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BiteSpeed is a chat-based startup that focuses on helping e-commerce brands.

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BiteSpeed | 56,981 followers on LinkedIn. Chat-based commerce for E-commerce brands | We are a seed funded SaaS startup helping e-commerce brands on Shopify move away from boring, impersonal marketing emails and sell on chat apps like FB Messenger &amp; Whatsapp.

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BiteSpeed - Online platform offering Whatapp marketing for Shopify businesses. Raised a total funding of $2.17M over 2 rounds from 33 investors. Valued at $7.78M. Founded by Vinayak Aggarwal, Abhinav Tuli and 2 others in the year 2019. BiteSpeed has 519 competitors.

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&quot;Gurugram, Haryana based BiteSpeed is a a seed funded SaaS startup helping e-commerce brands on Shopify move away from boring, impersonal marketing emails and sell on chat apps like FB Messenger and WhatsApp. Founded in 2019 by Vinayak, They are hard at work helping e-commerce brands on Shopify ...

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We are a bunch of BITS Pilani graduates that started BiteSpeed out of our dorm rooms. When we started we hardly knew much about running a company, in fact most of us hadn’t written a line of code in our lives.

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When we think of debugging, we think of breakpoints in IDEs, stepping over, inspecting variables, etc. However, there are instances where stepping outside the conventional confines of an IDE becomes essential to track and resolve complex issues. This is where tools like DTrace come into play, offering a more nuanced and powerful approach to debugging than traditional methods. This blog post delves into the intricacies of DTrace, an innovative tool that has reshaped the landscape of and system analysis. debugging https://youtu.be/3M0AhZnVoUk?embedable=true As a side note, if you like the content of this and the other posts in this series, check out my that covers this subject. If you have friends who are learning to code, I'd appreciate a reference to my . If you want to get back to Java after a while, check out my Debugging book Java Basics book Java 8 to 21 book . DTrace Overview DTrace was first introduced by Sun Microsystems in 2004. DTrace quickly garnered attention for its groundbreaking approach to dynamic system tracing. Originally developed for Solaris, it has since been ported to various platforms, including MacOS, Windows, and Linux. DTrace stands out as a dynamic tracing framework that enables deep inspection of live systems – from operating systems to running applications. Its capacity to provide real-time insights into system and application behavior without significant performance degradation marks it as a revolutionary tool in the domain of system diagnostics and debugging. Understanding DTrace’s Capabilities DTrace, short for Dynamic Tracing, is a comprehensive toolkit for real-time system monitoring and debugging, offering an array of capabilities that span across different levels of system operation. Its versatility lies in its ability to provide insights into both high-level system performance and detailed process-level activities. System Monitoring and Analysis At its core, DTrace excels in monitoring various system-level operations. It can trace system calls, file system activities, and network operations. This enables developers and system administrators to observe the interactions between the operating system and the applications running on it. For instance, DTrace can identify which files a process accesses, monitor network requests, and even trace system calls to provide a detailed view of what's happening within the system. Process and Performance Analysis Beyond system-level monitoring, DTrace is particularly adept at dissecting individual processes. It can provide detailed information about process execution, including CPU and memory usage, helping to pinpoint performance bottlenecks or memory leaks. This granular level of detail is invaluable for performance tuning and debugging complex software issues. Customizability and Flexibility One of the most powerful aspects of DTrace is its customizability. With a scripting language based on C syntax, DTrace allows the creation of customized scripts to probe specific aspects of system behavior. This flexibility means that it can be adapted to a wide range of debugging scenarios, making it a versatile tool in a developer’s arsenal. Real-World Applications In practical terms, DTrace can be used to diagnose elusive performance issues, track down resource leaks, or understand complex interactions between different system components. For example, it can be used to determine the cause of a slow file operation, analyze the reasons behind a process crash, or understand the system impact of a new software deployment. Performance and Compatibility of DTrace A standout feature of DTrace is its ability to operate with remarkable efficiency. Despite its deep system integration, DTrace is designed to have minimal impact on overall system performance. This efficiency makes it a feasible tool for use in live production environments, where maintaining system stability and performance is crucial. Its non-intrusive nature allows developers and system administrators to conduct thorough debugging and performance analysis without the worry of significantly slowing down or disrupting the normal operation of the system. Cross-Platform Compatibility Originally developed for Solaris, DTrace has evolved into a cross-platform tool, with adaptations available for MacOS, Windows, and various . Each platform presents its own set of features and limitations. For instance, while DTrace is a native component in Solaris and MacOS, its implementation in Linux often requires a specialized build due to kernel support and licensing considerations. Linux distributions Compatibility Challenges on MacOS On MacOS, DTrace's functionality intersects with System Integrity Protection (SIP), a security feature designed to prevent potentially harmful actions. To utilize DTrace effectively, users may need to disable SIP, which should be done with caution. This process involves booting into recovery mode and executing specific commands, a step that highlights the need for a careful approach when working with such powerful system-level tools. We can disable SIP using the command: csrutil disable We can optionally use a more refined approach of enabling SIP without dtrace using the following command: csrutil enable --without dtrace Be extra careful when issuing these commands and when working on machines where dtrace is enabled. Back up your data properly! Customizability and Flexibility of DTrace A key feature that sets DTrace apart in the realm of system monitoring tools is its highly customizable nature. DTrace employs a scripting language that bears similarity to C syntax, offering users the ability to craft detailed and specific diagnostic scripts. This scripting capability allows for the creation of custom probes that can be fine-tuned to target particular aspects of system behavior, providing precise and relevant data. Adaptability to Various Scenarios The flexibility of DTrace's scripting language means it can adapt to a multitude of debugging scenarios. Whether it's tracking down memory leaks, analyzing CPU usage, or monitoring I/O operations, DTrace can be configured to provide insights tailored to the specific needs of the task. This adaptability makes it an invaluable tool for both developers and system administrators who require a dynamic approach to problem-solving. Examples of Customizable Probes Users can define probes to monitor specific system events, track the behavior of certain processes, or gather data on system resource usage. This level of customization ensures that DTrace can be an effective tool in a variety of contexts, from routine maintenance to complex troubleshooting tasks. Following in a simple hello world dtrace probe: sudo dtrace -qn 'syscall::write:entry, syscall::sendto:entry /pid == $target/ { printf("(%d) %s %s", pid, probefunc, copyinstr(arg1)); }' -p 9999 The kernel is instrumented with hooks that match various callbacks. dtrace connects to these hooks and can perform interesting tasks when these hooks are triggered. They have a naming convention, specially: . In this case the provider is a system call in both cases. We have no module, so we can leave that part blank between the colon ( ) symbols. We grab a write operation and entries. When an application writes or tries to send a packet, the following code event will trigger. provider:module:function:name : sendto These things happen frequently, which is why we restrict the process ID to the specific target with . This means the code will only trigger for the PID passed to us in the command line. The rest of the code should be simple for anyone with basic C experience; it's a printf that would list the processes and the data passed. pid == $target Real-World Applications of DTrace DTrace's diverse capabilities extend far beyond theoretical use, playing a pivotal role in resolving real-world system complexities. Its ability to provide deep insights into system operations makes it an indispensable tool in a variety of practical applications. To get a sense of how dtrace can be used, we can use the command whose output on my Mac is below: man -k dtrace bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
