Sunday, 11 December 2022

More on Dynamic proxy

This is a follow up post from dynamic proxy to share about more realistic examples of dynamic proxy.

Today's software development is heavily reliant on system observability. Observability helps us to understand when the system degrades or misbehaves so that we can take proactive measures to fix it. 

Toy service for this example will be FXService 

public interface FXService {

double convert(String from, String to, int amount);

This service will use API for FX conversion.

Lets look at what types of dynamic proxy we can create on top of this.

Method Timing

It will keep track of method execution time and make it available later for analysis or other purposes. Using this proxy, X slow-running methods will be provided.
In FXService, there is only one method, so this proxy will create a method key with the method name and parameters.

Method timing proxy will show top X slow running method for eg. 

Method convert( SGD,IDR,1 ) took 1041 ms
Method convert( SGD,GBP,1 ) took 994 ms
Method convert( SGD,USD,1 ) took 983 ms
Method convert( SGD,IDR,1 ) took 672 ms
Method convert( SGD,INR,1 ) took 650 ms
Method convert( SGD,USD,1 ) took 593 ms
Method convert( SGD,JPY,1 ) took 593 ms
Method convert( SGD,GBP,1 ) took 582 ms
Method convert( SGD,USD,1 ) took 580 ms
Method convert( SGD,INR,1 ) took 566 ms

Such type of proxy is very helpful in identifying outage or degradation in API.

Stand In Processing

Proxy services such as this can be used to provide stand-in processing when the underlying service is down. For example, when a real FX service is down, this proxy can answer queries from the last successful call.

It is useful for not only enhancing availability but also improving latency, since such a service can answer queries from the local cache right away. Additionally, it may be possible to save some costs if the underlying API is charged by usage.

Chain of proxy

Nice thing about proxies is that multiple proxies can be composed together to create a complex chain of proxy. For example, we can chain Stand In & Method timing together to get features of both.

Below code snippet is creating chain of proxy

FXService core = new FXServiceAPI("", 1);
FXService timeRecorderProxy = create(FXService.class, new TimeRecorderProxy(core, tracker));
FXService standInProxy = create(FXService.class, new StandInProcessingProxy(timeRecorderProxy, cache));
FXService fx = standInProxy; 

Full code using all the proxy

List<String> currency = new ArrayList<String>() {{

IntStream.range(0, 100).forEach($ -> {
currency.parallelStream().forEach(code -> {
try {
Double d = fx.convert("SGD", code, 1);
} catch (Exception e) {
System.out.println("Failed for " + code);


Code used in this post is available @ fx service


A dynamic proxy is a powerful tool that is part of Java's ecosystem. It can be a very useful tool for writers of libraries or frameworks, since a proxy's primary purpose is to extend the functionality of an underlying service/api. Therefore, special precautions must be taken to ensure that it does not negatively impact the underlying service.


Dynamic Proxy

In software design, a proxy pattern is one of the popular GOF design patterns. 

A proxy is a class functioning as an interface to something else, it could be an interface to some business logic, network, file, or anything else.

This can also be seen as a wrapper around something core or real, main goal of a proxy is to add abstraction to get something extra it could be logging, permission check, cache, metric collection, etc.

We interact with proxies in real life. Take a example of bank interaction via ATM

ATM acts like a proxy to the bank branch, it allows to do almost everything that can be done at a branch.

In software we see many variations of a proxy, some of the examples are in IO API in java

Another variation is chain of responsibility.  

In java proxy can be of 2 types it can be static and dynamic, lets look at a static proxy example.

