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Friday
Apr142017

Stuff The Internet Says On Scalability For April 14th, 2017

Hey, it's HighScalability time:

 

After 20 years, Cassini will not go gently into that good night, it will burn and rave at close of day. (nasa)

If you like this sort of Stuff then please support me on Patreon.
  • 10^15: synapses activated per second in human brain (2/3rds fail); $4.5B: Amazon spend on video (Netflix $6 billion); 22,000: AWS database migrations served; ~15%: Dropbox reduced CPU usage using Brotli; $3.5 trillion: IT spending in 2017; 10%: reduction in QoQ hard drive shipments; 33.3%: Nginx share of webserver market; 37.2 trillion: human cells in a Cell Atlas; 6.2 miles: journey to the center of the earth; 200: lines of code for blockchain; 95%: Wikipedia pages end up at philosophy; 1.2 billion: Messenger monthly users; 

  • Quotable Quotes:
    • Jeff Bezos: Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always Day 1.
    • Bob Schmidt: If debugging is the process of removing errors from a design, then designing must be the process of putting errors into a design!
    • @swardley: the gap between where the cutting edge is and where the majority are just seems to increase year on year.
    • Riot Games: We need to provide resources when it's time to grow, we need to react when it gets sick, and we need to do it all as fast as possible at a global scale.
    • masklinn: High-performance native code already does these specialisation, generally on a per-project basis (some projects include multiple allocators for different bits of data), and possibly using a non-OS allocator in the first place
    • @erikbryn: MT: @DKThomp : there are 950k warehouse workers —6X the number of steel workers and miners combined
    • Joeri: The challenge of a rewrite is not in mapping the core architecture and core use case, it's mapping all the edge cases and covering all the end user needs. You need people intimately familiar with the old system to make sure the new system does all the weird stuff which nobody understood but had good reasons that was in the corners of the old system's code. 
    • @redblobgames: 2016 GDC Diablo talk: let's switch from turn-based to real-time 2017 GDC Civilization talk: let's switch from real-time to turn-based
    • @random_walker: Encrypted traffic has a fingerprint—enough to distinguish among 200 Netflix vids with 99.5% accuracy in < 2.5 mins.
    • Sophie Wilson: You’re going to buy a 10-way, 18-way multi-core processor that’s the latest, all because we told you you could buy it and made it available, and we’re going to turn some of those processors off most of the time. So you’re going to pay for logic and we’re going to turn it off so you can’t use it.
    • qq66: But is there anything more personal than a computer programmer writing a bot to send messages for him?
    • Anu Hariharan: Unlike other social products, WeChat does not only measure growth by number of users or messages sent. Instead they also focus on measuring how deeply is the product engaged in every aspect of daily life (e.g., the number of tasks WeChat can help with in a day).
    • @fredwilson: "The real issue here is Facebook’s market power. And we face similar market power issues in search (Google) and commerce (Amazon)"
    • There are so many quotable quotes I couldn't include them all here. Click through to read the full article.

  • Luna Duclos on Game Development and Rebuilding Microservices. Switching from PHP/Python to Go. Go is much faster and uses less CPU. As big as the switch to Go is the switch from Google App Engine to VMs. GAE servers are small and CPU constrained despite the relatively high cost. Their Go cluster runs in the Google Cloud on Google Container Engine.

  • Werner Against the Machine. Wait, aren't you the machine now?

  • Kwabena Boahe on Stanford Seminar: Neuromorphic Chips: Addressing the Nanostransistor Challenge. A dollar bought more and more transistors until 2014, when for the first time the price for transistors went up. Fundamental constraints at the physical level is the cause. The challenge is to continually shrink the footprint of the transistor so it occupies less space. A traffic metaphor is used to explain the difficulty of continually shrinking transistors. Shrinking gives you fewer lanes and electrons can block a lane by being trapped in a pothole. When you get down to one lane and electron is trapped the current flows slowly. Our brains work with ultimately scaled devices...

Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...

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Tuesday
Apr112017

Sponsored Post: Pier 1, Aerospike, Clubhouse, Stream, Scalyr, VividCortex, MemSQL, InMemory.Net, Zohocorp

Who's Hiring? 

  • Pier 1 Imports is looking for an amazing Sr. Website Engineer to join our growing team!  Our customer continues to evolve the way she prefers to shop, speak to, and engage with us at Pier 1 Imports.  Driving us to innovate more ways to surprise and delight her expectations as a Premier Home and Decor retailer.  We are looking for a candidate to be another key member of a driven agile team. This person will inform and apply modern technical expertise to website site performance, development and design techniques for Pier.com. To apply please email cmwelsh@pier1.com. More details are available here.

  • Etleap is looking for Senior Data Engineers to build the next-generation ETL solution. Data analytics teams need solid infrastructure and great ETL tools to be successful. It shouldn't take a CS degree to use big data effectively, and abstracting away the difficult parts is our mission. We use Java extensively, and distributed systems experience is a big plus! See full job description and apply here.

  • Advertise your job here! 

Fun and Informative Events

  • DBTA Roundtable OnDemand Webinar: Leveraging Big Data with Hadoop, NoSQL and RDBMS. Watch this recent roundtable discussion hosted by DBTA to learn about key differences between Hadoop, NoSQL and RDBMS. Topics include primary use cases, selection criteria, when a hybrid approach will best fit your needs and best practices for managing, securing and integrating data across platforms. Brian Bulkowski, CTO and Co-founder of Aerospike, presented along with speakers from Cask Data and Splice Machine. View now.

  • Advertise your event here!