cpuwalk.d(1m)            - Measure which CPUs a process runs on. Uses DTrace
creatbyproc.d(1m)        - snoop creat()s by process name. Uses DTrace
dappprof(1m)             - profile user and lib function usage. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
dispqlen.d(1m)           - dispatcher queue length by CPU. Uses DTrace
dtrace(1)                - dynamic tracing compiler and tracing utility
dtruss(1m)               - process syscall details. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
execsnoop(1m)            - snoop new process execution. Uses DTrace
fddist(1m)               - file descriptor usage distributions. Uses DTrace
filebyproc.d(1m)         - snoop opens by process name. Uses DTrace
hotspot.d(1m)            - print disk event by location. Uses DTrace
iofile.d(1m)             - I/O wait time by file and process. Uses DTrace
iofileb.d(1m)            - I/O bytes by file and process. Uses DTrace
iopattern(1m)            - print disk I/O pattern. Uses DTrace
iopending(1m)            - plot number of pending disk events. Uses DTrace
iosnoop(1m)              - snoop I/O events as they occur. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
kill.d(1m)               - snoop process signals as they occur. Uses DTrace
lastwords(1m)            - print syscalls before exit. Uses DTrace
loads.d(1m)              - print load averages. Uses DTrace
newproc.d(1m)            - snoop new processes. Uses DTrace
opensnoop(1m)            - snoop file opens as they occur. Uses DTrace
pathopens.d(1m)          - full pathnames opened ok count. Uses DTrace
perldtrace(1)            - Perl's support for DTrace
pidpersec.d(1m)          - print new PIDs per sec. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks
priclass.d(1m)           - priority distribution by scheduling class. Uses DTrace
pridist.d(1m)            - process priority distribution. Uses DTrace
procsystime(1m)          - analyse system call times. Uses DTrace
rwbypid.d(1m)            - read/write calls by PID. Uses DTrace
rwbytype.d(1m)           - read/write bytes by vnode type. Uses DTrace
rwsnoop(1m)              - snoop read/write events. Uses DTrace
sampleproc(1m)           - sample processes on the CPUs. Uses DTrace
seeksize.d(1m)           - print disk event seek report. Uses DTrace
setuids.d(1m)            - snoop setuid calls as they occur. Uses DTrace
sigdist.d(1m)            - signal distribution by process. Uses DTrace
syscallbypid.d(1m)       - syscalls by process ID. Uses DTrace
syscallbyproc.d(1m)      - syscalls by process name. Uses DTrace
syscallbysysc.d(1m)      - syscalls by syscall. Uses DTrace
topsyscall(1m)           - top syscalls by syscall name. Uses DTrace
topsysproc(1m)           - top syscalls by process name. Uses DTrace
Tcl_CommandTraceInfo(3tcl), Tcl_TraceCommand(3tcl), Tcl_UntraceCommand(3tcl) - monitor renames and deletes of a command
bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
cpuwalk.d(1m)            - Measure which CPUs a process runs on. Uses DTrace
creatbyproc.d(1m)        - snoop creat()s by process name. Uses DTrace
dappprof(1m)             - profile user and lib function usage. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
dispqlen.d(1m)           - dispatcher queue length by CPU. Uses DTrace
dtrace(1)                - dynamic tracing compiler and tracing utility
dtruss(1m)               - process syscall details. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
execsnoop(1m)            - snoop new process execution. Uses DTrace
fddist(1m)               - file descriptor usage distributions. Uses DTrace
filebyproc.d(1m)         - snoop opens by process name. Uses DTrace
hotspot.d(1m)            - print disk event by location. Uses DTrace
iofile.d(1m)             - I/O wait time by file and process. Uses DTrace
iofileb.d(1m)            - I/O bytes by file and process. Uses DTrace
iopattern(1m)            - print disk I/O pattern. Uses DTrace
iopending(1m)            - plot number of pending disk events. Uses DTrace
iosnoop(1m)              - snoop I/O events as they occur. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
kill.d(1m)               - snoop process signals as they occur. Uses DTrace
lastwords(1m)            - print syscalls before exit. Uses DTrace
loads.d(1m)              - print load averages. Uses DTrace
newproc.d(1m)            - snoop new processes. Uses DTrace
opensnoop(1m)            - snoop file opens as they occur. Uses DTrace
pathopens.d(1m)          - full pathnames opened ok count. Uses DTrace
perldtrace(1)            - Perl's support for DTrace
pidpersec.d(1m)          - print new PIDs per sec. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks
priclass.d(1m)           - priority distribution by scheduling class. Uses DTrace
pridist.d(1m)            - process priority distribution. Uses DTrace
procsystime(1m)          - analyse system call times. Uses DTrace
rwbypid.d(1m)            - read/write calls by PID. Uses DTrace
rwbytype.d(1m)           - read/write bytes by vnode type. Uses DTrace
rwsnoop(1m)              - snoop read/write events. Uses DTrace
sampleproc(1m)           - sample processes on the CPUs. Uses DTrace
seeksize.d(1m)           - print disk event seek report. Uses DTrace
setuids.d(1m)            - snoop setuid calls as they occur. Uses DTrace
sigdist.d(1m)            - signal distribution by process. Uses DTrace
syscallbypid.d(1m)       - syscalls by process ID. Uses DTrace
syscallbyproc.d(1m)      - syscalls by process name. Uses DTrace
syscallbysysc.d(1m)      - syscalls by syscall. Uses DTrace
topsyscall(1m)           - top syscalls by syscall name. Uses DTrace
topsysproc(1m)           - top syscalls by process name. Uses DTrace There's a lot here; we don't need to read everything. The point is that when you run into a problem, you can just search through this list and find a tool dedicated to debugging that problem. Let’s say you're facing elevated disk write issues that are causing the performance of your application to degrade... But is it your app at fault or some other app? rwbypid.d can help you with that. It can generate a list of processes and the number of calls they have for read/write based on the process ID, as seen in the following screenshot: We can use this information to better understand IO issues in our code or even in 3rd party applications/libraries. is another tool that helps us track IO operations but with more details: iosnoop In diagnosing elusive system issues, DTrace shines by enabling detailed observation of system calls, file operations, and network activities. For instance, it can be used to uncover the root cause of unexpected system behaviors or to trace the origin of security breaches, offering a level of detail that is often unattainable with other debugging tools. Performance optimization is the main area where DTrace demonstrates its strengths. It allows administrators and developers to pinpoint performance bottlenecks, whether they lie in application code, system calls, or hardware interactions. By providing real-time data on resource usage, DTrace helps in fine-tuning systems for optimal performance. Final Words In conclusion, DTrace stands as a powerful and versatile tool in the realm of system monitoring and debugging. We've explored its broad capabilities, from in-depth system analysis to individual process tracing and its remarkable performance efficiency that allows for its use in live environments. Its cross-platform compatibility, coupled with the challenges and solutions specific to MacOS, highlights its widespread applicability. The customizability through scripting provides unmatched flexibility, adapting to a myriad of diagnostic needs. Real-world applications of DTrace in diagnosing system issues and optimizing performance underscore its practical value. DTrace's comprehensive toolkit offers an unparalleled window into the inner workings of systems, making it an invaluable asset for system administrators and developers alike. Whether it's for routine troubleshooting or complex performance tuning, DTrace provides insights and solutions that are essential in the modern computing landscape. Also published . here