Static Proxy

We are building Big Collection that allows to store unlimited data, our big collection interface looks like

public interface BigCollection<V> {
void add(V value);

boolean exists(V value);

void forEach(Consumer<V> c);

Static proxy will have same interface as original interface and will manually delegate calls to real object, it will look something like below.

public class BigCollectionProxy<V> implements BigCollection<V> {

private final Supplier<BigCollection<V>> supplier;
private final BigCollection<V> realObject;

public BigCollectionProxy(Supplier<BigCollection<V>> supplier) {
this.supplier = supplier;
this.realObject = supplier.get();

public void add(V value) {

public boolean exists(V value) {
return realObject.exists(value);

public void forEach(Consumer<V> c) {

Client API for using the proxy will look something like below 

BigCollection<String> collection = new BigCollectionProxy<>(AwsCollection::new);



System.out.println("Exists " + collection.exists("Value2"));

Static proxy is easy to implement but it has got few problems 

  • Manual delegation is painful and very verbose.
  • Any changes in interface required proxy also to implement changes.
  • Special treatment to functions that are part of language ecosystem like equals, hashcode,getClass etc. 
  • and as name suggest it is static, can't change the behavior at runtime.

Dynamic proxy solves issue with static proxy, lets look at dynamic proxy.

Dynamic Proxy

Dynamic proxy creates proxy at runtime, it is very flexible and convenient.

 JDK has dynamic proxy API since 1.3 that allows to create dynamic proxy using very simple API

Foo f = (Foo) Proxy.newProxyInstance(Foo.class.getClassLoader(),
                                          new Class[] { Foo.class },
Lets create dynamic proxy for BigCollection class.

(BigCollection<V>) Proxy.newProxyInstance(BigCollection.class.getClassLoader(),
new Class<?>[]{BigCollection.class},
new BigCollectionDynamicProxy(supplier.get()));

This proxy looks exactly like BigCollection implementation and can be passed around. This also does not have the verbosity of static/hand crafted proxy, full proxy looks something like below

public class BigCollectionDynamicProxy implements InvocationHandler {
private final Object realObject;

public BigCollectionDynamicProxy(Object realObject) {
this.realObject = realObject;

public Object invoke(Object proxy, Method method, Object[] args) throws Throwable {
return method.invoke(realObject, args);

public static <V> BigCollection<V> create(Supplier<BigCollection<V>> supplier) {
return (BigCollection<V>) Proxy.newProxyInstance(BigCollection.class.getClassLoader(),
new Class<?>[]{BigCollection.class},
new BigCollectionDynamicProxy(supplier.get()));


Java reflection makes it easy to delegate calls to underlying real object.

Dynamic Proxy Use case

Lets look at some use case where dynamic proxy will come handy.

 - Timing of method execution

Elapsed time calculation is one of the cross-cutting concerns and proxy comes in handy for such use cases without the need of adding time tracking code all over the place.
public Object invoke(Object proxy, Method method, Object[] args) throws Throwable {
long start = System.nanoTime();
try {
return method.invoke(realObject, args);
} finally {
long total = System.nanoTime() - start;
System.out.println(String.format("Function %s took %s nano seconds", method.getName(), total));

 - Single thread execution

Many time some use case need single thread access to critical data structure. Dynamic proxy can add synchronization at the higher level, core code is not worrying about language level synchronization APIs.

public Object invoke(Object proxy, Method method, Object[] args) throws Throwable {
synchronized (realObject) {
return method.invoke(realObject, args);

 - Asynchronous Execution.

Asynchronous execution is one of the common techniques to get best out of  cores of underlying machine. Java has made parallel computation very easy with Completable future, parallel streams etc. Newer JDK version will have support for fibers and that will add concept of light weight threads.

Dynamic proxy can be used to convert synchronous API to asynchronous with simple code.  

Code snippet for async exeuction.

es.submit(() -> {
try {
System.out.println("Using thread " + Thread.currentThread().getName());
method.invoke(realObject, args);
} catch (Exception e) {

Few things to understand for such type of scenario
 - Value is not return, client API has to use some mechanism like call back handler or reply API to get value.
 - Exception handling.

 - Logging.

This is straight forward use case and very popular one. 

public Object invoke(Object proxy, Method method, Object[] args) throws Throwable {
long start = System.nanoTime();
try {
return method.invoke(realObject, args);
} finally {
long total = System.nanoTime() - start;
System.out.println(String.format("Function %s took %s nano seconds", method.getName(), total));

Dynamic Proxy Tradeoff

Nothing comes for free, dynamic proxy has below tradeoff.