Cool Products and Services

  • A note for .NET developers: You know the pain of troubleshooting errors with limited time, limited information, and limited tools. Log management, exception tracking, and monitoring solutions can help, but many of them treat the .NET platform as an afterthought. You should learn about Loupe...Loupe is a .NET logging and monitoring solution made for the .NET platform from day one. It helps you find and fix problems fast by tracking performance metrics, capturing errors in your .NET software, identifying which errors are causing the greatest impact, and pinpointing root causes. Learn more and try it free today.

  • Etleap provides a SaaS ETL tool that makes it easy to create and operate a Redshift data warehouse at a small fraction of the typical time and cost. It combines the ability to do deep transformations on large data sets with self-service usability, and no coding is required. Sign up for a 30-day free trial.

  • InMemory.Net provides a Dot Net native in memory database for analysing large amounts of data. It runs natively on .Net, and provides a native .Net, COM & ODBC apis for integration. It also has an easy to use language for importing data, and supports standard SQL for querying data. http://InMemory.Net

  • www.site24x7.com : Monitor End User Experience from a global monitoring network. 

  • Working on a software product? Clubhouse is a project management tool that helps software teams plan, build, and deploy their products with ease. Try it free today or learn why thousands of teams use Clubhouse as a Trello alternative or JIRA alternative.

  • Build, scale and personalize your news feeds and activity streams with getstream.io. Try the API now in this 5 minute interactive tutorial. Stream is free up to 3 million feed updates so it's easy to get started. Client libraries are available for Node, Ruby, Python, PHP, Go, Java and .NET. Stream is currently also hiring Devops and Python/Go developers in Amsterdam. More than 400 companies rely on Stream for their production feed infrastructure, this includes apps with 30 million users. With your help we'd like to ad a few zeros to that number. Check out the job opening on AngelList.

  • Scalyr is a lightning-fast log management and operational data platform.  It's a tool (actually, multiple tools) that your entire team will love.  Get visibility into your production issues without juggling multiple tabs and different services -- all of your logs, server metrics and alerts are in your browser and at your fingertips. .  Loved and used by teams at Codecademy, ReturnPath, Grab, and InsideSales. Learn more today or see why Scalyr is a great alternative to Splunk.

  • VividCortex is a SaaS database monitoring product that provides the best way for organizations to improve their database performance, efficiency, and uptime. Currently supporting MySQL, PostgreSQL, Redis, MongoDB, and Amazon Aurora database types, it's a secure, cloud-hosted platform that eliminates businesses' most critical visibility gap. VividCortex uses patented algorithms to analyze and surface relevant insights, so users can proactively fix future performance problems before they impact customers.

  • MemSQL provides a distributed in-memory database for high value data. It's designed to handle extreme data ingest and store the data for real-time, streaming and historical analysis using SQL. MemSQL also cost effectively supports both application and ad-hoc queries concurrently across all data. Start a free 30 day trial here: http://www.memsql.com/

  • Advertise your product or service here!

If you are interested in a sponsored post for an event, job, or product, please contact us for more information.

Click to read more ...

Monday
Apr102017

Five things we’ve learned about monitoring containers and their orchestrators

This is a guest post by Apurva Davé, who is part of the product team at Sysdig.

Having worked with hundreds of customers on building a monitoring stack for their containerized environments, we’ve learned a thing or two about what works and what doesn’t. The outcomes might surprise you - including the observation that instrumentation is just as important as the application when it comes to monitoring.

In this post, I wanted to cover some details around what it takes to build a scale-out, highly reliable monitoring system to work across tens of thousands of containers. I’ll share a bit about what our infrastructure looks like, the design choices we made, and tradeoffs. The five areas I’ll cover:

  • Instrumenting the system

  • Relating your data to your applications, hosts, and containers.

  • Leveraging orchestrators

  • Deciding what to data to store

  • How to enable troubleshooting in containerized environments

For context, Sysdig is the container monitoring company. We’re based on the open source Linux troubleshooting project by the same name. The open source project allows you to see every single system call down to process, arguments, payload, and connection on a single host. The commercial offering turns all this data into thousands of metrics for every container and host, aggregates it all, and gives you dashboarding, alerting, and an htop-like exploration environment.

Ok, let’s get into the details, starting with the impact containers have had on monitoring systems.

Why do containers change the rules of the monitoring game?

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Friday
Apr072017

Stuff The Internet Says On Scalability For April 7th, 2017

Hey, it's HighScalability time:

 

Visualization of the magic system behind software infrastructure. (eyezmaze@ThePracticalDev

If you like this sort of Stuff then please support me on Patreon.
  • 10-20: aminoacids can be made per second; 64800x: faster DDL Aurora vs MySQL; 25 TFLOPS: cap for F1 simulations; 15x to 30x: Tensor Processing Unit faster than GPUs and CPUs; 100 Million: Intel transistors per square millimeter; 25%: Internet traffic generated by Google; $1 million: Tim Berners-Lee wins Turing Award; 43%: phones FBI couldn't open because of crypto;