Advanced Debugging Techniques: Revisiting DTrace for Troubleshooting

Often when debugging, we need to step outside of the comforting embrace of the IDE to reproduce or track an issue. In this series, I’d like to go over some tools you might find useful for these cases. I’ll try to limit myself to tools that are 100% debugging tools and not those that are useful for development testing. E.g. tools like or are remarkably useful. You can/should use them while debugging. But you probably used them while building and testing the feature. So you would already be familiar with them and should have some sense of what they do. I want to focus on tools that you would mostly reach for when debugging. In that sense, tools like etc. also make no sense here. curl jq SDKMan I also want to avoid tools like database tools. They are remarkably useful when debugging, but again, you’d probably use them during development, too. They’re also very vendor specific, so it’s a vast subject that’s too broad to cover here. The tools I'll cover in this series encompass the following categories: System monitoring tools - as is the case with strace and dtrace, which we'll discuss today Network monitors - also fits in the above bracket but warrants a category of its own VM/Runtime monitoring - e.g. tools that let us inspect the JVM, etc. Profilers and memory monitors In this first post I'd like to discuss two heavyweight champions: dtrace and strace. If you're a Java developer or a Windows user, there's a good chance you never heard of these tools. You might have inadvertently used one of them since so many tools were built on top of them, but that's probably not the case. Both tools will let you debug anything without the source code. You could detect problems and gain a level of understanding you never imagined. Dtrace Back in 2004, I first heard about DTrace while working at Sun Microsystems. It became all the rage in the hallways as it was an innovation that Sun Microsystems was promoting. Dtrace was ported later into MacOS X (it originated on Solaris). Today there are ports on Windows and Linux as well. Dtrace is a powerful low-level dynamic tracing framework. But that's just another superlative and if you never used such a tool and don't have a background in systems programming, you might be feeling a bit confused: what the hell does it do? It lets you "see" everything. Want to know which files a process has opened? OK Want to know who invokes a kernel API and get a stack trace to the caller? OK Want to know why a process dies? OK Want to know how much CPU Time is spent on an operation? OK You might think that dtrace is one of those tools that will destroy your CPU completely... But here's the killer feature: it's fast enough to run in production with minimal to no performance impact! It was revolutionary when it was launched almost two decades ago and it's still the case to this day! Running Dtrace Before we begin, a word of warning. Save your data! This tool can easily crash your machine and enabling it requires disabling important security facilities on MacOS. This is a risky "low level" system service and should be treated as such... On a Mac dtrace conflicts with "System Integrity Protection" which is a security feature that blocks some interactions between processes (among other things). Under normal circumstances, this would be great. But if you want to run dtrace this would be a problem. The solution is booting to recovery mode on Intel Macs; this means holding the keys on boot. On an ARM mac just long-press the power button. Command-R Then, in a recovery mode terminal, issue the command: . csrutil disable Then upon reboot, dtrace should work as expected. Basic Usage As mentioned before, dtrace is a very powerful tool. There are whole books written about it. It has its own programming language based on C syntax that you can use to build elaborate logic. E.g. the following command will log some information from the given callbacks: sudo dtrace -qn 'syscall::write:entry, syscall::sendto:entry /pid == $target/ { printf("(%d) %s %s", pid, probefunc, copyinstr(arg1)); }' -p [PID] The code snippet passed to the dtrace command listens to the sendto callback on the target process ID. Then prints out the information to the console, e.g.: (pid) text If this seems like a bit too much and too hard to get started with... You're 100% right. It's a powerful tool when you need it. But for most of our day-to-day usage, it's just too powerful. What we want is to know a bit of basic stuff and this is just too much! Simple Usage As luck would have it, we have a simple solution: man -k dtrace This prints out a list of tools that's worth reading just to get a sense of how extensive this thing is. Here are a few interesting lines of output from that command: bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks It's worth your time going over this list to realize what you can truly do here. Examples You're facing elevated disk write issues that are causing the performance of your application to degrade... But is it your app at fault or some other app? Just run: sudo rwbypid.d It will print out the reads/writes to the disk: PID CMD                       DIR    COUNT
  2957 wordexp-helper              W        1
  2959 wc                          W        1
  2961 grep                        W        1

... snipped for clarity ...

   637 firefox                     R     6937
   637 firefox                     W    15325
   343 sentineld                   W   100287 The security software is really holding performance down... You can also use to get more specific results on the number of bytes written/distribution. bitesize.d That's pretty high level though. What if you want specifics: file name, process name, etc? sudo iosnoop -a Prints out output that includes pretty much everything you would need: STRTIME              DEVICE  MAJ MIN   UID   PID D      BLOCK     SIZE                     PATHNAME ARGS
2022 Jun 30 12:16:56 ??        1  17   501  1111 W  150777072     4096 ??/idb/3166453069wcaw.sqlite-wal firefox\0
2022 Jun 30 12:16:56 ??        1  17   501   661 W  150777175   487424  ??/index-dir/the-real-index Slack Helper\0
2022 Jun 30 12:16:57 ??        1  17   499   342 W  150777294     4096 ??/persistent/.dat.nosync0156.ztvXap sentineld\0 I can see the process id and how many bytes it wrote to the specific file! Let's say your program spans processes and you want to see what's going on. E.g. I run a source code build in a server I built: sudo errinfo Lets me detect the error returned from system calls to and the command that originally triggered that: EXEC          SYSCALL  ERR  DESC
    WindowServer workq_kernreturn   -2 
    WindowServer workq_kernreturn   -2 
   SentinelAgent workq_kernreturn   -2 
   SentinelAgent workq_kernreturn   -2 
          Signal           Helper    0 
          Google           Chrome    0 
           Brave          Browser    0 
          Google           Chrome    0 These are just the tip of the iceberg. I suggest checking out this old from Oracle or . Disclaimer: I didn't read the book... dtrace tutorial the book strace Amusingly enough, the strace tool also originated at Sun Microsystems in this case in the 90s. This isn't surprising though as the list of technologies originating from Sun Microsystems is absolutely mind-numbing. Strace is much simpler than dtrace in both usage and capabilities. For better and for worse. Since dtrace requires deep OS support, it never became an official feature of common Linux distributions and as a result, on Linux people use strace instead of dtrace. They aren't exactly interchangeable, though. Strace is enabled thanks to the kernel feature known as ptrace. Since ptrace is already in Linux, we don't need to add additional kernel code or modules. Typically, dtrace requires deeper kernel support, to get around licensing issues on Linux it's in a separate loadable module but that still presents some challenges. Working with strace is akin to printing a log entry every time we make a kernel call. This creates very verbose logging for every command you execute. As a result, you can follow what's going on under the hood of a running process. really Running Strace Nowadays, strace is commonly used in Linux, it's my favorite system diagnostic tool on that platform. It's remarkably convenient to work with since we can run it with no special privileges. Notice that, unlike dtrace, you should keep strace away from production environments (unless the code is segregated). It carries a significant performance overhead and would bring a production system down. The most basic usage of strace is just passing the command line to it: strace java -classpath. PrimeMain The output of strace for this is pretty long, lets go over a few of the lines: execve("/home/ec2-user/jdk1.8.0_45/bin/java", ["java", "-classpath.", "PrimeMain"], 0x7fffd689ec20 /* 23 vars */) = 0
brk(NULL)                               = 0xb85000
mmap(NULL, 4096, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x7f0294272000
readlink("/proc/self/exe", "/home/ec2-user/jdk1.8.0_45/bin/j"..., 4096) = 35
access("/etc/ld.so.preload", R_OK)      = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
stat("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/x86_64", 0x7fff37af09a0) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
stat("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls", 0x7fff37af09a0) = -1 ENOENT (No such file or directory) Every one of these lines is a Linux system call. We can google each one of them to get a sense of what's going on. Here's a simple example: open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory) Java tries to load the library from the directory using a system open call to load the file. The exit code of the system call is , which means that the file isn't there. Under normal circumstances, we should get back a file descriptor value from this API. Looking in the directory, it seems the directory is missing. I'm guessing that this is because of a missing JCE installation. This is probably OK but might have been interesting in some cases. pthread tls -1 tls Obviously, the amount of output is overwhelming sometimes. We usually just want to see things like "which file was opened" and "what's going on with our network calls". We can easily accomplish that by only looking at specific system calls using the argument. -e strace -e open java -classpath . PrimeMain Will only show the open system calls: open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/tls/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/tls/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/etc/ld.so.cache", O_RDONLY|O_CLOEXEC) = 3
open("/lib64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = 3
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/libjli.so", O_RDONLY|O_CLOEXEC) = 3
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/libdl.so.2", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory) There are many system calls you can learn and use to track many behaviors such as this: connect, write, etc. This is only the tip of the iceberg of what you can do using strace. Julia Evans wrote some of the most . If you want to learn more about it, there's probably no better place (also check out her other stuff... Amazing resources!). exhaustive and entertaining posts on strace Strace and Java As you saw before, strace works great with the JVM. Since strace predates Java and is a very low-level tool, it has no awareness of the JVM. The JVM works like most other platforms and invokes system calls which you can use to debug its behavior. However, because of its unique approach to some problems, some aspects might not be as visible with strace. A good example is allocations. System tools use malloc, which maps to kernel allocation logic, but Java takes a different route. It manages its own memory for efficiency and easier garbage collection logic. As a result, some aspects of memory allocation will be hidden from strace output. This can be a blessing in disguise, as the output can be overwhelming sometimes. At the time of this writing, threading works well with strace. But this might not be the case moving forward as might change the one-to-one mapping between Java threads and system threads. This might make strace output harder to pinpoint in heavily threaded applications. project Loom Finally There's an alphabet soup of "*trace" utilities in various forms that keep borrowing ideas from one another. It's a significant challenge to keep up with all of that noise. There are just too many great tools to cover, I would like to discuss btrace in a future installment though. It's very similar to dtrace but also very JVM specific so probably worth a different post. The tools I discussed today take different approaches to solve similar problems: how do we understand what a binary application "really" does? Security researchers and hackers use these tools to understand your program. They don't need the code and don't need a disassembly to see what you're actually doing. You can also use these tools to understand the impact of your actions. We often invoke an API and let things end there. But the devil is in the details and those details can carry a heavy toll. As a Java developer, I rarely think about signal delivery, process management or such low-level staples. But I do spend time looking at these things since they ultimately impact the stability and performance of my application. Also Published Here

