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Нейронные оптимизаторы запросов в реляционных БД (Часть 2): На пути к продуктивизации

0b0649145892afcd0bf2ede84007f4dc.png

Введение

Нельзя просто взять и заменить нейросетями миллионы человеко-часов, вложенных в разработку классических оптимизаторов запросов реляционных СУБД. Надёжность, гибкость и скорость — ключевые характеристики экспертных систем, которые нарабатывались и отлаживались десятилетиями.

В прошлой статье рассказали о пионерах в области нейросетевых оптимизаторов, которые создали плацдарм для развития подобных ML-систем и их последующего вывода на уровень коммерческих продуктов. В этой же — затронем относительно стабильные подходы, не требующие гигантских вычислительных кластеров и удовлетворяющие большую часть потребностей бизнеса. Серебряной пули, конечно, не существует, но с каждым из этих методов можно прийти к оптимальному решению для конкретной задачи.

Bao

Bao можно воспринимать как логическое продолжение и развитие идей, заложенных в Neo. Более того, создатели Bao и Neo — одни и те же люди!

Эта модель хоть и появилась 4 года назад, но до сих пор фигурирует в сравнениях, и на её основе делают большое количество модификаций. Именно Bao взялась за исправление недостатков, которые останавливали индустрию от внедрения нейрооптимизаторов в продуктовые решения. Например:

  1. Необходимость большого количества данных для обучения модели. Во-первых, их на практике может и не быть, а, во-вторых, некоторые модели должны обучаться по несколько дней, прежде чем достигнут уровня базовых оптимизаторов.

  2. Отсутствие быстрой адаптации модели под новую схему БД, изменение рабочей нагрузки при выполнении запросов, изменение объема данных и т.д. То есть даже обучив модель хорошо отрабатывать в конкретном контексте, малейшее изменение среды нивелирует все полученные бенефиты от нейрооптимизатора — всё придётся делать по новой.

  3. Сложность отладки и интерпретации результатов. Подавляющее большинство нейросетевых подходов представляют из себя монолитные чёрные ящики, интерпретация и отладка результатов которых чрезвычайно сложна.

  4. Очень плохая работа на малом числе запросов (так называемая проблема хвостовых примеров). Дело в том, что в среднем обученный нейрооптимизатор генерирует план качественнее, чем стандартные оптимизаторы. Если взять наиболее типичный запрос — на нём всё отработает хорошо. Однако на редких, уникальных по своей структуре запросах, качество нейросети может сильно падать (вплоть до 100 раз) — это просто неприемлемо в большинстве реальных задач. Что если именно на таком запросе будет выстроена важная логика работы системы? Есть хорошая иллюстрация для понимания концепции хвоста на нашем примере:

Strengthen Your Marketing With Long Tail Search Queries | Sticky

Таким образом, на «Coffe» и Coffe« Beans» всё будет хорошо, потому что именно они создают основной костяк обучающих примеров (на схеме трамплин распределения начинается именно с них, поэтому их количество будет подавляющим). А вот для примера, типа «Medium Roast Coffee Beans Vacuum Pack» выйдет катастрофа. Этот пример почти не вписывается в общую канву, да их и не так чтобы много — поэтому модель просто предпочтёт игнорировать ошибку, возникающую при обработке подобных хвостовых элементов, даже если она будет колоссальной.

Чтобы превзойти как open-source, так и коммерческие оптимизаторы, обучение Bao (Bandit optimizer) может занять до нескольких часов. При всём этом он не будет допускать сильных ошибок на хвостовых запросах, как это делали предыдущие подходы. Также Bao продолжает хорошо выполнять свои функции при изменении схемы БД, рабочей нагрузки и данных в таблицах. Он не отменяет все предшествующие наработки вручную настроенных оптимизаторов — как бы не хотелось признавать, в них вложена мудрость нескольких поколений умнейших программистов. Так почему бы не использовать её вместе с нашими обучающимися машинами?

Разбор алгоритма

Основная идея Bao — встать над базовым оптимизатором и давать ему грубые подсказки, ориентируясь на текущий запрос и то, есть ли сейчас какая-то информация в кэше. Например, модель решит, что лучше не применять loop join'ы в определённой ситуации или отказаться от последовательного сканирования таблиц в пользу использования индекса и т.д. Посмотрим на схему алгоритма:

798736f05ab0b4484477381fab48e633.png

Сначала на вход поступает SQL-запрос, он прогоняется через существующий оптимизатор, который генерирует множество разных планов — по одному на каждое множество подсказок (Hint set). Далее планы поступают на вход уже знакомой нам графовой свёртке, после которой на основе выделенных признаков предсказываются награды, полученные за выполнение планов. Здесь и замечаем основное сходство с Neo: схема выделения признаков, обучения и дообучения предсказательной модели по сути своей идентичны. Однако отличие в том, что Bao не генерирует весь план выполнения запроса с нуля, а лишь выбирает, по его мнению, лучший из уже сгенерированных базовым оптимизатором.