  • Quotable Quotes:
    • @adulau: To summarize the discussions of yesterday. All tor exit nodes are evil except the ones I operate.
    • @sinavaziri: Let's say a data center costs $1-2B. Then the TPU saved Google $15-30B of capex?
    • Vinton G. Cerf: While it would be a vast overstatement to ascribe all this innovation to genetic disposition, it seems to me inarguable that much of our profession was born in the fecund minds of emigrants coming to America and to the West over the past century.
    • Alan Bundy: AI systems are not just narrowly focused by design, because we have yet to accomplish artificial general intelligence, a goal that still looks distant. 
    • JamesBarney: Soo much this, just worked on a project that sacrificed reliability, maintainability, and scalability to use a real time database to deal with loads that were on the order of 70 values or 7 writes a second.
    • bobdole1234: 3.5x faster than CPU doesn't sound special, but when you're building inference capacity by the megawatt, you get a lot more of that 3.5x faster TPU inside that hard power constraint.
    • Eugenio Culurciello: As we have been predicting for 10 years, in SoC you can achieve > 10x more performance that current GPUs and > 100x more performance per watt.
    • Google: The TPU’s deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. 
    • visarga: TPU excited me too at first, but when I realized that it is not related to training new networks (research) and is useful only for large scale deployment, I toned down my enthusiasm a little. 
    • Julian Friedman: Kube is being designed by system administrators who like distributed systems, not for programmers who want to focus on their apps.
    • shadowmint: Given what I've seen, I'd argue that clojure has an inherent complexity that results in poor code quality outcomes during the software maintenance cycle.
    • weberc2: I like Go, but it's not dramatically faster than Java. Any contest between the two of them will probably just be a back and forth of optimizations. They share pretty much the same upper bound.
    • adrianratnapala: All this means is that we should stop thinking of this stuff as RAM. Only the L1 cache is really RAM. Everything else is just a kind of fast, volatile, solid state disk that just happens to share an address space with the RAM.
    • pbreit: Getting a million users is infinitely harder than scaling a system to handle a million users. Most systems could run comfortably on a Raspberry Pi.
    • @sustrik: If you want your protocol to be fully reliable in the face of either peer shutting down, the terminal handshake has to be asymmetric. As we've seen above, TCP protocol has symmetric termination algorithm and thus can't, by itself, guarantee full reliability.
    • @damonedwards: Unit tests are critical for good dev, but aren't really ops concern. Integration tests are critical for good ops. Ops wants more int tests.
    • mannigfaltig: the brain appears to spend about 4.7 bits per synapse (26 discernible states, given the noisy computation environment of the brain); so it seems to be plenty enough for general intelligence. This could, of course, merely be a biological limit and on silicon more fine-grained weights might be the optimum.
    • marwanad: The main power of GraphQL is for client developers and lies in the decoupling it provides between the client and server and the ability to fulfill the client needs in a single round trip. This is great for mobile devices with slower networks.
    • kyleschiller: As a pretty good rule of thumb, a system that fails 1/nth of the time and has n opportunities to fail has ~.63 probability of failure, where n is more than ~10.
    • jjirsa: databases aren't where you want to have hipster tech. You want boring things that work. For me, Cassandra is the boring thing that works. 
    • @etherealmind: "rule #1 of Enterprise IT: easier to spend 10 million on equipment than 100k for a person. A third person would increase capacity by 30%"
    • @SwiftOnSecurity: “Just pick a good VPN” is like telling thirsty people to “go to a store and drink clear liquid.” They drank bleach, but at least you helped.
    • falsedan: There's 2 secrets to scaling to millions of users: 1. You aren't going to have millions of users so any work you do to support it is stopping you from delivering features that will make your existing 10 clients happier. 2. Write code that can be replaced (i.e. design for change). 
    • X86BSD: Have you tested running it on a FreeBSD box with ZFS? It has lz4 compression by default and makes such a great storage solution for PG. You get compression, snapshots, replication (not quite realtime but close), self healing, etc etc in a battled hardened and easy to manage filesystem and storage manager. I've found you can't beat ZFS and PG for most applications. Edge cases exist of course everywhere.

  • Worried about too much infrastructure? Only 2% of DNA codes for proteins, the other 98% codes for RNA. Harry Noller Lecture. Maybe lots of infrastructure is not a bad thing. One of they key differences in programming and biology is how in biology form completely determines function. Just amazing to watch in action: mRNA Translation (Advanced). Programming is the complete opposite.

Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...

Click to read more ...

Friday
Mar312017

Stuff The Internet Says On Scalability For March 31st, 2017

Hey, it's HighScalability time:

 

What lies beneath? Networks...of blood vessels. (Wellcome Image Awards)

If you like this sort of Stuff then please support me on Patreon.
  • 5000: node (150,000 pod) clusters in Kubernetes 1.6; 15 years: time to @spacex launch with a recycled rocket booster; 174 mbps: Internet speed in Dublin; 10 nm: Intel’s new Moore approved process; 30 minutes: to create Samsung's S8; 50 billion: of your cells replaced each day; 2 million: new red blood cells per second; 3dbm: attenuation of human body, same as a wall; 12: hours of tardis sounds; 350: pages to stop a bullet; 2: meters of DNA pack in a space .000006m wide; 

