# mgr role stress: ownership #heavy

I feel some people are good at (or trained for) juggling big responsibilities that affect other people as “They depend on my system as a service provider”. I’m not so good at it so I feel these responsibilities are very heavy, and a type of cancer-stress.

Someone like Josh, Larry … need to be on the ball and keep track of the large number of (big or small [1]) changes affecting “his baby”.

In contrast, as a foot soldier I only have obligation (professional responsibility) to keep an eye on my project or my module. Some foot soldiers are eager to learn, but usually her scope of responsibility is much smaller.

Stay on top of “everything” — as a parent I have to be on top of everything about my kids and my house.

LocalSys — If I don’t understand one of many things in and outside my team’s applications, I don’t need to struggle to find out and clarify my understanding.

[1] it’s not always straightforward to determine if a change is big or small. The app owner need to quickly estimate the impacts of each change. Sometimes the estimate is inaccurate. There’s a bit of risk.

# scan array{both ends, keeping min/max #O(N)

I feel a reusable technique is

• scan the int array from Left  and keep track of the minimum so far. Optionally save it in an array
• scan the int array from right and keep track of the maximum so far. Optionally save it in an array
• save the difference of these two arrays

With these three shadow arrays, many problems can be solved visually and intuitively, in linear  time.

How about the classic max profit problem?

How about the water container problem?

# sorting J integers, each in [1,N]

Q: What’s the time complexity of sorting J integer scores which are all [1, N], possibly non-unique?

This is classic counting sort. My book [[algo in a nutshell]] incorrectly says O(J). Incorrect when N is large.

My analysis below uses a total of three dimensions N, J and K, where N and J can be vastly different, and K <=min(N, J), but K could be much smaller.

====analysis

Textbook counting sort is listed at the bottom. If N is smaller than J, then O(N+J) is dominated by O(J). Now suppose N is huge (like the revenue figure of a company).

Suppose there are K distinct scores among the J scores. I would want a constant-time translation from each distinct score to a distinct counter. I wish there’s a perfect hashing from the K scores to K buckets but I think it’s impossible since the K distinct values are not known in advance. Even with imperfect hashing, I can get O(1) translation from each score value to a distinct counter. I will iterate over the J scores. For each,

• look up (on hash table) the corresponding counter.
• If the score is new i.e. missing from the hash table,
• then create a counter
• add the “pair” {score -> counter} into the hash table.
• Also insert the score into a min-heap
• increment the counter

O(K logK) : Now pop the min-heap K times, each time to get a distinct score in ascending order. Lookup the score in hash table to get the counter. If counter for score 55 the count is 3, then output 55 three times. This output would be a sorted sequence of the original J scores.

— comparing with alternatives: Mine is O(J + K logK). If K*logK exceeds N, then I would fall back to standard counting sort.

The comparison-based sort would be O(J logJ), inferior to mine if K is much smaller than J.

The textbook counting sort would be O(J + N) , using N independent counters.

# range bump-up@intArray 60% #AshS

https://www.hackerrank.com/challenges/crush/problem Q: Starting with a 1-indexed array of zeros and a list of operations, for each operation add a “bump-up” value to each of the array element between two given indices, inclusive. Once all operations have been performed, return the maximum in your array.

For example, given array 10 of zeros . Your list of 3 operations is as follows:

    a b k
1 5 3
4 8 7
6 9 1


Add the values of k between the indices a and b inclusive:

index->	 1 2 3  4  5 6 7 8 9 10
[0,0,0, 0, 0,0,0,0,0, 0]
[3,3,3, 3, 3,0,0,0,0, 0]
[3,3,3,10,10,7,7,7,0, 0]
[3,3,3,10,10,8,8,8,1, 0]


The largest value is 10 after all operations are performed.

====analysis

This (contrived) problem is similar to the skyline problem.

— Solution 1 O(minOf[N+J, J*logJ ] )

Suppose there are J=55 operations. Each operation is a bump-up by k, on a subarray. The subarray has left boundary = a, and right boundary = b.
Step 1: Sort the left and right boundaries. This step is O(N) by counting sort, or O(J logJ) by comparison sort. A conditional implementation can achieve O(minOf[N+J, J*logJ ] )

In the example, after sorting, we get 1 4 5 6 8 9.

Step 2: one pass through the sorted boundaries. This step is O(J).
Aha — the time complexity of this solution boils down to the complexity of sorting J small positive integers whose values are below n.