Но не может ведь Bao сразу применять необученную политику на старте своей работы? Каждая такая подсказка — отдельная рука в схеме контекстуального многорукого бандита.

Контекстуальный многорукий бандит

В контекстуальном многоруком бандите (Contextual multi-armed bandit — CMAB), как и в обучении с подкреплением (RL), есть некий агент. Он должен выбрать одно из нескольких доступных действий (рук) в каждом раунде на основе наблюдаемого контекста. Агенту нужно найти баланс между исследованием (exploration) и использованием (exploitation) доступных действий для максимизации накопленной награды. Таким образом, CMAB — это упрощённая версия RL, где агенту не нужно строить долгосрочную стратегию выбора множества последовательных действий, ведущих к финальной награде. Здесь всего одно действие сразу приносит конечный фидбек, что позволяет решать задачу методами с более быстрой сходимостью. Среди таких — сэмплирование Томсона, который авторы Bao и взяли за основу.

Cэмплирование Томсона

Крайне интересный способ решения задачи многорукого бандита. Он связан с вероятностной методологией выбора действия относительно уже прожитого опыта — мы случайно выбираем план, а не просто берём самый оптимальный как в большинстве ML алгоритмов. Если модель сильно уверена, что какой-то план выполнится быстрее остальных — он будет выбран с большей вероятностью, но не наверняка. Всегда остаётся шанс выбрать не самый оптимальный план по оценке модели. Но сделав это, она исследует пространство планов и, возможно, обнаружит, что положение дел изменилось и стоит начать выбирать именно этот, недооценённый ею вариант.

Звучит логично — почему бы изредка не решаться на рискованный шаг, отходить от привычного образа жизни и исследовать существующие возможности, мало ли успех где-то рядом?) Да, можно ошибиться, но это только убедит вас в правильности изначально выбранного курса.

Стоит отметить, что переобучение всей модели — затратная операция, поэтому в Bao модель переобучается каждые n запросов на k последних примерах из блока Experience. Здесь n и k — это гиперпараметры модели, с помощью которых пользователь может регулировать работу системы по своим потребностям.

Векторизация признаков

Осталось посмотреть, как именно векторизуется план в разбираемом алгоритме. Ведь мы помним, что Bao как-то решает проблему адаптации к изменяющимся таблицам базы данных.

Здесь всё проще, чем кажется — Bao при векторизации вообще не учитывает ни конкретные таблицы, ни конкретные столбцы, как было в предыдущих подходах. Взглянем на сравнение векторизации в Bao и Neo:

Bao (позволил себе исправить опечатку авторов в векторе признаков для Merge Join)
Bao (позволил себе исправить опечатку авторов в векторе признаков для Merge Join)
Neo
Neo

Bao, в отличие от Neo, кодирует только сами операторы. В остальном всё так же — one-hot для типа оператора (с учётом null, чтобы всегда получалось бинарное дерево) и 2 числовых значения в конце для кардинальности и стоимости соответственно. Для учёта большего количества динамики можно добавить one-hot значение для имеющегося кэша, что, конечно, будет подсказкой для оценки фактического времени исполнения плана в реальных продуктовых базах.

Результаты

Что отличает статью о Bao от уже рассмотренных мной, так это объём практических экспериментов на реальных продуктовых задачах.

  1. Сравнения проводили на PostgreSQL (с Bao и без), а также на неназванной коммерческой СУБД (с Bao и без).

  2. В качестве бенчмарков использовали IMDb, Stack и Corp датасеты — каждый со своей спецификой под конкретный кейс (изменение схемы БД/изменение рабочей нагрузки/изменение данных).

  3. И вишенка на торте — использовали реальные облачные серверы от Google Cloud разной конфигурации (от 2 до 16 ядер CPU + T4 GPU). Измеряли, как затраченные деньги на все использованные мощности, так и качество/скорость модели.

