Lazin

#include <iostream>
#include <vector>
#include <boost/coroutine/all.hpp>
#include <boost/bind.hpp>
#include <memory>

struct Cursor;

typedef boost::coroutines::coroutine< void(Cursor*) > coro_t;

Lazin

#include <iostream>
#include <vector>
#include <algorithm>
#include <stack>
#include <iterator>
#include <random>
#include <boost/timer.hpp>

using namespace std;

Lazin

template<typename T>
struct serializer<
	type_prop::not_a_pod,
	ser_method::use_internal_serializer,
	archive_type::binary,
	direction::in,
	std::list<T>
> {
	template<typename Archive>
	static Archive& apply(Archive& ar, std::list<T>& list) {

Lazin

//! Loading for std::list
template <class Archive, class T, class A> inline
void load( Archive & ar, std::list<T, A> & list )
{
  size_type size;
  ar( make_size_tag( size ) );

  list.resize( static_cast<size_t>( size ) );

  for( auto & i : list )

Lazin

#include <iostream>
#include <tuple>
#include <vector>
#include <algorithm>
#include <boost/timer.hpp>
#include <boost/range/iterator_range.hpp>
#include <boost/heap/skew_heap.hpp>
#include <google/profiler.h>

using namespace std;

Lazin

class RWLock {
    pthread_rwlock_t rwlock_;
public:
    RWLock()
        : rwlock_ PTHREAD_RWLOCK_INITIALIZER
    {
    }
    ...
};

Lazin

#include <iostream>
#include <vector>
#include <boost/pool/pool.hpp>
#include <boost/timer.hpp>
#include <jemalloc/jemalloc.h>

using namespace std;

const size_t COUNT = 1000000;
const size_t SMALL_OBJ_SIZE = sizeof(int);

Lazin

___ Begin jemalloc statistics ___
Version: 3.5.1-0-g7709a64c59daf0b1f938be49472fcc499e1bd136
Assertions disabled
Run-time option settings:
opt.abort: false
opt.lg_chunk: 22
opt.dss: "secondary"
opt.narenas: 32
opt.lg_dirty_mult: 3
opt.stats_print: false

Lazin

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

Lazin

  void do_read()
  {
    auto self(shared_from_this());
    socket_.async_read_some(boost::asio::buffer(data_, max_length),
        [this, self](boost::system::error_code ec, std::size_t length)
        {
          if (!ec)
          {
            do_write(length);
          }