# 3overhead@creating a java stackframe]jvm #DQH

• additional assembly instruction to prevent stack overflow… https://pangin.pro/posts/stack-overflow-handling mentions 3 “bang” instructions for each java method, except some small leaf methods
• safepoint polling, just before popping the stackframe
• (If the function call receives more than 6 arguments ) put first 6 args in register and the remaining args in stack. The ‘mov’ in stack involves more instructions than registers. The subsequent retrieval from stack is likely L1 cache, slower than register read.

# age40-50career peak..really@@stereotype,brainwash,

stereotype…

We all hear (and believe) that the 40-50 period is “supposed” to be the peak period in the life of a professional man. This expectation is created on the mass media (and social media such as Linkedin) brainwash that presents middle-aged managers as the norm. If not a “manager”, then a technical architect or a doctor.

[[Preparing for Adolescence]] illustrates the peer pressure (+self-esteem stress) felt by the adolescent. I feel a Deja-vu. The notion of “normal” and “acceptable” is skewed by the peer pressure.

Q: Out of 100 middle-aged (professional or otherwise) guys, how many actually reach the peak of their career in their 40’s?
A: Probably below 10%.

In my circle of 40-somethings, the norm is plateau or slow decline, not peak. The best we could do is keep up our effort and slow down the decline, be it wellness, burn rate, learning capacity, income,,,

It’s therefore hallucinatory to feel left behind on the slow track.

Q: at what age did I peak in my career?
A: I don’t want to overthink about this question. Perhaps towards the end of my first US era, in my late 30s.

I think middle-aged professional guys should read [[reconciliations]] by Theodore Rubin. The false expectation creates immense burden.

# const data member initialization: simple on the surface

The well-known Rule 1 — a const data member must be initialized exactly once, no more no less.

The lesser-known Rule 2 — for class-type data member, there’s an implicit default-initialization feature that can kick in without us knowing. This default-init interacts with ctor initializer in a strange manner.

##### On a side note, [[safeC++]] P38 makes clever use of Rule 2 to provide primitive wrappers. If you use such a wrapper in place of a primitive field (non-const), then you eliminate the operational risk of “forgetting to initialize a non-const primitive field“

The well-known Rule 3 — the proper way to explicitly initialize a const field is the ctor initializer, not inside ctor body.

The lesser-known Rule 4 — at run-time, once control passes into the ctor body, you can only modify/edit an already-initialized field. Illegal for a const field.

To understand these rules, I created an experiment in https://github.com/tiger40490/repo1/blob/cpp1/cpp/lang_misc/constFieldInit.cpp

— for primitive fields like int, Rule 2 doesn’t apply, so we must follow Rule 1 and Rule 3.

— for a class-type field like “Component”,

• We can either leave the field “as is” and rely on the implicit Rule 2…., or
• If we want to initialize explicitly, we must follow Rule 3. In this case, the default-init is suppressed by compiler.

In either case, there’s only one initialization per const field (Rule 1)

# joinable instance of std::thread

[[effModernC++]] P 252 explains why in c++ joinable std::thread objects must not get destroyed. Such a destruction would trigger std::terminate(), therefore, programmers must make their std::thread objects non-joinable before destruction.

The key is a basic understanding of “joinable”. Informally, I would say a joinable std::thread has a real thread attached to it, even if that real thread has finished running. https://en.cppreference.com/w/cpp/thread/thread/joinable says “A thread that has finished executing code, but has not yet been joined is still considered an active thread of execution and is therefore joinable.”

An active std::thread object becomes unjoinable

• after it is joined, or
• after it is detached, or
• after it is be “robbed” via move()

The primary mechanism to transition from joinable to unjoinable is via join().

# std::thread key points

For a thread to actually become eligible, a Java thread needs start(), but c++ std::thread becomes eligible immediately after initialization i.e. after it is initialized with its target function.

For this reason, [[effModernC++]] dictates that between an int field and a std::thread field in a given class Runner, the std::thread field should be the last initialized in constructor. The int field needs to be already initialized if it is needed in the new thread.

Q1: Can you initialize the std::thread field in the constructor body?
A: yes unless the std::thread field is a declared const field

Now let’s say there’s no const field.

Q2: can the Runner copy ctor initialize the std::thread field in the ctor body, via move()?
A: yes provided the ctor parameter is non-const reference to Runner.
A: no if the parameter is a const reference to Runner. move(theConstRunner) would evaluate to a l-value reference, not a rvr. std::thread ctor and op= only accept rvr, because std::thread is move-only

See https://github.com/tiger40490/repo1/tree/cpp1/cpp/sys_thr for my experiments.