- Reflection cost 
- Parameter box/unboxing
- Array overhead for parameters.

Reflection related cost are not that much of issue in JDK8 onwards. I wrote about reflection in few post.

Methodhandle returns back in java 8

Boxing & Unboxing overhead are really hard to resolve and this will be always overhead for dynamic proxy unless some code generation technique is used.

Dynamic proxy can be used to build many useful things. In next part of this post i will have more advance usage of dynamic proxy.

Code used in this post is available @ proxy github project

Tuesday, 15 March 2022

Concurrent Heap data structure

Lets Heapify!!!

Heap is very popular data structure used for solving Top X types of problem.

For eg find the top 10 popular items by sales volume, top X users by activity etc.

PriorityQueue data structure of java is based on heap and can help in answering any top X type of query. PriorityQueue is not thread safe, so it can't be used in highly concurrent environment without adding lock.

Underlying data structure of heap is array and elements are shifted up and down to maintain the element order, for each swim operation the full array should be locked down to avoid race condition. 

Even read options like poll are mutating operation due to which it is hard to share Heap with multiple threads.

Underlying algorithm makes is very hard to use heap in concurrent or parallel environment.  

Heap - Source: Wikipedia

Lets look at other options to achieve heap like functionality without giving up on concurrency.

Concurrent heap data structure need following properties

  • Highly concurrent ordered collection. 
  • Parallel writes/read support.
  • Top X type of API.
  • Multiple top operations supported concurrently using same instance of data structure.

Concurrent Skip list from JDK looks good candidate for this but we need to add some missing functionality.

Lets recap how Skip List data structure looks.

SkipList - Source:Wikipedia

SkipList is ordered multiple link list, it has got some fast lanes and slow lanes. Fast lanes allow to find element in approx log(n) cost.

- Unique Item identification 

JDK has set and map implementation of Skiplist. Map/Set can only have unique keys, we need to find a way to tweak unique key requirement to make set behave like Heap. 

We can use little trick, every value that is added to SkipList will have additional metadata that can running sequence number or timestamp, this extra metadata can be used for resolving conflict when 2 items are equal based on comparison.

Lets take product by sales use case for code samples. 

SalesItem is Comparable and it compares items by sales volume. 

class SalesItem implements Comparable<SalesItem> {

private final String product;
private final long sales;

public int compareTo(SalesItem o) {
return, o.sales);

We can't add SalesItem in SkipList because items having same sales volume will be rejected.

We can add another wrapper class that adds extra metadata to handle this problem. It will look something like this

class Item implements Comparable<Item> {
private final T value;
private final long index;

public int compareTo(Item o) {

int r = this.value.compareTo(o.value);
r = heapType.equals(HeapType.Max) ? -r : r;
if (r != 0) {
return r;
return, o.index);

index is that extra metadata that is added to handle items with same sales volume and it case of conflict it will order by index

 - TopX API

For TopX API streams.limit can be used, another benifit of using streams APIs is that client application can use other cool features of Streams API.

Full Code for Concurrent Heap

public class ConcurrentHeap<T extends Comparable> {

private final AtomicLong id = new AtomicLong();
private final NavigableSet<Item> data = new ConcurrentSkipListSet<>();
private final HeapType heapType;

public void add(T value) {
data.add(new Item(value, id.incrementAndGet()));

public Stream<T> stream() {
return data
.map(v -> v.value);

public Stream<T> top(int x) {
return stream().limit(x);

class Item implements Comparable<Item> {
private final T value;
private final long index;

public int compareTo(ConcurrentHeap<T>.Item o) {

int r = this.value.compareTo(o.value);
r = heapType.equals(HeapType.Max) ? -r : r;
if (r != 0) {
return r;
return, o.index);

Item(T value, long index) {
this.value = value;
this.index = index;


Underlying data structure that is behaving like Heap is NavigableSet, JDK has 2 implementation if this first one is TreeSet and another one is ConcurrentListSkipSet. 