  • Quotable Quotes:
    • @swardley: Having met many "leaders" in technology & business, I wouldn't bet on the future survival of humanity. If anything AI might help the odds
    • Francis Pouliot: Any contentious hard fork of the Bitcoin blockchain shall be considered an alternative cryptocurrency (altcoin), regardless of the relative hashing power on the forked chain.
    • @coda: WhatsApp: 900M users, built w/ < 35 devs, using #erlang Krispy Kreme: 1004 locations, 3700 employees, original glazed is 190 #calories
    • @BenedictEvans: Still think it's interesting Instagram shifted emphasis from interests to friends. Is that a law of nature for social if you want scale?
    • @johnrobb: "each robot per thousand workers decreased employment by 6.2 workers and wages by 0.7 percent"
    • Alex Woodie: The Hadoop dream of unifying data and compute in a distributed manner has all but failed in a smoking heap of cost and complexity, according to technology experts and executives who spoke to Datanami.
    • @RichRogersIoT: "First you learn the value of abstraction, then you learn the cost of abstraction, then you are ready to engineer." - @KentBeck
    • @codemanship: Don't explain code quality to execs. Explain high cost of change. Explain slowing down of innovation. Explain longer cycle times.
    • @malwareunicorn: Bad malware pickup lines: Hey girl, I heard you like sandboxes. I would never try to escape yours ;)
    • dkhenry: The selling of data isn't the policy you need to fight. The monopoly power of ISP's is the problem you must push back on. 
    • @MaxWendkos: An SEO expert walks into a bar, bars, pub, tavern, public house, Irish pub, drinks, beer, alcohol
    • Barry Lampert: the point of Amazon isn't to offer a consumer the absolute lowest price possible; it's to offer the lowest price possible given the convenience that Amazon offers
    • Daniel Lemire: Let us make the statement precise: Most performance or memory optimizations are useless.
    • @sarahmei: People run into trouble with DRY because it doesn't tell you *what* not to repeat. People assume syntax, but it's actually concepts.
    • Dan Rayburn: China suffers from 9.2% transfer failure rate (similar to Malaysia, India and Brazil), and a high packet loss.  These two parameters have severe impact on content download time and overall performance.
    • Daniel Lemire: I submit to you that it is no accident if the StackOverflow list of top-paying programming languages is made of obscure languages. They are comparing the average of a niche against the average of a large population
    • For even more Quotable Quotes please click through to the main article.

  • For good WiFi you don't necessarily need one big powerful router bristling with antenna like a radiation mutated ant. 802.eleventy what? A deep dive into why Wi-Fi kind of suck and New Screen Savers (@20 min). You want a true mesh network (Plume). WiFi should whisper, use 5G to create pools of WiFi in each room so signals don't penetrate between rooms. Lots of little access points can automatically find a path through your house. Use a wired backhaul for best performance. Raw throughput isn't the best measure. How does it perform with many people using many devices? Roaming isn't always well supported. Consider how well the system hands-off devices as you walk through the house. 

  • BloomCON 2017 Videos are now available. You might like Honey, I Stole Your C2 [Command-and-control] Server: A dive into attacker infrastructure.

Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...

Click to read more ...

Wednesday
Mar292017

How to speed up your MySQL with replication to in-memory database

Original article available at https://habrahabr.ru/company/mailru/blog/323870/

I’d like to share with you an article based on my talk at Tarantool Meetup(the video is in Russian, though). It’s a short story of why Mamba, one of the biggest dating websites in the world and the largest one in Russia, started using Tarantool. Why did we decide to busy ourselves with MySQL-to-Tarantool replication?

First, we had to migrate to MySQL 5.7 at some point, but this version didn’t have HandlerSocket that was being actively used on our MySQL 5.6 servers. We even contacted the Percona team — and they confirmed MySQL 5.6 is the last version to have HandlerSocket.

Second, we gave Tarantool a try and were pleased with its performance. We compared it against Memcached as a key-value store and saw the speed double from 0.6 ms to 0.3 ms on the same hardware. In relative terms, Tarantool’s twice as fast as Memcached. In absolute terms, it’s not that cool, but still impressive.

Third, we wanted to keep the whole existing architecture. There’s a MySQL master server and its slaves — we didn’t want to change anything in this structure. Can MySQL 5.6 slaves with HandlerSocket be replaced with something else without having to make significant architectural changes?

We learned that the Mail.Ru Group team has a replicator they created for their own purposes. The idea of replicating data from MySQL to Tarantool belongs to them. We asked the team to share the source code, which they did. We had to rewrite the code, though, since it worked with MySQL 5.1 and Tarantool 1.5, not 1.7. The replicator uses libslave, an open-source solution for reading events from a MySQL master server, and is built statically without any of MySQL’s system libraries. It’s been open-sourcedunder the BSD license, so anyone can use it for free.

Replication constraints

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Wednesday
Mar292017

Sponsored Post: ButterCMS, Aerospike, Loupe, Clubhouse, Stream, Scalyr, VividCortex, MemSQL, InMemory.Net, Zohocorp

Who's Hiring? 

  • Etleap is looking for Senior Data Engineers to build the next-generation ETL solution. Data analytics teams need solid infrastructure and great ETL tools to be successful. It shouldn't take a CS degree to use big data effectively, and abstracting away the difficult parts is our mission. We use Java extensively, and distributed systems experience is a big plus! See full job description and apply here.

  • Advertise your job here! 

Fun and Informative Events

  • Analyst Webinar: Forrester Study on Hybrid Memory NoSQL Architecture for Mission-Critical, Real-Time Systems of Engagement. Thursday, March 30, 2017 | 11 AM PT / 2 PM ET. In today’s digital economy, enterprises struggle to cost-effectively deploy customer-facing, edge-based applications with predictable performance, high uptime and reliability. A new, hybrid memory architecture (HMA) has emerged to address this challenge, providing real-time transactional analytics for applications that require speed, scale and a low total cost of ownership (TCO). Forrester recently surveyed IT decision makers to learn about the challenges they face in managing Systems of Engagement (SoE) with traditional database architectures and their adoption of an HMA. Join us as our guest speaker, Forrester Principal Analyst Noel Yuhanna, and Aerospike’s VP Marketing, Cuneyt Buyukbezci, discuss the survey results and implications for your business. Learn and register

  • Advertise your event here!

Cool Products and Services

  • Etleap provides a SaaS ETL tool that makes it easy to create and operate a Redshift data warehouse at a small fraction of the typical time and cost. It combines the ability to do deep transformations on large data sets with self-service usability, and no coding is required. Sign up for a 30-day free trial.

