central features of WPF

After studying (50%?) of the most popular wpf features at a superficial level, I now feel at the “center” of the features are
– data binding from VM to visuals
– DProp

Many important features grow (extend, borrow) from the center
– 2-way data binding
– binding from visual to visual
– data triggers
– property triggers? based on DProp
– AProp? based on DProp
– command binding? borrows from data binding
– Most controls rely on data binding
– Most controls expose a lot of dependency properties, and these are easily programmable in mere xaml
– a huge variety of requirements are now achievable in xaml without any c# code, largely due to the simple ideas in the “center”.


itemsControl.Items ^ ItemsSource ^ itemsCollection

Any time you have a collection to show, ItemsControl and subtypes ListBox/ListView are the most popular choices. ItemsControl.Items is a property, whose type is ItemsCollection. By the way, ItemsCollection is a subtype of CollectionView — sorting, filter etc.

For Data binding, the target must be this.ItemsSource, not this.Items. Once using that, Items.Add() will trigger *runtime* exception. Instead, add to the data-bound physical collection instead. That collection is usually an ObservableCollection. After you add to it, the screen will auto-refresh, without any intervention.

Under the hood, I believe After the add/remove, the data binding engine queries the physical data and refreshes the display. Just put a breakpoint on the getter of the added item.

strength/weakness ] sg dev talent pool

+++ core trading engine green field development, not just maintenance
+++ [d] java keywords and syntax nitty gritty
+++ [d] threading, data structure, STL
+++ [d] integration of MOM, dispatchers, queues, threads in a high volume design
+++ java and c++
+++ sybase
+++ [d] DB tuning
+++ unix
+++ bond math
+++ tibrv
+++ complex query
+ concurrent package
+ [d] low latency techniques + theory
+ swing
+ java mem mgmt
+ pre-trade quote pricing
+ trade execution, order matching
+ marking, pnl
+ design patterns

– greeks
– unix tuning for low latency
– python
— mkt data
— c#. Many candidates have c# and c++

[d = details]

## low latency – key expertise4GTD

I talked to a few big and small low-latency shops. I feel latency optimization is “low-logic, high speed, high throughput”. Below are the Expertise required, but in any real system, you will see diminishing return on each aspect, so remember to prioritize the cost-effective areas to optimize. See my post on 80/20 (http://bigblog.tanbin.com/2012/03/8020-rule-dimishing-return.html)

* threading
** parallel lock free threading but sometimes we can’t avoid inter-thread-comm (condVar)
** [L] try every technique to avoid locking (such as immmutables)

* [L] low-level memory management — custom allocators, custom (or disabled) garbage collectors
** care and feeding of java GC
** try every way to avoid non-deterministic garbage collection

* in-memory data stores — KDB etc
** This is how secDB compensates for some of the performance drawbacks
** state maintenance in memory — important in many OMS, DMA, exchange connectivity engines.

* try every techniques to avoid RDBMS
** use flat files

* low-latency messaging — 29West, tibrv messaging appliance …
** [H] async MOM is the only choice for high volume systems
** multicast is the de-facto standard

* OS monitoring and optimization
** dtrace
** network utilization
** paging activity
** cpu utilization

* [L] socket programming customized
* avoid ethernet collisions
* [H] avoid choke points, promote multi-lane highways
* [H] connectivity, collocation
* [L] serialization – customized
* [L] powerful hardware, FPGA
* [H] scale out – important
* 64 bit
* realtime java vs C++
[L] = low-level
[H] = high-level architectural feature

c++/java MS commodity IV – 2010

Q: SQL when would u turn on dirty read?

Q: can you tell me some of the drawbacks of stored-proc?

Q: challenges in database refactor?

Q: technical/project challenges you faced in your career?

Q: how do you put across your argument in that challenging situation?

Q: when I enter some.pl in a shell, what happens?

Q: 3 threads are adding/remove/reading single elements in a hashmap. How do you optimize synchronization?
A (now i think): i guess lockfree is perhaps the best
A: concurrent hashmap with each segment holding multiple keys

Q: but if remover and reader use different keys, then why should they wait for each other?
A: they might hit the same bucket

Q: what other maps beside hashmap?
A: treemap, a red-black tree

Q: what’s a red-black tree?
A: perhaps a balanced tree

Q: how is it balanced?
A: perhaps by shifting the root?

Q: What are the list implementations?

Q: is insertion faster in linked list or array-based list

Q: is insertion faster in linked list or hashmap

Where is c++ used? quant lib for risk, PnL and trade booking. Is c++ going away? no.