We can choose between TreeSet/ConcurrentListSkipSet based on need to avoid the cost of concurrency in single thread env.


Full working code for this blog post is available @ github 

Friday, 7 May 2021

What is Artificial Intelligence ?

This is a post from series on artificial intelligence and machine

In this post, we will try to understand what AI is and where machine learning fits in it.

As per Wikipedia Artificial Inteligence is 

Simulating any intellectual task.

It can be also seen as the industrial revolution to simulate the brain.

AI is a very broad field and it contains many subfields and it is important to understand what the full landscape looks like and focus on the core part that overlaps with almost every subfield of AI.

Let's try to understand each subfield.

Knowledge representation

This is core to many AI applications, it is based on an expert system that collects explicit knowledge that is available in some database or possessed by experts.
This can be also seen as Knowledge about knowledge. We interact with system type of system every day be it Amazon Alexa, Apple Siri, or Google Assistance.


Machine Perception is about using sensor input to understand context and action to take. Nowadays we are surrounded by cameras, microphones, IoT devices, etc.

Some real-world applications include facial recognition, computer vision, speech, etc.

Motion and manipulation

This is one of the heavy use of AI, it includes robotics. The industrial revolution has already helped the world economy grow, and robotics will take it to the next level. Some applications in industrial/domestic robots. In the time of pandemics like Covid, robotics is even going to help more as everyone is concerned about safety. Autonomous vehicles are one of the important applications of this sub-field. 

Natural language processing

NLP allows the machine to read and understand human language. It includes processing huge unstructured data and derives meaning from it. Some of the application that we get interact every day is search autocomplete, auto-correction, language translator, chatbots, targeted advertisement, etc.

Search and planning

This area covers machine that is set a goal and achieves it. The machine builds the state of the world and can make predication on how their action will change it. 


This is also called as Machine Learning and it is the study of computer algorithms that automatically improve through experience. 
It sounds like how humans learn something!

It is a subfield of AI but the most important one as it is applied to all the subfields of AI, knowing this is a must before starting on any other subfield of AI.

Let's explore more on the Learning part now.

What is machine learning? 


One of the quick definitions of machine learning is pattern recognization, it can also be seen as how computers can discover to solve problems without explicit programming. 

Machine learning is made up of 3 steps.

The step of updating the model via learning is where real machine learning happens. 

Data science is related to machine learning but is often seen as only machine learning.  AI & data science good overlap with machine learning, it can be seen as below.

Where does data science fits in machine learning? It is the unified concept of statistics, maths, data mining, data analysis, etc

Data Science

Now with a high-level understanding of AI, ML & data science, we are ready to do deep dive in ML.

Artificial Intelligence and machine learning

This post contains a catalog of high-level concepts in AI & ML.

I will keep on updating as I write more stuff

Saturday, 10 April 2021

Timeless investing lesson

I usually don't share my thoughts about investment via the blog but thought it would be useful to record the most important investing lesson so that I can come back and refer to it.

The last few weeks have been interesting in the stocks investment world, some of the recent events reinforce timeless investment advice that people only learn by making mistakes.

In this post, I will share 2 such pieces of advice with very recent examples. 

Never trade on leverage

This is the number 1 reason why people lose money and also differentiate gambler(i.e trade) vs investor.

Archegos hedge fund took down 4 major investment banks on March 2021.

Let's try to understand leverage before we get into how Archegos crash and burned.

Meaning of leverage

"use borrowed capital for (an investment), expecting the profits made to be greater than the interest payable."

The first exposure of leverage everyone gets exposed to is via housing loans, banks will fund part of the house price and the borrower will pay interest over the amount that is borrowed.

I would say leverage via house loan is good leverage because it allows to get shelter overhead and also has a good chance of price appreciation. 

