Monitoring a Network Connection in Realtime with LiveData

Monitoring network connectivity should be simple. Most of us just want to know if there is internet available. Here I use ConnectivityManager to listen for changes to the network connection.

Code: http://bit.ly/2MEqV6N

🔔 Subscribe: https://www.youtube.com/channel/UCoNZZLhPuuRteu02rh7bzsw

#livedata #web-development

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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

PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II

In our previous posts in this series, we spoke at length about using PgBouncer  and Pgpool-II , the connection pool architecture and pros and cons of leveraging one for your PostgreSQL deployment. In our final post, we will put them head-to-head in a detailed feature comparison and compare the results of PgBouncer vs. Pgpool-II performance for your PostgreSQL hosting !

The bottom line – Pgpool-II is a great tool if you need load-balancing and high availability. Connection pooling is almost a bonus you get alongside. PgBouncer does only one thing, but does it really well. If the objective is to limit the number of connections and reduce resource consumption, PgBouncer wins hands down.

It is also perfectly fine to use both PgBouncer and Pgpool-II in a chain – you can have a PgBouncer to provide connection pooling, which talks to a Pgpool-II instance that provides high availability and load balancing. This gives you the best of both worlds!

PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II

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Performance Testing

While PgBouncer may seem to be the better option in theory, theory can often be misleading. So, we pitted the two connection poolers head-to-head, using the standard pgbench tool, to see which one provides better transactions per second throughput through a benchmark test. For good measure, we ran the same tests without a connection pooler too.

Testing Conditions

All of the PostgreSQL benchmark tests were run under the following conditions:

1. Initialized pgbench using a scale factor of 100.
2. Disabled auto-vacuuming on the PostgreSQL instance to prevent interference.
3. No other workload was working at the time.
4. Used the default pgbench script to run the tests.
5. Used default settings for both PgBouncer and Pgpool-II, except max_children*. All PostgreSQL limits were also set to their defaults.
6. All tests ran as a single thread, on a single-CPU, 2-core machine, for a duration of 5 minutes.
7. Forced pgbench to create a new connection for each transaction using the -C option. This emulates modern web application workloads and is the whole reason to use a pooler!

We ran each iteration for 5 minutes to ensure any noise averaged out. Here is how the middleware was installed:

• For PgBouncer, we installed it on the same box as the PostgreSQL server(s). This is the configuration we use in our managed PostgreSQL clusters. Since PgBouncer is a very light-weight process, installing it on the box has no impact on overall performance.
• For Pgpool-II, we tested both when the Pgpool-II instance was installed on the same machine as PostgreSQL (on box column), and when it was installed on a different machine (off box column). As expected, the performance is much better when Pgpool-II is off the box as it doesn’t have to compete with the PostgreSQL server for resources.

Throughput Benchmark

Here are the transactions per second (TPS) results for each scenario across a range of number of clients:

#database #developer #performance #postgresql #connection control #connection pooler #connection pooler performance #connection queue #high availability #load balancing #number of connections #performance testing #pgbench #pgbouncer #pgbouncer and pgpool-ii #pgbouncer vs pgpool #pgpool-ii #pooling modes #postgresql connection pooling #postgresql limits #resource consumption #throughput benchmark #transactions per second #without pooling

Autonomous Driving Network (ADN) On Its Way

Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.

Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.

The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.

Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.

In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.

The Vision

At the current stage, there are different terminologies to describe ADN vision by various organizations.

Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.

On the network layer, it contains the below critical aspects:

• Intent-Driven: Understand the operator’s business intent and automatically translate it into necessary network operations. The operation can be a one-time operation like disconnect a connection service or continuous operations like maintaining a specified SLA (Service Level Agreement) at the all-time.
• **Self-Discover: **Automatically discover hardware/software changes in the network and populate the changes to the necessary subsystems to maintain always-sync state.
• **Self-Config/Self-Organize: **Whenever network changes happen, automatically configure corresponding hardware/software parameters such that the network is at the pre-defined target states.
• **Self-Monitor: **Constantly monitor networks/services operation states and health conditions automatically.
• Auto-Detect: Detect network faults, abnormalities, and intrusions automatically.
• **Self-Diagnose: **Automatically conduct an inference process to figure out the root causes of issues.
• **Self-Healing: **Automatically take necessary actions to address issues and bring the networks/services back to the desired state.
• **Self-Report: **Automatically communicate with its environment and exchange necessary information.
• Automated common operational scenarios: Automatically perform operations like network planning, customer and service onboarding, network change management.

On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).

No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.

David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):

“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”

Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)

“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”

Is This Vision Achievable?

If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:

• software-defined networking (SDN) control
• industry-standard models and open APIs
• Real-time analytics/telemetry
• big data processing
• cross-domain orchestration
• programmable infrastructure
• cloud-native virtualized network functions (VNF)
• DevOps agile development process
• everything-as-service design paradigm
• intelligent process automation
• edge computing
• cloud infrastructure
• programing paradigm suitable for building an autonomous system . i.e., teleo-reactive programs, which is a set of reactive rules that continuously sense the environment and trigger actions whose continuous execution eventually leads the system to satisfy a goal. (Nils Nilsson, 1996)
• open-source solutions

#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks

Monitoring a Network Connection in Realtime with LiveData

Monitoring network connectivity should be simple. Most of us just want to know if there is internet available. Here I use ConnectivityManager to listen for changes to the network connection.

Code: http://bit.ly/2MEqV6N

🔔 Subscribe: https://www.youtube.com/channel/UCoNZZLhPuuRteu02rh7bzsw

#livedata #web-development

Matic Network in Blockchain Gaming

Matic Network is getting lots of attraction amidst the blockchain game developers. This is because, their competition has stepped away from the gaming scene. Matic - as a general purpose platform, capable of creating all types of DApps, and have already build 60+ DApps on Matic Network.

As a result Matic Network is busy gaining a lots of new gaming partners. They have already been integrated into many gaming DApps.

Key reasons why DApps chooses Matic Network

• Near-instant blockchain transactions
• Low Transaction fees >> less than 1/1000th of the fees on the Ethereum mainchain
• Seamless migration for existing Ethereum DApps
• Access to, and assistance with, a wide range of developer tooling.
• Unparalleled technical support for developers.

If you have an idea to build your own Gaming DApp - you could benefit from matic network’s high-speed, low-fee infrastructure and our assistance to transform your DApp from a great idea into a successful DApp business.

Being a Predominant DApp Game Development Company, GamesDApp helps you to Build DApp Game on matic network and also expertize in developing various popular games on the blockchain network using smart contract.

Hire Blockchain Game Developers >> https://www.gamesd.app/#contactus

#matic network #build dapp game on matic network #dapp game on matic network #matic network in blockchain gaming #matic network for game development