# ##[18] 4 qualities I admire ] peers #!!status

I ought to admire my peers’ [1] efforts and knowledge (not their STATUS) on :

1. personal wellness
2. parenting
3. personal finance, not only investment and burn rate
4. mellowness to cope with the multitude of demands, setbacks, disappointments, difficulties, realities about the self and the competition
5. … to be Compared to
• zbs, portable GTD, not localSys
• how to navigate and cope with office politics and big-company idiosyncrasies.

Even though some of my peers are not the most /accomplished/ , they make a commendable effort. That attitude is admirable.

[1] Many people crossing my path are … not really my peers, esp. those managers in China. Critical thinking required.

I don’t have a more descriptive title..

# 2011 white paper@high-perf messaging

https://www.informatica.com/downloads/1568_high_perf_messaging_wp/Topics-in-High-Performance-Messaging.htm is a 2011 white paper by some experts. I have saved the html in my google drive. Here are some QQ  + zbs knowledge pearls. Each sentence in the article can expand to a blogpost .. thin->thick.

• Exactly under what conditions would TCP provide low-latency
• TCP’s primary concern is bandwidth sharing, to ensure “pain is felt equally by all TCP streams“. Consequently, a latency-sensitive TCP stream can’t have priority over other streams.
• Therefore, one recommendation is to use a dedicated network having no congestion or controlled congestion. Over this network, the latency-sensitive system would not be victimized by the inherent speed control in TCP.
• to see how many received packets are delayed (on the receiver end) due to OOS, use netstat -s
• TCP guaranteed delivery is “better later than never”, but latency-sensitive systems prefer “better never than late”. I think UDP is the choice.
• The white paper features an in-depth discussion of group rate. Eg: one mkt data sender feeding multiple (including some slow) receivers.

# analyzing my perception of reality

Using words and numbers, am trying to “capture” my perceptions (intuitions + observations+ a bit of insights) of the c++/java job market trends, past and future. There’s some reality out there but each person including the expert observer has only a limited view of that reality, based on limited data.

Those numbers look impressive, but actually similar to the words — they are mostly personal perceptions dressed up as objective measurements.

If you don’t use words or numbers then you can’t capture any observation of the “reality”. Your impression of that reality [1] remains hopelessly vague. I now believe vague is the lowest level of comprehension, usually as bad as a biased comprehension. Using words + numbers we have a chance to improve our perception.

[1] (without words you can’t even refer to that reality)

My perceptions shape my decisions, and my decisions affect my family’s life chances.

My perceptions shape my selective listening. Gradually, actively, my selective listening would modify my “membrane” of selective listening! All great thinkers, writers update their membrane.

Am not analyzing reality. Instead, am basically analyzing my perception of the reality, but that’s the best I could do. I’m good at analyzing myself as an object.

Refusing to plan ahead because of high uncertainty is lazy, is pessimistic, is doomed.

# latency zbs in java: lower value cf c++@@

Warning — latency measurement gotchas … is zbs but not GTD or QQ

— My tech bet — Demand for latency QQ will remain higher in c++ than java

• The market’s perception would catch up with reality (assuming java is really no slower than c++), but the catch-up could take 30 years.
• the players focused on latency are unused to the interference [1] by the language. C++ is more free-wheeling
• Like assembly, c++ is closer to hardware.
• In general, by design Java is not as a natural a choice for low latency as c++ is, so even if java can match c++ in performance, it requires too much tweaking.
• related to latency is efficiency. java is a high-level language and less efficient at the low level.

[1] In the same vein, (unlikely UDP) TCP interferes with data transmission rate control, so even if I control both sender and receive, I still have to cede control to TCP, which is a kernel component.

— jvm performance tuning is mainstream and socially meaningful iFF we focus on
* machine saturation
* throughput
* typical user-experience response time

— In contrast, a narrow niche area is micro-latency as in HFT

After listening to FPGA, off-heap memory latency … I feel the arms race of latency is limited to high-speed trading only. latency technology has limited economic value compared to mobile, cloud, cryptocurrency, or even data science and machine learning.

Churn?

accu?

# find all subsums divisible by K #Altonomy

Q: modified slightly from Leetcode 974: Given an array of signed integers, print all (contiguous, non-empty) subarrays having a sum divisible by K.

https://github.com/tiger40490/repo1/blob/py1/py/algo_arr/subarrayDivisibleByK.py is my one-pass, linear time solution. I consider this technique an algoQQ. Without prior knowledge, a O(N) solution is inconceivable.