2 main specializations — physical scheduling + risk including pnl attribution

smart ptr is 50% similar to a raw ptr

I’d say it’s 90% different and 10% similar to a raw ptr. Smart pointers are class objects, way beyond 32-bit pointers. smart pointers overload de-referencer and many other operators to “look like” a raw pointer, but it’s really a class-template (Avoid the non-standard “template-class” jargon)

– pbclone? A raw pointer is always passed by clone (bitwise clone), just as in java. Smart pointers override copier (and op=), so a new instance is created based on the RHS smart pointer instance. Is this pbclone or pbref? I’d say more like pbclone.
– When you use a star to dereference a raw ptr, you simply dereference (unwrap) the real pointer. Smart pointer dereference is a high-level user-defined operation. There’s a get() method to return the  real pointer, but I think we seldom need it.
– Similarly, you use an arrow to access a member of the pointee object, without the get() method.
– You can up/down cast raw pointers, not smart pointers.
– a raw ptr can be cast to void ptr. A smart ptr can’t.
– Raw pointers are key to virtual functions, not smart pointers.
– Creation and initialization is simple for raw pointers.
– size? A Smart pointer exceeds 32 bits except intrusive_ptr
– raw ptr has tight integration with arrays, with pointer arithmetic. Not smart ptr.
– double ptr sematics with 2 stars is natural for raw ptr, not smart ptr
– delete
– new? we can put raw ptr as lvalue of a “new” expression
– null assignment

In conclusion, raw pointers are such a part of the fabric. As a crude analogy, an e-book can feel like a paper book, but you can’t fold corners; can’t write anywhere using any pen; can’t spread 3 books out on your table; can’t tear off a page; can’t feel the thickness

linked hash map with LRU size control

I was asked to implement a generic LRU cache with size control. When size reaches a threshold, the LRU item must be kicked out. Optionally thread safe.

I came up with a concurrent hash map to provide fast get() and put(). Then I realized the kickout() operation would require a full scan of O(n) unless I provide an auxiliary data structure to physically
store the nodes in lastAccess order. I came up with a sorted map (concurrent skip list map) of {time stamp –> key}. My idea was to __adjust__ the physical positions within the aux at every get() and put(). My “adjust” implementation basically removes an item from the aux and re-insert.

Alternatively, the key object could be decorated in a stampedKey objects with a mutable timestamp field and an immutable key field.

That was my back-of-envelope design. Let’s look at the standard solution — a LinkedHashMap with “A special constructor to create a linked hash map whose order of iteration is the order in which its
entries were last accessed, from least-recently accessed to most-recently (access-order). This kind of map is well-suited to building LRU caches. Invoking the put or get method results in an access to the corresponding entry (assuming it exists after the invocation completes). The putAll method generates one entry access
for each mapping in the specified map, in the order that key-value mappings are provided by the specified map’s entry set iterator. No other methods generate entry accesses. In particular, operations on collection-views do not affect the order of iteration of the backing map.”

Apparently that too uses a regular hash table + an aux which is a linked list. (Linked list allows 2 nodes with identical time stamp. For my design to accommodate this, the stampedKey decorator might be stored
in the aux.)

Without looking at its source code, I believe the adjustment uses remove-reinsert on the aux.

Q: removing (not kickout()) from linked list is O(n). How do you avoid that? Remember any remove() on the cache must hit both the hash table and the aux.
%%A: the hash table entry holds a pointer to the node on the linked list

wpf/swing – invite EDT to query the model and update screen

In Swing, You can never change the screen directly — You always “invite” the EDT (or the UI thread in wpf)  to asynchronously query the model and update the screen.

I guess this is a golden rule in wpf and swing, at least for the mainstream visual components with a model/view.

If my custom method synchronously updates a visual component, then this method must always be on the EDT.

In WPF, How about listBox1.Items.Add(..)? I guess this is editing the Model or ViewModel, not the View. I guess screen is updated upon invitation.

(Asked Neeraj…)

"events" in wpf – 3 meanings

In WPF, the “event” word takes on multiple meanings. Know the subtle differences.

1) CLR event — used extensively in INPC. These events are generated by code, not user actions. Here “event” has the unique dotnet meaning. In other GUI systems (like swing), events are also generated from code, such as table change event after you add a row.

2) UI event from keyboard/mouse — the traditional GUI events, implemented (in WPF) with the CLR event + other things. These events have a hardware flavor.

3) routed (bubble/tunnel) event — a special type of UI events?