Using External Debugging Tools dtrace and strace

How to increase your website traffic with competitor analysis

What’s “this”? In the simplest of terms, the JavaScript keyword refers to the object it belongs to on runtime, depending upon its (where it is called). this call-site However, understanding what it would refer to in any given context, requires a slightly deeper understanding of some relevant concepts, which will be covered in this article. Just to start with, can have the following values depending upon where it is accessed : this : refers to the global object. 1. By default this : refers to the global object. In mode, however, will be . 2. Inside a function this strict this undefined : refers to the owner object. (A method is a function that belongs inside an object. In other words, it’s a function that’s an object’s property.) 3. Inside a method this : refers to the element on which the event was triggered. 4. In an event this : refers to the global object. In strict mode, will be , just like any other function in a global context. 5. Inside an Immediately Invoked Function Expression (IIFE) this this undefined : When a fat arrow is used to define a function, it doesn’t create a new value for , instead, it keeps referring to the same object it was referring to outside of the function. 6. Inside a Fat-Arrow function ()=> this This article hopes to give you an understanding of how these values are assigned to this, and how this knowledge can be utilized to suit our requirements. Call-Site and Call-Stack As discussed in the last section, we got to know that this is a made for each function invocation, which entirely depends upon where exactly it was called. runtime-binding This location in the code where the concerned function was called, is called the . An understanding of determining the is crucial towards understanding what this would be bound to, at any given point of the execution. call-site call-site While finding the is generally as simple as locating where a function was called from, it might not always be that clear because of certain coding patterns that might obscure it. call-site Hence, it’s important to think about the , the stack of functions that have been called to get us to the current stage of the execution which we’re concerned with. call-stack Let us take a simple example to illustrate how a and could be determined. call-stack call-site By following the chain of function calls in order, you can determine the call-stack and call-sites. * Tip for determining call-stack Utilize the built-in JS provided with any modern browser’s developer tools. debugger In the execution of any JS code, you can set a breakpoint by using the keyword to stop the execution at that point in the browser. debugger , Let’s say, we add a breakpoint when was called. thunderbolt() The stops the execution at the custom breakpoint, and the function at that point can be viewed on the right side. debugger call-stack In the image above, we can see that the execution was stopped at the point where we mentioned the keyword, as soon as is called. At this point, we will not observe any execution of code that comes after the (just the , in this case). debugger thunderbolt() debugger thunderbolt() log Our primary point of interest right now, is the which is clearly illustrated on the right-hand side, same as we determined in the example above. at the bottom of the stack, refers to the initial global call to call-stack (anonymous) choosePikachu() . Binding rules for “this” Now that we understand what a and a is, we can learn about how a determines what will hold during execution. call-site call-stack call-site this There are which apply. First, let’s understand them , and then, their when multiple rules can apply to the . four general rules independently order of precedence call-site 1. Default Binding This is the default catch-all rule, when none others apply. It comes from the most common case of a function invocation, which a standalone function call. Let’s look at the below example. The variable declared in global scope is the same as declaring a property on the global object of the same name. ultraBall Inside , the reference to this defaults to the global object. Hence, we would see the value of getting logged. getPokemon() this.ultraBall However, if mode is in effect globally or inside , the object is not permitted default binding. In that case, we will see the error . strict getPokemon global TypeError : 'this' is 'undefined' 2. Implicit Binding If the call-site has a context object (if a function is called through an owning or containing object, as its property), implicit binding applies. The rule states that, when there is a context object for a function reference, it’s object that should be used for its method calls’ binding. that this Let’s look at a few examples to illustrate the different cases which can arise. Since the object is the for the call, is synonymous to . pikachu this getBaseSpeed this.baseSpeed pikachu.baseSpeed Let’s look at another example to see how only the top or last level of an Object property reference chain matters to the call-site for implicit binding. this As we can see, the value is still . That’s because the call to is bound to its direct caller, , which serves as its binding. In this context, the value is . baseSpeed 90 getBaseSpeed pikachu this baseSpeed 90 Let’s look at a few more examples to show common cases where implicit binding can seem unexpected. In this example, we have lost our implicit binding to in case of assigning to a different variable . Now, for , this refers to the global object ( takes place). Hence, for the call, will be . this pikachu pikachu.getBaseSpeed baseSpeedFunction baseSpeedFunction default binding this.baseSpeed 50 Now, a more common and not-so-obvious way this loss of implicit binding can occur is when we pass a callback function. Consider the following example : Once again, inside the callback function executor , we are effectively passing a reference to . Upon execution, will be bound to the object again (or throw a , if mode is enabled), instead of . executeFunction pikachu.getBaseSpeedfunction this global TypeError strict pikachu It’s quite common for function callbacks to lose their binding. Another unexpected outcome can arise when the function we’ve passed our callback to, intentionally alters the for the call. For example, Event handlers in popular JavaScript libraries often modify to point to the that triggered the event. this this this DOM element You are not really in control of how your callback function reference will be executed. So far, you don’t have any way of controlling the call-site to assign the binding you intended. This is where comes into play. explicit binding 3. Explicit Binding To resolve the unintended loss of with implicit binding, we can explicitly set the value of to a given object for a function call. this this There are several in-built methods that can help us achieve like : explicit binding , The bind() method is a method of the property. This means can be used by every single function. bind() Function.prototype bind() The method creates a new function that, when called, has its keyword set to the provided value, with a given sequence of arguments preceding any provided when the new function is called. bind() this In other words, returns a new function that is hardcoded to call the original function with the context set as specified. bind() this The call() and apply() methods and are also methods of the property, with similar but slightly different usage. call() apply() Function.prototype The method calls a function with a given value and arguments provided individually. call() this Whereas, the method calls a function with a given value, and arguments provided as an array (or an ). apply() this array-like object Invoking with explicit binding by or allows us to force its to be the of function . Pokémon Pokémon.call() Pokémon.apply() this this PokémonExtension Also, a noteworthy aspect of the above example is that all instances of will bind their respective to the execution of within them. Such an explicit binding is also called PokémonExtension this Pokémon hard binding. 4. New Binding In JavaScript, there is really no such thing as “constructor functions”, but rather construction call of functions. When a function is invoked with new in front of it, otherwise known as a constructor call, the following things are done automatically. A brand new object is created (aka constructed) out of thin air. The newly constructed object is -linked. (Out of the scope of this article) [[Prototype]] The newly constructed object is set as the binding for that function call. this Unless the function returns its own alternate object, the new invoked function call will return the newly constructed object. automatically All binding rules in action It should be clear that the is the lowest priority rule of the four. default binding Let’s compare and with each other. implicit binding, explicit binding, new binding Implicit versus Explicit As we saw, the of with took precedence over its own as it did for the second case as well. explicit binding firstAttempt.catchPokémon secondAttempt implicit binding, Hence, is of higher precedence than . explicit binding implicit binding Implicit versus new So, is more precedent than . new binding implicit binding Explicit versus new? and or cannot be used together, so something like is not allowed to test directly against . But, we can still use a to test the precedence of the two. new call apply var fourthAttempt = new catchPokémon.call(firstAttempt); new binding explicit binding hard binding is hard-bound against , but did not change to , as we may have expected, but it remained . attemptBinder firstAttempt new attemptBinder(“Steelix”) firstAttempt.name "Steelix" "Onix" Instead, the hard-bound call to is able to be overridden with . Since was applied, we got the newly created object back, which we named , and we see that indeed has the value . attemptBinder("Steelix") new new secondAttempt secondAttempt.name "Steelix" Thus, the newly created is used, rather than the previously specified for . Effectively, is able to override . this hard-binding this new hard-binding The primary reason for this behaviour is to create a function that essentially ignores the , and presets some or all of the function’s arguments. this hard-binding Finally, determining “this” We can summarize the rules for determining this from a function call’s in their order of precedence. call-site, Here they are : Is the function called with ? If so, this is the newly constructed object Example, new (New binding). var attempt = new catchPokémon("Pidgey"); Is the function called with or , even hidden inside a ? If so, this is the explicitly specified object ( . Example, call apply bind hard-binding Explicit binding) var attempt = catchPokémon.call("Pidgeotto"); Is the function called with a context, otherwise known as an owning or containing object? If so, this is that context object ( . Example, Implicit binding) var attempt = firstAttempt.catchPokémon("Pidgeot"); Otherwise, this defaults to the object, or in mode ( global undefined strict Default binding). Summary Determining the binding for an executing function requires finding the direct of that function. this call-site Once examined, four rules can be applied to the call site, in this order of precedence. Called with ? Use the newly constructed object. new Called with or or ? Use the specified object. call apply bind Called with a context object owning the call? Use that context object. Default : in mode, object otherwise. undefined strict global Credits Official documentation: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/this You Don’t Know JS: this and Object Prototypes, by Kyle Simpson. Previously published at https://anmshpndy.com/how-well-do-you-know-this