I received this problem in an Altonomy hackerrank. I think Kyle gave me this problem too.

===analysis

Sliding window? I didn’t find any use.

Key idea — build data structure to remember cumulative sums, and remember all the positions hitting a given “cumsum level”. My homegrown solution kSub3() shows more insight !

# enumerate()iterate py list/str with idx+val

The built-in enumerate() is a nice optional feature. If you don’t want to remember this simple simple syntax, then yes you can just iterate over xrange(len(the_sequence))

https://www.afternerd.com/blog/python-enumerate/#enumerate-list is illustrated with examples.

— to enumerate backward,

Since enumerate() returns a generator and generators can’t be reversed, you need to convert it to a list first.

for i, v in reversed(list(enumerate(vec)))

# c++nlg pearls: xx new to refresh old 知新而后温故

Is this applicable in java? I think so, but my focus here is c++.

— 温故而知新 is less effective at my level. thick->thin, reflective.

— 知新而后温故 — x-ref, thin->thick->thin learning.

However, the pace of learning new knowledge pearls could appear very slow and disappointing. 5% new learning + 95% refresh. In such a case, the main benefit and goal is the refresh. Patience and Realistic expectation needed.

In some situations, the most effective learning is 1% new and 99% refresh. If you force yourself to 2% new and 98% refresh, learning would be less effective.

This technique is effective with distinct knowledge PEARLS. Each pearl can be based on a sentence in an article but developed into a blogpost.

# non-volatile field can have volatile behavior #DQH

Unsafe.getObjectVolatile() and setObjectVolatile() should be the only access to the field.

I think for an integer or bool field (very important use cases), we need to use Unsafe.putIntVolatile() and Unsafe.getIntVolatile()

Q: why not use a volatile field?
A: I guess in some designs, a field need not be volatile at most access points, but at one access point it needs to behave like volatile field.  Qihao agrees that we want to control when we want to insert a load/store fence.

Non-volatile behavior usually has lower latency.

# half%%peers could be forced into retirement

Reality — we are living longer and healthier.

Observation — compared to old men, old women tend to have more of a social life and more involvement with grandchildren.

I suspect that given a choice, half the white-collar guys in my age group actually wish to keep working past 65 (or 70), perhaps at a lower pace. In other words, They will decide to retire not by choice. My reasoning for the suspicion — Beside financial needs, many in this group do not have enough meaningful, “engaging” things to do. Many would suffer.

#### It takes long-term planning to stay employed past 65.

I think most of the guys in this category do not prepare well in advance and will find themselves unable to find a suitable job. (We won’t put it this way, but) They will be kinda forced into early retirement. The force could be health or in-demand skillset or …

# [19] keen observer@SG workers in their 40s,like me

“Most of them in the 40s are already stable and don’t want to quit. Even though the pay may not be so good, they’re willing to work all the way[1]. It’s an easy-going life.”

The observer was comparing across age groups.

[1] I think “all the way” means all the way to retirement age, no change of direction, not giving in to boredom, sticking to the chosen career despite occasional challenges.

This observation is becoming increasingly descriptive of me… Semi-retired on the back of my passive income streams.

# local variables captured in nested class #Dropcopy

If a (implicitly final) local variable [1] is captured inside a nested class, where is the variable saved?

https://stackoverflow.com/questions/43414316/where-is-stored-captured-variable-in-java explains that the anonymous or local class instance has an implicit field to hold the captured variable !

[1] local variable can be an arg passed into the enclosing function. Could a primitive type or a reference type i.e. heapy thingy

The java compiler secretly adds this hidden field. Without this field, a captured primitive would be lost and a captured heapy would be unreachable when the local variable goes out of scope.

A few hours later, when the nested class instance need to access this data, it would have to rely on the hidden field.

# lambda^anon class instance ] java

A java lambda expression is used very much like an instance of an anonymous class. However, http://tutorials.jenkov.com/java/lambda-expressions.html#lambda-expressions-vs-anonymous-interface-implementations pointed out one interesting difference:

The anonymous instance in the example has a field named. A lambda expression cannot have such fields. A lambda expression is thus said to be stateless.

# get collection sum after K halving operations #AshS

Q: given a collection of N positive integers, you perform K operations like “half the biggest element and replace it with its own ceiling”. Find the collection sum afterwards.

Note the collection size is always N. Note K(like 5) could exceed N(like 2), but I feel it would be trivial.

====analysis====

This is a somewhat contrived problem.

I think O(N + K log min(N,K)) is pretty good if feasible.