  • InMemory.Net provides a Dot Net native in memory database for analysing large amounts of data. It runs natively on .Net, and provides a native .Net, COM & ODBC apis for integration. It also has an easy to use language for importing data, and supports standard SQL for querying data. http://InMemory.Net

  • www.site24x7.com : Monitor End User Experience from a global monitoring network. 

  • ButterCMS is an API-based CMS that seamlessly drops into your app or website. Great for blogs, dynamic pages, knowledge bases, and more. Butter works with any language/framework including Ruby, Rails, Node.js, .NET, Python, Django, Flask, React, Angular, Go, PHP, Laravel, Elixir, Phoenix, and Meteor.

  • Working on a software product? Clubhouse is a project management tool that helps software teams plan, build, and deploy their products with ease. Try it free today or learn why thousands of teams use Clubhouse as a Trello alternative or JIRA alternative.

  • A note for .NET developers: You know the pain of troubleshooting errors with limited time, limited information, and limited tools. Log management, exception tracking, and monitoring solutions can help, but many of them treat the .NET platform as an afterthought. You should learn about Loupe...Loupe is a .NET logging and monitoring solution made for the .NET platform from day one. It helps you find and fix problems fast by tracking performance metrics, capturing errors in your .NET software, identifying which errors are causing the greatest impact, and pinpointing root causes. Learn more and try it free today.

  • Build, scale and personalize your news feeds and activity streams with getstream.io. Try the API now in this 5 minute interactive tutorial. Stream is free up to 3 million feed updates so it's easy to get started. Client libraries are available for Node, Ruby, Python, PHP, Go, Java and .NET. Stream is currently also hiring Devops and Python/Go developers in Amsterdam. More than 400 companies rely on Stream for their production feed infrastructure, this includes apps with 30 million users. With your help we'd like to ad a few zeros to that number. Check out the job opening on AngelList.

  • Scalyr is a lightning-fast log management and operational data platform.  It's a tool (actually, multiple tools) that your entire team will love.  Get visibility into your production issues without juggling multiple tabs and different services -- all of your logs, server metrics and alerts are in your browser and at your fingertips. .  Loved and used by teams at Codecademy, ReturnPath, Grab, and InsideSales. Learn more today or see why Scalyr is a great alternative to Splunk.

  • VividCortex is a SaaS database monitoring product that provides the best way for organizations to improve their database performance, efficiency, and uptime. Currently supporting MySQL, PostgreSQL, Redis, MongoDB, and Amazon Aurora database types, it's a secure, cloud-hosted platform that eliminates businesses' most critical visibility gap. VividCortex uses patented algorithms to analyze and surface relevant insights, so users can proactively fix future performance problems before they impact customers.

  • MemSQL provides a distributed in-memory database for high value data. It's designed to handle extreme data ingest and store the data for real-time, streaming and historical analysis using SQL. MemSQL also cost effectively supports both application and ad-hoc queries concurrently across all data. Start a free 30 day trial here: http://www.memsql.com/

If you are interested in a sponsored post for an event, job, or product, please contact us for more information.

Click to read more ...

Monday
Mar272017

Faster Networks + Cheaper Messages => Microservices => Functions => Edge

When Adrian Cockroft—the guy who helped put the loud in Cloud through his energetic evangelism of Cloud Native and Microservice architectures—talks about what’s next, it pays to listen. And you can listen, here’s a fascinating forward looking talk he gave at microXchg 2017: Shrinking Microservices to Functions. It’s typically Cockroftian: understated, thoughtful, and full of insight drawn from experience.

Adrian makes a compelling case that the same technology drivers, faster networking and cheaper messaging, that drove the move to Microservices are now driving the move to Functions.

The payoffs are all those you’ve no doubt heard about Serverless for some time, but Adrian develops them in an interesting way. He traces how architectures have evolved over time. Take a look at my gloss of his talk for more details.

What’s next after Functions? Adrian talks about pushing Lambda functions to the edge. A topic I’m excited about and have been interested in for sometime, though I didn’t quite see it playing out like this.

Datacenters disappear. Functions are not running in an AWS region anymore, code is placed near the customer using a CDN at CDN endpoints. Now you have a fully distributed, at the edge, low latency, milliseconds from the customer way of running code. Now you can build architectures that are partly in the datacenter, partly at the edge, and partly at the customer premises. And since this is AWS, it’s all, of course, built around Lambda. AWS Greengrass and Snowball Edge are peeks into what the future might look like.

There’s a hidden tension here. Once you put code at the edge you violate two of Lambda’s key assumptions: functions are composed using scalable backend services; low latency messaging. The edge will have a high latency path back to services in the datacenter, so how do you make a function based distributed application at the edge? Does edge computing argue for a more retro architecture with fewer messages back to a more monolithic core?

Or does edge computing require something completely different? Here’s one thought as to what that something completely different might look like: Datanet: A New CRDT Database That Let's You Do Bad Bad Things To Distributed Data.

Now, let’s see the future by first taking a tour of the past….

From Monoliths, to Microservices, to Functions

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Friday
Mar242017

Stuff The Internet Says On Scalability For March 24th, 2017

Hey, it's HighScalability time:

 This is real and oh so eerie. Custom microscope takes a 33 hour time lapse of a tadpole egg dividing.

If you like this sort of Stuff then please support me on Patreon.