Everything You Need to Know About “this” in JavaScript

Not an Early Adopter I remember the first time I heard about Progressive Web Applications.  I remember being completely underwhelmed ... I was certainly not an early adopter of this technology.  In fact, it was several years before I caught the PWA bug after beginning a deep-dive into Web Workers for another project.  I heard about Service Workers and immediately saw the implications for several side projects I had been contemplating. It All Starts Coming Together ... I built two PWAs, implementing them with the simplest code-base I could manage (embedded CSS, JavaScript, Data, and Images). The designs and concepts were simple and ; worked very well, in fact. worked While my thought had been to design these sites with the minimal amount of viable code (without any frameworks), I quickly realized that I had developed a strong baseline that I could use for comparison: if these sites were re-designed using modern frameworks, I could then see very quickly where the benefits were. Then I hit a game-changer ... Hyper Lightweight Websites Hyper Lightweight Websites (Chris Zacharias) In this presentation, we get ... A hyper-light website is a of a web-based user interface designed for the purposes of pushing the limits of performance. reconception The goal is to provide an experience, roughly consistent with the real thing, that can be delivered to an end-user as fast as possible. High latency environments are everywhere, even to this today. Steps to build a hyper-light website: the most active page on your website. IDENTIFY a new version of that page from scratch with a singular focus on optimization. BUILD all of the HTML, CSS, and JavaScript into a single "hyper-light" HMTL page. ASSEMBLE the hyper-light page behind a CDN with compression and HTTP/2 pipelining. SERVE both the original page, uncached, and the hyper-light page. MEASURE the results. ANALYZE Things you want to KEEP doing: Serve real content, preferably dynamically. Responsive layouts at minimum, common breakpoints. Responsive images. Sprites, in some cases. SVG graphics optimized responsibly. Accessibility. Things you want to STOP doing: Ads and social media trackers. CMS integrations. JavaScript libraries. CSS layouts via frameworks. Lazy loading content, . except images Data URIs, . unless absolutely certain JavaScript and CSS compilation. Custom web fonts. Recommendations (when possible) Inline as much as possible on the server-side. Use HTML5 semantic elements. Relative layout using root em sizing. Use HTML templates. State transitions using the CSS :target selector. Replace SVG with HTML and CSS. Use SVG sprite with fragment identifiers. Image-based sprites. Responsive images using srcset. Measuring Performance Use . sitespeed.io Repeat the process with other pages. Build a Performance Budget. Run site speed regularly. or similar tooling PWAs (and Code) Progressive Web Apps following hyper-lightweight design: United States License Page Game: http://bit.ly/uslpg Interview Question & Answer Application: http://bit.ly/interviewqa Code for designs: CODE: United States License Page Game: http://bit.ly/uslpg-code CODE: Interview Question & Answer Application: http://bit.ly/interview-qa-pwa-code Conclusions First, PWAs are both fun and cool.  The Service Workers are providing features for much more than mobile development. Second, the tight pattern that I used, while going old-school in my development of a few PWAs was actually the use of newer patterns ... allowing me to step away from frameworks and SPAs while focusing on speed and performance. Also, I'm now truly glad that I took the time to play with Progressive Web Applications and the associated technologies; it's been an eye-opening experience. Finally, I'm not sure if old-school has become new again, but there were definitely some useful implementations we had to overcome limited bandwidth (which we still have ... mobile devices or location-specific). Also published at https://dev.to/rfornal/hyper-lightweight-website-designs-1h3d