One of the most important things about a house loan is that tenor of payment is fixed and not linked to the underlying asset value.  

If house price falls by 50% then the borrower doesn't have to pay more to a bank but he can gain if the price goes up by 50%. I am not saying a house loan is a good leverage but it is on the fence type of thing. 

Lets see how leverage works in the investment world. 

In trading/investment world leverage is called a margin, the trading broker will allow x times of leverage to clients but with conditions that whenever they issue a margin call then the investor has to deposit more money or sell some securities.


Another example to understand this, assume we have 1 Million and we get 10X leverage, this means that we can buy securities worth of 10 million. 

Only 2 things can happen and each has the probability of 50-50 in short term.

The bet goes your way

This is the happy scenario where the market value of securities goes up by 20% and you are happy and also pay back some of the leverage and reduce risk.

The market is cruel to you

This is what happens in most of the scenarios and you loose 20%, in this scenario, you lose your 1 Million and an additional 1 Million since you will not have the cash to fulfill the margin call you will start selling securities or take more leverage. In whatever options are selected it will cause panic in the market and will cause more selloff. 

Now, this is exactly what happened with Archegos hedge fund because a bet on one of the Chinese company did not go their way. 

Archegos hedge fund is a family-run business and the owner has not so good reputation, they did few things very extreme to fail big.

- Took leverage of around 500 times.

- Used Contract for difference, which are very high leverage instruments and it is ban in many markets. Many individual investors are not allowed to trade in CFD by regulators. 

Now how does anyone gets 500 times leverage? 

They used a couple of brokers to get leverage and these are big names like Goldman Sachs, Morgon Stanley, Deutsche Bank, Credit Suisse, Nomura Holding.

None of these brokers were aware that Archegos is operating on such high margin and also to add that risk management process of these banks were not to the mark to issue some warning signal before it was too late.

Look at the stock prices of these brokers when this thing came out.

Goldman Sachs & morgan stanley was little smart enough to recover their loss but Credit Suisse and Nomura were caught off guard.

Billions of dollars were wiped up, some of the numbers that are coming in news are 10 Billion but many experts feel that it could be 100 Billion, many of these banks have already declared that they are going to report losses in the next quarter and will also result in some people losing jobs.

As I write this post, the stock price has dropped more :-(

This is not the only example where a big leverage bet has gone wrong, the 2008 subprime crisis was also due to leverage and the common man was directly impacted by that.

Keep eye on this news to understand the real impact.

As an individual investor never trade on leverage. 

Markets are efficient 

There are 2 popular styles of investing growth and value. Warren Buffett is a value investor and many companies and people try to follow him.

It is hard to achieve anything close to Mr Warren because to become Warren you need the temperament and patience of warren. 

Many investors try to pick stocks using value investing techniques and when they fail then they try to pick a fund manager that can do value investing for them.

Trust me that picking stock or fund management is like flipping a coin and the downside probability is very high. 

On March 10, 2021, International Value Advisers ( IVA) decided to liquidate the fund.

IVA was an esteem value shop and it has a sad and common end of actively managed funds.

IVA was sitting on cash for a very long time because they thought the market is expensive, there was a time when the fund had 50% cash waiting to be deployed.

They got asset allocation wrong and waited for the timing market

The efficient market hypothesis states that share prices reflect all information and consistent alpha generation is impossible.

Every now and then someone will come and tell the market is inefficient and will try to fight against it.

This has been proved multiple times and a nice article was posted on Forbes about it, it is called any monkey can beat the market.

IVA investors would have made lots of money by just investing in a broad market index fund. 

Morningstar has done a nice analysis of the IVA fund, read this to understand in detail what went wrong. 

If you can't beat the market then be the market, Index fund should be the core strategy. 

I will leave you with one more interesting read about Warren Buffett Just Won a 10-Year Million-Dollar Bet, where he challenges hedge fund managers to beat the market and they end up losing and have to close the fund.

I will wrap up now but if you want to remember one thing then it has to be "never trade on leverage"