  • 40Gbit/s: indoor optical wireless networks; 15%: energy produced by wind in Europe; 5: new tasty particles; 2000: Qubits are easy; 30 minutes: flight time for electric helicopter; 42.9%: of heathen StackOverflowers prefer tabs;

  • Quotable Quotes:
    • @RichRogersIoT: "Did you know? The collective noun for a group of programmers is a merge-conflict." - @omervk
    • @tjholowaychuk: reviewed my dad's company AWS expenses, devs love over-provisioning, by like 90% too, guess that's where "serverless" cost savings come in
    • @karpathy: Nature is evolving ~7 billion ~10 PetaFLOP NI agents in parallel, and has been for ~10M+s of years, in a very realistic simulator. Not fair.
    • @rbranson: This is funny, but legit. Production software tends to be ugly because production is ugly. The ugliness outpaces our ability to abstract it.
    • @joeweinman: @harrietgreen1 : Watson IoT center opened in Munich... $200 million dollar investment; 1000 engineers #ibminterconnect
    • David Gerard: This [IBM Blockchain Service] is bollocks all the way down.
    • digi_owl: Sometimes it seems that the diff between a CPU and a cluster is the suffix put on the latency times.
    • Scott Aaronson: I’m at an It from Qubit meeting at Stanford, where everyone is talking about how to map quantum theories of gravity to quantum circuits acting on finite sets of qubits, and the questions in quantum circuit complexity that are thereby raised.
    • Founder Collective: Firebase didn’t try to do everything at once. Instead, they focused on a few core problems and executed brilliantly. “We built a nice syntax with sugar on top,” says Tamplin. “We made real-time possible and delightful.” It is a reminder that entrepreneurs can rapidly add value to the ecosystem if they really focus.
    • Elizabeth Kolbert: Reason developed not to enable us to solve abstract, logical problems or even to help us draw conclusions from unfamiliar data; rather, it developed to resolve the problems posed by living in collaborative groups. 
    • Western Union: the ‘telephone’ has too many shortcomings to be seriously considered as a means of communication.
    • Arthur Doskow: being fair, being humane may cost money. And this is the real issue with many algorithms. In economists’ terms, the inhumanity associated with an algorithm could be referred to as an externality. 
    • Francis: The point is that even if GPUs will support lower precision data types exclusively for AI, ML and DNN, they will still carry the big overhead of the graphics pipeline, hence lower efficiency than an FPGA (in terms of FLOPS/WATT). The winner? Dedicated AI processors, e.g. Google TPU
    • James Glasnapp: When we move out of the physical space to a technological one, how is the concept of a “line” assessed by the customer who can’t actually see the line? 
    • Frank: On the other hand, if institutionalized slavery still existed, factories would be looking at around $7,500 in annual costs for housing, food and healthcare per “worker”.
    • Baron Schwartz: If anyone thought that NoSQL was just a flare-up and it’s died down now, they were wrong...In my opinion, three important areas where markets aren’t being satisfied by relational technologies are relational and SQL backwardness, time series, and streaming data. 
    • CJefferson: The problem is, people tell me that if I just learn Haskell, Idris, Closure, Coffescript, Rust, C++17, C#, F#, Swift, D, Lua, Scala, Ruby, Python, Lisp, Scheme, Julia, Emacs Lisp, Vimscript, Smalltalk, Tcl, Verilog, Perl, Go... then I'll finally find 'programming nirvana'.
    • @spectatorindex: Scientists had to delete Urban Dictionary's data from the memory of IBM's Watson, because it was learning to swear in its answers.
    • Animats: [Homomorphically Encrypted Deep Learning] is a way for someone to run a trained network on their own machine without being able to extract the parameters of the network. That's DRM.
    • Dino Dai Zovi: Attackers will take the least cost path through an attack graph from their start node to their goal node.
    • @hshaban: JUST IN: Senate votes to repeal web privacy rules, allowing broadband providers to sell customer data w/o consent including browsing history
    • KBZX5000: The biggest problem you face, as a student, when taking a programming course at a University level, is that the commercially applicable part of it is very limited in scope.
      You tend to become decent at writhing algorithms. A somewhat dubious skill, unless you are extremely gifted in mathematics and / or somehow have access to current or unique hardware IP's (IP as in Intellectual Property).
    • Brian Bailey: The increase in complexity of the power delivery network (PDN) is starting to outpace increases in functional complexity, adding to the already escalating costs of modern chips. With no signs of slowdown, designers have to ensure that overdesign and margining do not eat up all of the profit margin.
    • rbanffy: Those old enough will remember the AS/400 (now called iSeries) computers map all storage to a single address space. You had no disk - you had just an address space that encompassed everything and an OS that dealt with that.
    • @disruptivedean: Biggest source of latency in mobile networks isn't milliseconds in core, it's months or years to get new cell sites / coverage installed
    • Greg Ferro: Why Is 40G Ethernet Obsolete? Short Answer: COST. The primary issue is that 40G Ethernet uses 4x10G signalling lanes. On UTP, 40G uses 4 pairs at 10G each. 
    • @adriaanm: "We chose Scala as the language because we wanted the latest features of Spark, as well as [...] types, closures, immutability [...]"Adriaan Moors added,
    • ajamesm: There's a difference between (A) locking (waiting, really) on access to a critical section (where you spinlock, yield your thread, etc.) and (B) locking the processor to safely execute a synchronization primitive (mutexes/semaphores).
    • @evan2645: "Chaos doesn't cause problems, it reveals them" - @nora_js #SREcon17Americas #SRECon17
    • chrissnell: We've been running large ES clusters here at Revinate for about four years now. I've found the sweet spot to be about 14-16 data nodes, plus three master-only nodes. Right now, we're running them under OpenStack on top of our own bare metal with SAS disks. It works well but I have been working on a plan to migrate them to live under Kubernetes like the rest of our infrastructure. I think the answer is to put them in StatefulSets with local hostPath volumes on SSD.
    • @beaucronin: Major recurring theme of deep learning twitter is how even those 100% dedicated to the field can't keep up with progress.
    • Chris McNab: VPN certificates and keys are often found within and lifted from email, ticketing, and chat services.
    • @bodil: And it took two hours where the Rust version has taken three days and I'm still not sure it works.
    • azirbel: One thing that's generalizable (though maybe obvious) is to explicitly define the SLAs for each microservice. There were a few weeks where we gave ourselves paging errors every time a smaller service had a deploy or went down due to unimportant errors.
    • bigzen: I'm worn out on articles dissing the performance of SQL databases without quoting any hard numbers and then proceeding to replace the systems with no thanks of development in the latest and great tech. I have nothing against spark, but I find it very hard to believe that alarm code is now readable than SQL. In fact, my experience is just the opposite.
    • jhgg: We are experimenting with webworkers to power a very complicated autocomplete and scoring system in our client. So far so good. We're able to keep the UI running at 60fps while we match, score and sort results in a web-worker.
    • DoubleGlazing: NoSQL doesn't reduce development effort. What you gain from not having to worry about modifying schemas and enforcing referential integrity, you lose from having to add more code to your app to check that a DB document has a certain value. In essence you are moving responsibility for data integrity away from the DB and in to your app, something I think is quite dangerous.
    • Const-me: Too bad many computer scientists who write books about those algorithms prefer to view RAM in an old-fashioned way, as fast and byte-addressable.
    • Azur: It always annoys me a bit when tardigrades are described as extremely hardy: they are not. It is ONLY in the desiccated, cryptobiotic, form they are resistant to adverse conditions.
    • rebootthesystem: Hardware engineers can design FPGA-based hardware optimized for ML. A second set of engineers then uses these boards/FPGA's just as they would GPU's. They write code in whatever language to use them as ML co-processors. This second group doesn't have to be composed of hardware engineers. Today someone using a GPU doesn't have to be a hardware engineer who knows how to design a GPU. Same thing.