Following These 6 Steps Will Help You Build A Super Lightweight Website

Recently, I have decided to become a founder again. After 2.5 years of being employed/contractor, I just felt it was about time to venture out again. There were a couple triggers for that decision, which I will tackle in the following few paragraphs. Firstly, I am grateful for all the people who have put their trust in me in the past 2.5 years and I can honestly say that I gave it my very best every single time. But to be completely transparent, I think a part of me knew that this part of my career was always going to be temporary. I always wanted to be a founder, since I was a kid. It is something deeply embedded in how I think about life. And going forward, I can’t really see myself doing anything else than building companies. Secondly, around December my personal life started falling apart. I went through a really bad break up. And I had one of those epiphany moments when you ask yourself — what the hell am I going to do with my life now. So again, it felt only natural to me to go back to who I really am. And finally, I dug up some of my old ‘Evernotes’ and I realised that I have been passionate about this space for the past 4 years. And I feel with every step that I have taken, I have been subconsciously preparing myself to launch . Crunchdex Before I launched my newest venture, I have decided to write down from all the startups that I have been ever involved in. And because a couple of my close friends have asked me to share this publicly, you can find the full list below. I sincerely hope it will help at least one of you to avoid making the same mistakes that I have made. If it does, please do let me know. It will make my day. the biggest lessons I will keep it bitesized. So feel free to ask for more details in the comments. 1. ClickandStudy 📚 — marketplace of language schoolsLessons: Not all attention will result in revenue.& Don’t ever accept media for equity . Don’t ever do that. I know this might sound like a really harsh opinion, but bare with me for a second. First of all, your shiny new media partner will have absolutely . Second of all, the sheer economics of such a deal never work in the long run. Sure you will see a cool spike in the short term, but at some point there is only as many times as you can recycle a promotional piece of content. And finally, if you are really clever, I am sure you can figure out a way to get in the press anyway. Screw media for equity, seriously. zero skin in the game Secondly, .My first hand experience: We did a viral video together with two Youtube influencers, which now has over . I think only in the first week, it had like 1M views and brought us hundreds of thousands of visitors. Do you know how many conversions we have actually made? De nada. Jackshit. Zero. I mean ok, some people did convert, but none of them became loyal customers. And it had absolutely zero impact on the business. some attention is totally worthless 7M views on Youtube 2. Labelgram 🤳 — influencer marketplaceLessons:**Small Social Media influencers have a bigger influence than celebrities.** If you can’t sell it, don’t even bother building it. . In early 2014, when I started telling brands that it’s better to hire 100 people with 1.000 followers than one celebrity, to promote their newest coolest product, everyone thought I was insane. How would we manage 100 people? Is this even legal? Don’t they have to tell their friends it is product placement? Shouldn’t we rather work with a pro? If you want to run a really effective influencer-driven campaign, hire a lot of small influencers rather than really big ones Ok, in hindsight I have to admit, at that point I had no freaking idea about the legal part. And that’s probably like 50% of why this venture failed. But I still firmly stand behind the claim that you should work with small influencers whose followers actually care about what they post. You wouldn’t believe how many big influencers to grow their follower-base. use bots . I still believe the idea was firm. And if you look at the funded startups in this space, I also think the timing was right. I just didn’t have any experience with enterprise sales at that point of my life. I had a pretty good idea of the influencer/social part. But I should have spent 100% of my time trying to find the first customer before I even started building it. Don’t build it, if you can’t sell it 3. Nestpick 🏠 — Airbnb for long-term rentalsLessons:**If you are a crucial team member, ask for equity as soon as you can.Money can screw up almost any friendship.** So here is another unpopular opinion, I think you should never ever ask someone to sign a non-compete. Especially, if they are a crucial team member. Instead give them equity or stock options and if they want to leave — fine, let them. But they are leaving their baby behind as well. Also, as an early employee . Negotiate. Be reasonable. Get it signed. Get it done. Don’t wait. It will always be a difficult discussion. The longer you wait, the more difficult it will become. don’t ever be afraid to ask for equity M . I hope you will never have to go through this yourself. As experiencing this first hand, especially when you are young can really leave some scars for a long time. There is no real way to avoid this. I think all you can do is try to be honest and sincere. And try to understand other peoples’ motivations. oney can screw up any friendship People will say it’s business. It’s not personal. The thing is — startups are personal, because you start from nothing with a handful of people and a somewhat crazy idea. So when you start making it, try to keep the same attitude as when you had nothing. It’s tough. Most people can’t manage. But if you ever get into a situation where you have to choose $ over your closest friends, try to take a step back and think. Because you can always make more money, but nothing will bring these real friendships back. 4. Rentomato 🧰 — SaaS tool for Landlords and Property ManagersLessons:**Don’t stay in the same industry just because you think it makes sense. Sometimes timing is more important than execution.** After nestpick, I felt it’s only natural to do something in Real Estate again. I can now honestly say it was maybe one of the worst decisions that I have ever made. Why? Simple. Even though, at my job was to rent out as many places as possible, I never actually cared about the real estate part of it. I was happy to market a service that is much needed and that creates a real value for the customer. nestpick However, when we launched Rentomato I quickly realised that this is one of those businesses that you just need to plow through. . Secondly, we wanted to charge a % commission (on rent) instead of a regular SaaS fee. Now, this looks fantastic in a pitch deck. But in reality every commission business which has some real life component (except a very few cases) will have a pretty unscalable operational part. And this part will cost you a fortune to scale. It was extremely difficult for me to get passionate about building a SaaS tool which is a nice-to-have but not an actual painkiller In addition, . In some cases, I think it really defines your fate. So if I now look at successful European startups, I feel we were a bit too early to the party. And given our lack of passion, we just didn’t have the stamina nor the resources to make this business a real success. I truly believe timing really matters for startups prop-tech 5. Spaceship 🚀 — Data Mining Company for B2B and B2CLessons: A bit too many :) was probably the one startup that defined my career so far. If there weren’t for Spaceship, I would have never found my passion for data. I would have never been able to join a VC fund. And I would have never made it anywhere. Spaceship Yes, we really did have Donald Trump with never@president.com on our homepage :D The story of how we founded Spaceship is something that I wanted to share for a long time, because I really find it quite funny… so here it comes.. At some point Rentomato was running out of money pretty fast. One day, I woke up, checked our bank account and found out we have have $7 in the bank. I don’t know why, but my natural reaction was to create a spreadsheet with ideas that could make money immediately. We then ranked every idea on 3 variables — how much it can make, how fast we can build it, and how easily can we market it. The list contained anything from 3D printed weed grinders to webinars. Somehow, we landed on the Spaceship idea. Which for us basically meant to repackage and sell our own lead generation tools. This business then evolved to a data mining ‘agency’ which serviced hundreds of businesses. However, on this journey we had to hike up the price 1500x. We totally oversold the product offering. We unfortunately totally pissed off some valued customers. And we realised that some businesses are good cash-cows, but a bit of a nightmare to grow. If I should however share the biggest learning from this whole Spaceship flight — I would say it’s the following: Now you might be asking; what about passion? Sure, passion is important. But people who are in dire need to make money typically subconsciously evaluate their best skills and go for something that they know how to do fairly well. At least well enough for others to pay for it. I know it doesn’t sound romantic. And sure, most of these businesses will probably not scale. However, and this I firmly believe, it will teach you the most about business. Go and ask every real entrepreneur who had to start from zero at some point of his/her life. Sometimes the best business ideas come from a desperate need to make money. 🛒 🍨 6. DataDaily & Nanuk — Data Marketplace & — marketplace for co-living (for digital nomads)Lessons**:Do things with passion or not at all.Not everyone is meant to be an entrepreneur.** During my free time when I was still in VC, I have helped one of my friends and also my ex-girlfriend to launch an online business. Neither of them was a total failure and both of them made some $. However, it taught me that . And there is absolutely nothing wrong with that. Really. Nobody can tell you how to live your life. And if you don’t want to build a company, it doesn’t mean that your career has less meaning or value. It can actually have a lot more meaning and impact than building a startup. not everyone is cut out to be an entrepreneur Secondly, . If you start obsessing about the startup more than the founder, get the hell out. Just say — sorry, and leave. Immediately. And if you can, on the way out of the door try to politely explain to them that in order to start something real, they need to be really committed. Otherwise, they are wasting the most valuable currency — TIME. Not only theirs. But also the time of everyone involved, which is a crime. don’t ever engage in a halfhearted startup 7. Other Startups 😶 — Lessons: Not that many I have been involved in a few other startups over the past few years. Unfortunately, I cannot say that any of them has taught me anything valuable. And that’s fine as well. Not every experience you go through in your life has to necessarily teach you something. However, once asked me in his podcast — what is the one learning I would tell to my younger self. My answer was: . Simply because you are not learning anything real. Nathan If you ever look back and you don’t feel what you were doing 9, 12, 18 months ago was stupid, then you are seriously wasting your time Podcast If I look back at some of the marketing f*** ups I have made. If I look back at some of the brutally naive sales tactics that I have used. If I look back at some of the terrible business decisions I have made. I FEEL STUPID. Sometimes even a little ashamed. And for me that’s totally fine. This feeling comes from the sheer fact that I am convinced that I have grown and I can do it much better now. That’s awesome. It doesn’t guarantee that next time around it will be a success. But at least I have learned something. So these are some of my lessons so far. I do hope you laughed a little. And I do hope it helps at least one of you at some point of your life. If you enjoyed the read, please ❤ it or even better send me a message.

