed to a container with pre-allocated CPU, disk and memory. Together, your code and its associated configuration are called a Lambda function; these functions run in response to external events or triggers. Lambda functions are “stateless” with no affinity to the underlying infrastructure, allowing developers to focus solely on the code. Lambda is undoubtedly at the heart of Serverless applications.

#devops #aws #monitoring #serverless #monitoring and performance