  • There should be some sort of Metcalfe's law for events. Maybe: the value of a platform is proportional to the square of the number of scriptable events emitted by unconnected services in the system. CloudWatch Events Now Supports AWS Step Functions as a Target@ben11kehoe: This is *really* useful: Automate your incident response processes with bulletproof state machines #aws

  • Cute faux O'Reilly book cover. Solving Imaginary Scaling Issues.

  • Intel's Optane SSD is finally out, though not quite meeting it's initial this will change everything promise, it still might change a lot of things. Intel’s first Optane SSD: 375GB that you can also use as RAM. 10x DRAM latency. 1/1000 NAND latency. 2400MB/s read, 2000MB/s write. 30 full-drive writes per day. 2.5x better density. $4/GB (1/2 RAM cost). 1.5TB capacity. 500k mixed random IOPS. Great random write response. Targeted at power users with big files, like databases. NDAs are still in place so there's more to learn later. PCPerspective: comparing a server with 768GB of DRAM to one with 128GB of DRAM combined with a pair of P4800X's, 80% of the transactions per second were possible (with 1/6th of the DRAM). More impressive was that matrix multiplication of the data saw a 1.1x *increase* in performance. This seems impossible, as Optane is still slower than DRAM, but the key here was that in the case of the DRAM-only configuration, half of the database was hanging off of the 'wrong' CPU.  foboz1: For anyone think that this a solution looking for a problem, think about two things: Big Data and mobile/embedded. Big Data has an endless appetite for large quantities for memory and fast storage; 3D XPoint plays into the memory hierarchy nicely. At the extreme other end of the scale, it may be fast enough to obviate the need for having DRAM+NAND in some applications. raxx7: And 3D XPoint isn't free of limitations yet. RAM has 50-100 ns latency, 50 GB/s bandwidth (128 bit interface) and unlimited write endurance. If 3D XPoint NVDIMM can't deliver this, we'll still need to manage the difference between RAM and 3D XPoint NVDIMM. zogus: The real breakthrough will come, I think, when the OS and applications are re-written so that they no longer assume that a computer's memory consists of a small, fast RAM bank and a huge, slow persistent set of storage--a model that had held true since just about forever. VertexMaster: Given that DRAM is currently an order of magnitude faster (and several orders vs this real-world x-point product) I really have a hard time seeing where this fits in. sologoub: we built a system using Druid as the primary store of reporting data. The setup worked amazingly well with the size/cardinality of the data we had, but was constantly bottlenecked at paging segments in and out of RAM. Economically, we just couldn't justify a system with RAM big enough to hold the primary dataset...I don't have access to the original planning calculations anymore, but 375GB at $1520 would definitely have been a game changer in terms of performance/$, and I suspect be good enough to make the end user feel like the entire dataset was in memory.

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Friday
Mar172017

Stuff The Internet Says On Scalability For March 17th, 2017

Hey, it's HighScalability time:

 

Can it be a coincidence trapping autonomous cars is exactly how demons are trapped on Supernatural?