10 Life Lessons from 8 Startups

When I am discussing , and distributed ledger technologies with friends and colleagues, I always try to mention and explain the following statement: Bitcoin blockchain Don’t trust, verify. This is a very strong statement, which indirectly describes one of the biggest advantages of Bitcoin and consequently blockchain. Bitcoin arose as an alternative to our existing, catastrophically failing banking system. Our current banking system is based on trust and like initially wrote in 2009: Satoshi Nakamoto “The root problem with conventional currency is all the trust that’s required to make it work. The central bank must be trusted not to debase the currency, but the history of fiat currencies is full of breaches of that trust. Banks must be trusted to hold our money and transfer it electronically, but they lend it out in waves of credit bubbles with barely a fraction in reserve. We have to trust them with our privacy, trust them not to let identity thieves drain our accounts. Their massive overhead costs make micropayments impossible.” With Bitcoin (and blockchain) we now got a technology, which is still in an early phase of its development, but already offers a solid solution, which has been proved itself as an alternative since 2009. The wallet is the interface to the underlying technology and up to now not quite ready for mass adoption. There are three essential aspects to evaluate a wallet: — Can I validate transaction myself? Do I have to trust a 3rd party? Trust — Is it possible to anonymously buy / sell / transact? Privacy — Is my key secure? How is it stored? Security When examining Coinbase: — you cannot validate the transaction yourself Trust — you need to register Privacy — you don’t have access to your key (or you don’t have a key at all) Security Coinbase is the counterexample of what Bitcoin is meant to be. However, (most popular full node implementation) is not perfect either: Bitcoin Core — it is a full node, which is able to fully validate Trust — no sign-up required and it is possible to create multiple addresses Privacy — it does not come with a secure store (e.g. hardware wallet) Security Before going deeper into the distinctions and trade-offs, the concept of and (Simplified Payment Verification) will be explained, which are two options to choose from, when not using an exchange: full nodes SPVs An is a Bitcoin implementation that does not verify everything. The SPV client submits a bloom filter (a filter to request only matching transactions and merkle blocks) to a full node providing SPV services. Each time a block header is sent, a full node will check against the provided bloom filter if transactions that may or may not be interested is in the block. If positive, then the identified transactions will be included, plus all other transactions required to compute the merkle root. For each block header received, the SPV client will check if it contains a proof of work solution that meets the difficulty requirement and if it is linked to the previous block. SPV Nodes that fully verify all of the rules of Bitcoin are called . The most popular implementation of a full node is . Full nodes download every block and transaction and check them against Bitcoin’s consensus rules. full nodes Bitcoin Core https://bitseed.org/mobile-bitcoin-wallets/ It is necessary to run a full node and not just an SPV. Otherwise miners can just change the consensus rules and/or the full node you trust can be simply adversarially adjusted. The fee estimation should not be done by anyone else, but by yourself. However, the SPV does not have the mempool / UTXO set, which is required to do proper fee estimation without relying on a centralized service. Nevertheless most of the people are using centralized validation like Coinbase and Trezor (the transactions are sent to their service, but alternatives with Electrum exists though), because of the good usability and the ease of use. For the average person it is impracticable to download the entire blockchain and set up a full node on a computer, a server or even a smartphone. There are neither mobile carrier contracts with up to 200 GB mobile data bandwith, nor smartphones with this amount of internal storage. With a block size limit of 1 MB, Bitcoin’s blockchain scales like around 50 GB per year. If the block size is even bigger, then it becomes even harder to run and maintain a full node for the average person, because it will be economically infeasible. Which means: Less full nodes, less decentralised. Centralized validation is convenient, but as Satoshi Nakamoto indirectly stated: it is also the root of failure in the past. However, full nodes are required to make Bitcoin trustless. There is no central institution (like a bank or PayPal), just the software running on your own computer. Everyone is his/her own bank and should verify for themself. Whenever features are outsourced to someone else, trust and reliance is needed. Though, the less functionalities are outsourced, the more complex the usage becomes and that’s an obstacle for most of the people. It is quite a tough balancing act to have something “easy to use”, in order to have more adoption and at the same time a minimized reliance on a centralized service. Hence, there is still a lot of room for improvements. There are already multiple BIPs (Bitcoin Improvement Proposal), which can and most likely will pave the road for future wallets: e.g. , . The development of a (almost) trustless hardware wallet with great user experience is another thing I am looking forward to. BIP158 BIP174 Bitcoin (and blockchain) is about being able to operate in a trustless manner, in a hostile environment. It provides the great opportunity to verify everything, so you can be aware about everything which is happening in the system. Therefore, we should not forget about this powerful, inherent feature. Sidenote: Currently, it is not much of a hassle and quite affordable to set up and run Bitcoin full node — it is even possible on a Raspberry Pi. Check out this great . tutorial References: Thanks and s/o to Jonas Schnelli and more for the good thoughts and work on this! https://bitcoin.jonasschnelli.ch/BOB_jonasschnelli_csatfow.pdf http://p2pfoundation.ning.com/forum/topics/bitcoin-open-source https://en.bitcoin.it/wiki/Full_node https://bitcoin.org/en/glossary/bloom-filter _First of all, sorry by not asking an objective question, it's because I can't resume this one and I didn't find…_bitcoin.stackexchange.com Understanding SPV, pruning and its downsides, as well https://upscri.be/hackernoon/

What most people forget about Bitcoin (and blockchain)