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  • billion billion: exascale operations per second; 250ms: connection time saved by zero round trip time resumption; 800 Million: tons of prey eaten by spiders; 90%: accuracy of quantum computer recognizing trees; 80 GB/s: S3 across 2800 simultaneous functions;

  • Quotable Quotes:
    • @GossiTheDog: Here's something to add to your security threat model: backups. Why steal live data and when you can drive away with exact replica?
    • @ThePublicSquare: "California produces 160% of its 1990 manufacturing, but with just 60% of the workers." -@uclaanderson economist Jerry Nickelsburg
    • @rbranson: makes total sense. I have a friend (who is VC-backed) that has stuff in Azure, GCloud, and AWS to maximize the free credits.
    • @AndrewYNg: If not for US govt funding (DARPA, NSF), US wouldn't be an AI leader today. Proposed cuts to science is big step in wrong direction.
    • @CodeWisdom: "To understand a program you must become both the machine and the program." - Alan Perlis 
    • @codemanship: What does it take to achieve Continuous Delivery? 1. Continuous testing. e.g., Google have 4.2M automated tests, run avg of 35x a day
    • @sebastianstadil: Azure Storage services are down. They really are doing everything like AWS. 😂
    • Mehta: A fundamental belief in neuroscience has been that neurons are digital devices. They either generate a spike or not. These results show that the dendrites do not behave purely like a digital device. Dendrites do generate digital, all-or-none spikes, but they also show large analogue fluctuations that are not all or none. 
    • @jasongorman: Shocked faces after I explain to a room of hipsters that a build script is basically just a batch file. Y'know? Like in the old days
    • William Woody: The problem is that our industry, unlike every other single industry except acting and modeling (and note neither are known for “intelligence”) worship at the altar of youth. I don’t know the number of people I’ve encountered who tell me that by being older, my experience is worthless since all the stuff I’ve learned has become obsolete.
    • @DavidBrin: Now even your sex toys are spying on you...
    • Counterintuitive things about testing: #6: service-oriented-architecture would be the worst thing you could possibly do.
    • industry7: Don't batch you changes together in a single branch. Each change goes in it's own feature branch, and each feature can be individually rapid fired through the pipeline. Conversely, if all your changes are in the same branch, you can't deploy them individually with docker anyway.
    • Mike Elgan: In other words, A.I. will use data on social networks to rank people based on how much they can be trusted. The worst part is that this trust-judging process happens invisibly behind the scenes. When you don't get that job or loan, you'll never know why.
    • @viktorklang: Most processors control execution by tracking completion dependencies, using the same techniques seen when programming CompletableFutures
    • @iamdevloper: Every functional programming tutorial... [picture of drawing an owl using two simple circles then showing a completely finished beautiful owl with no intermediate steps explained]
    • @PatrickMcFadin: Actual advice from an AWS Solution Architect - Don’t run active-active over multiple regions. AZs should be enough for availability. #lolwut
    • RightScale: We also compared the Google 3-year Committed Use Discount to the AWS 3-year Convertible RI. The total cost of the Google environment was 35 percent less than AWS.
    • @kelseyhightower: The container image is just a packaging concept; think of them as the price of admission to modern platforms such as Kubernetes.
    • Uber: The biggest problem we face is that most rules are effective for several weeks; then fraudsters adapt, and rules end up with more false positives.
    • David Rosenthal: Yet again the DNA enthusiasts are waving the irrelevant absolute cost decrease in reading to divert attention from the relevant lack of relative cost decrease in writing. They need an improvement in relative write cost of at least 6 orders of magnitude. To do that in a decade means halving the relative cost every year, not increasing the relative cost by 10-15% every year.
    • @jakub_zalas: Law of code reviews: feedback is inversely proportional to the size of merge request
    • David Rosenthal: There is no way to greatly improve Web archiving without significantly increased resources. Library and archive budgets have been under sustained attack for years. Neither I nor Leetaru has any idea where an extra $30-50M/yr would come from. Much less isn't going to stop the rot.
    • @whispersystems: Ubiquitous e2e encryption is pushing intelligence agencies from undetectable mass surveillance to expensive, high-risk, targeted attacks
    • @b6n: the year is 2217, we have survived global warming and the water riots. ORMs are still a shitshow.
    • Google: Then there’s our improved Free Tier. First, we’ve extended the free trial from 60 days to 12 months, allowing you to use your $300 credit across all GCP services and APIs, at your own pace and on your own schedule.
    • @JoeEmison: But once an organization is buying, none of these services are fungible enough where the price difference is more than switching costs.
    • Pascal Bestebroer: What ever it is that’s holding you back on covering all platforms, I promise you the work involved to fix that is far less than creating a new game.
    • Quantum Gravity Research: We view consciousness as both emergent and fundamental. In its fundamental form, consciousness exists inside every tetrahedron/pixel in the 3D quasicrystal in the form of something we call viewing vectors. 
    • @hichaelmart: Essentially AppEngine Flexible requires you to specify an auto-scaling group
    • Segment: Because outsourcing infrastructure is so damn easy (RDS, Redshift, S3, etc), it’s easy to fall into a cycle where the first response to any problem is to spend more money.

  • Here's how Segment saved $1 million per year on their AWS bill in three months. Their detective efforts are interesting and detailed. Lots to learn from. It probably should not be a surprise that AWS doesn't make it easy to figure out where there are opportunities to save money. Process: scrutinize every single resource in your bill line-by-line; enable AWS Detailed billing; import the raw log file into Redshift (which ironically costs money); deep analysis netted a list of the top ~15 problem areas, which totaled up to around 40% of the monthly bill. Sources:  hundreds of large EBS drives, over-provisioned cache and RDS instances; DynamoDB hot shards ($300,000 annually); Service auto-scaling ($60,000 annually); Bin-packing and consolidating instance types ($240,000 annually). It takes engineering effort to decide if these costs are necessary or if there's a way to make changes to bring down costs.  Fixes: better DynamoDB partition key selection; better auto-scaling; move to bigger instances and pack 100-200 containers per instance. Lesson: most important investment is to prevent problems from occurring in the first place.

Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...

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