Hint: It’s NOT pretty… Let’s imagine Bitcoin has accomplished the unthinkable — its become the one true currency used for peer-to-peer payments around the world. In this Bitcoin Valhalla, let’s imagine that all non-cash payments are conducted with Bitcoin. Instead of credit cards, people whip out their favourite Bitcoin hardware or mobile wallets in coffee shops and hair salons across the world. Just how many of these non-cash payments would there be in this perfect world? Today, non-cash payments account for , and that number seems to be increasing in quadratic fashion, meaning it’ll be a lot bigger by the time we get to Bitcoin Valhalla. There’s no predicting when exactly we’ll get there, so let’s imagine that Bitcoin has taken over as the dominant currency approximately 522 billion transactions per year worldwide today_._ Where we stand in terms of non-cash transactions across the world , source: worldpaymentsreport.com Now of course, Bitcoin has to make sure its system can accommodate the enormous number of transactions here without bottlenecking. So let’s dive into the source code and flick the scalability switch that appeared to be there all along — let’s increase the size of the block enough to account for our 522 billion transactions. Just How Big Should Our Block Be? Let’s do some napkin math to see just how big our new block is. 522 billion transactions a year translates to 1.4 billion transactions a day. This is equivalent to , which coincides with how often blocks are published to the Bitcoin . 9,722,220 transactions every 10 minutes blockchain Assuming transaction sizes stay around the same size, at , this means that every block would hold about of data_._ 250 bytes 2.4 gigabytes This transaction volume would generate about on the blockchain every day, or every year. 350 gigabytes 127 terabytes For those who don’t know, the blockchain ledger is upheld and propagated by which are user machines that hold a copy of the current valid Bitcoin blockchain ledger. These nodes all work together to uphold the decentralized blockchain that we’ve come to love. Nodes receive new transactions from users’ wallets, have miners validate them, then receive mined blocks from miners to add to its ledger. The nodes then also immediately propagate these validated blocks out to other nodes across the Bitcoin network. nodes, A visualization of how Bitcoin nodes work in harmony Today, nodes validate and propagate approximately 1 megabyte blocks to other nodes across the world. According to about the effects of block validation on memory usage, the process of validating a single block on a node (i.e. serializing and hashing it) consumes about and takes approximately . The analysis also claims that block validation times scale quadratically, and an eight megabyte block would take approximately to validate. However, given only two datapoints, and for simplicity’s sake, let’s assume validation time and memory consumption scale only linearly. this analysis 1.25 gigabytes of memory thirty seconds 150 minutes This is what the scale of block validation might look like if Bitcoin took over the world today, with 2.4GB blocks Extrapolating to fit our newfound monster blocks, we’re looking at a validation time of over per block, or approximately It would take for a node running current-day consumer-grade hardware to validate any block that it receives. On top of that, memory consumption per block validation runs in the range of . The only machine that seems to come even close to that level of memory (outside of server racks) is the unreleased that will cost well over $10,000 at launch. 51,000 minutes 35.4 days! over a month 3 terabytes HP Z8 Optimizations may be made (for example, block validation is not done in parallel) and we can expect CPU power to continue growing, so a validation time of 30+ days is probably an unrealistic assumption. But will processing power scale enough? This machine would also need to store the 127 terabytes of data generated every year. With the cost of storage , this machine’s operator would need to spend approximately on storage costs to keep up with the blockchain’s growth! hovering at 1.9 cents per gigabyte $2,413 per year In comparison, the blockchain today produces about 50GB of data every year, which amounts to around $0.95 in yearly costs. Right away, this entire thing is starting to look absolutely ridiculous, but bear with me for a little while longer. Bandwidth for our Blocks So our nodes also need the ability to both download these blocks from nodes, and then transmit them to other nodes. Let’s have a look at the current distribution of nodes across the world. source: https://bitnodes.earn.com/ It should be noted that according to the , there are up to 15 times as many nodes (150,000+) that are not shown in distribution charts such as the one above, mainly due to port configurations, but again, let’s keep things simple and assume the given distribution is an accurate one. Let’s assume our node has to download a block every 10 minutes, and uploads the minimum 144 blocks per day . This comes out to over of bandwidth everyday, or . Bitcoin Wiki recommended by Bitcoin Node guidelines 691 GB 20.73 terabytes per month Let’s look at bandwidth costs in the United States, where the largest distribution of nodes reside. Since no consumer grade network could feasibly provide that level of bandwidth, I had to resort to a to see what our connectivity options were. handy CDN aggregator source: www.cdncalc.com We’re assuming that fiber networks are prevalent enough for speed to not be an issue. The absolute lowest price a single node operator in the United States could pay to propagate Bitcoin blocks would be $600/month. So let’s recap. If Bitcoin took over the world today, a single Bitcoin node operator in the United States: would have to purchase a machine capable of providing that would probably cost well above 3TB of memory, $10,000 would need in order to store the blockchain on the machine $2,413 per year would have to route blocks through a CDN operator that would cost over . Gone are the days of Google Fiber and AT&T**!** $600 per month would face block validation times of up to . So our node might not be doing much of anything at all. one month per block All of this, just to run a single Bitcoin node. It doesn’t look like the 150,000+ nodes running today will be able to stick around long enough to see our Bitcoin Valhalla. Instead, they will be priced out and replaced by monopolized and centralized entities with enough financial power to maintain server racks that can handle hundreds of billions of consumer transactions per year. Wait a minute, this sounds suspiciously like Visa, MasterCard, and the modern banking system! I wrote this article for myself more than anything, in order to visualize and understand how drastic the impact of a large-block-size blockchain scaled to meet the needs of our society would be. This is not a formal analysis and shouldn’t be treated as such. However, this extreme Bitcoin endgame does highlight why we shouldn’t let the block size get out of hand. If nothing but massive corporations are left to uphold the few full Bitcoin nodes in our society, , and the security of the network almost solely lies in their hands. In that case, Bitcoin will no longer be the trustless peer-to-peer network it was meant to be and you might as well go back to paying your monthly credit card statement. Bitcoin consensus rules go out the window There are also some holes in my napkin math. For example, by the time Bitcoin reaches this level of adoption, the cost of will have dropped and won’t be as insane as I previously outlined. There will also probably be unforeseen progressions in blockchain technology that could offset the impact of the increased block size. technology Do note that I only accounted for non-cash payments — cash payments still take up the majority of transactions in the world and open up a whole other dimension of non-scalability. Also note that if a node is hit with a malicious invalid block, it will waste an increasingly excessive amount of time trying to validate it. Non-cash payments are also rapidly increasing every year, so hardware usage will also be undoubtedly higher once we near this level of Bitcoin usage. Past these holes in my proverbial napkin, here is the central message all of this is meant to convey: At the end of the day, the beauty of running a Bitcoin node is in the sheer simplicity of it. You or I could run a Bitcoin node on our computers right now, provided we have the hard drive space for it. A Bitcoin node can be run on a . This simplicity is the reason why there are probably over 150,000 nodes in the world, and why Bitcoin continues to function seamlessly as a trustless peer-to-peer network. $90 Raspberry Pi or on a $20/month EC2 instance Palm-sized Bitcoin nodes — they exist! source: bitseed.org If Bitcoin usage starts to get even remotely closely to the numbers I’ve outlined, the ability to uphold the Bitcoin network will quickly slip outside of the hands of users and into the hands of centralized entities. All of this is also not to say that Bitcoin is unable to scale. Bitcoin can and will scale to meet the demands of billions. And it can do so without having to bloat the blockchain! There are several innovative solutions in the works that aim to scale the Bitcoin network elegantly over the long-term without having to increase the block size, the most important of which I’ve outlined in my recent in-depth article . here These solutions are intricate and take time to develop which is why we’re not seeing immediate results, but as the old Toy Story adage goes, “You can’t rush art.” I encourage readers to . Go ahead and see how easy it is! try setting up a Bitcoin Node today Make sure you remember how easy it is, because that is exactly what keeps Bitcoin alive. I’m receiving a lot of feedback (which is fantastic) regarding the fact that the price of technology will drop and so these numbers are not as much of a concern. This is a valid point and I highlighted as much in my conclusion. UPDATE: However, Bitcoin is being adopted rapidly and if you are betting on Bitcoin to reach mainstream adoption as fast as possible, you are also betting on the cost of technology to drop even faster. I personally don’t want to make that bet. I’m not advocating restricting hardware consumption to the point where nodes still need to run on Raspberry Pis. But at what point in time is it reasonable to expect users (or even miners) to house machines with numbers even remotely closely to the ones outlined above? . . Storage prices are stagnating Moore’s Law is struggling to stay alive I also didn’t touch upon the impact this level of chain scaling would have on miners. Even if you believe non-mining nodes are not a necessity, with the given hardware scale required, many miners will be similarly priced out and mining centralization will be probably be promoted. What’s the difference between trusting a minuscule number of near-billion-dollar mining pools securing the network and VISA’s server racks? If you’re interested in more in-depth and informative write-ups like these in the future, I recommend following me here on Medium and on Twitter ! If you have any feedback or if you’ve noticed any mistakes I’ve made, please don’t hesitate to let me know in the comments or in a private note! If you want to learn more about Bitcoin nodes, the Bitcoin wiki is a great place to start . BTC Address: 3MGguJhw1bm95tDQjZ3b8ySBwZLJ77CgG1 I also realize that SPV is sufficient for most users — this is true! But full nodes are still a necessity to ensure decentralization and consensus. We should be encouraging their use, not moving towards dissuading them. A great piece further explaining the importance of Bitcoin’s design: Blockchain, Money, and Social Scalability by Nick Szabo

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