Посмотрим на детальные показатели для датасета IMDb:

f2c4b31102fd1ae9d0323522ab377fe9.png

Графики слева указывают на зависимость стоимости в центах от конфигурации сервера и оптимизатора. Справа — совокупное время работы каждого оптимизатора в минутах, включая время на формирование плана, обучение и выполнение запросов (опять же в зависимости от конфигурации). Общая тенденция очевидна — Bao везде выигрывает по стоимости и скорости, независимо от конфигурации облачного сервера (2/4/8/16 ядер).

Аналогичная картина будет наблюдаться на остальных датасетах (везде конфигурация для 16 ядер):

f544582e5d917b9c8c7642e02b9ef3f5.png

Ещё у нас произошло снижение времени, затраченного на хвостовые примеры (в статистике бы их назвали выбросами):

54ccdac48125e4fe5161a573da834d51.png

Видим, что на перцентиле 99.5% сильно снижается общее время построения и исполнения запроса.

Остаётся ещё одна финальная схватка со своим концептуальным прародителем — Neo. Посмотрим на суммарное количество выполненных запросов при прогонке IMDb датасета 20 раз подряд на 16 ядрах CPU:

f2409a8e0733b47b83f5c0159d342eba.png

На графике слева нагрузка статична, Bao сразу показывает хороший результат, но через некоторое время Neo накапливает опыт и находит больше пространства для оптимизации внутри своего E2E-пайплайна. Однако справа уже есть график для динамической нагрузки на том же датасете. Здесь Neo, во-первых, гораздо дольше выходит на уровень стандартного оптимизатора, а, во-вторых, и близко не приближается к скорости Bao.

Можно сказать, что Bao превосходно справился с поставленной задачей и во всех смыслах вывел нейросетевые оптимизаторы на уровень продуктовых решений. Более того, попробовать Bao можно самостоятельно с использованием реализации с GitHub.

Balsa

Оставим дивный мир совмещения существующих оптимизаторов с ML-моделями. Наш следующий подход, Balsa — это чисто нейросетевой оптимизатор. Его схема выглядит следующим образом:

d90a67b3551e9eed3761bbd0496b53b8.png

Он схож как с Neo, так и с DQ, но, тем не менее, принципиально отличается:

DQ

Neo

Сходства

- Balsa целиком строит весь план запроса

- Balsa является глубокой RL-моделью

- Balsa не адаптируется под структурные изменения в БД, как Bao

Balsa использует аналогичную DQ схему сбора обучающего датасета

Balsa может использовать beam search для поиска наиболее оптимального плана

- Balsa аппроксимирует истинную функцию стоимости

- Balsa кодирует план аналогично Neo

- Balsa применяет графовые свёртки для выделения признаков из планов запросов

Отличия

Balsa является on-policy RL-моделью, в то время как Neo и DQ — off-policy (это означает, что Balsa в процессе принятия решений использует ту же политику, что и обучает)

Balsa не использует экспертную функцию стоимости на этапе обучения

Balse не использует экспертный оптимизатор запросов на этапе обучения

Из таблицы видно, что Balsa вобрала в себя все самые продуктивные идеи предыдущих подходов, но избавилась от их существенного недостатка — использования результатов работы экспертных систем. Как же Balsa удаётся справиться с построением плана, не прибегая к человеческим наработкам? Ведь обучение политики с нуля вместе с исполнением каждого плана — катастрофически долго сходящийся процесс.

Здесь и возникает так называемый Minimal Simulator.

Minimal Simulator

Минимальный, потому что он использует для оценки планов наивное, не зависящее от эвристик, предположение — чем меньше суммарная кардинальность плана, тем лучше. Т.е. для хорошего плана значение следующей функции стоимости минимально:

C_{out}(T)=\begin{cases} |T| & \quad \text{если T - таблица}\\ |T| + C_{out}(T_1) + C_{out}(T_2) & \quad \text{если $T=T_1⋈T_2$}\end{cases},

где |T| - оцениваемая кардинальность таблицы или join'а в плане с учётом фильтраций.

Конечно, в общем случае это предположение просто неверно. Однако, если модель научится следовать этому принципу перед тем как столкнётся с реальной средой, она не будет на старте генерировать слишком плохие планы, которые на несколько порядков дольше экспертных. Таким образом, мы как бы ускоряем сходимость метода и не даём создавать планы, уходящие в timeout, что особенно критично в продуктовой среде. В таблице выше уже упоминали, что майнинг датасета для симулятора аналогичен DQ и состоит из следующих троек: (запрос, план, стоимость выполнения запроса).

Что ещё примечательно — авторы для выделения признаков пробовали использовать трансформеры, но результат с ними не сильно отличался от свёрток, а ресурсов требовалось больше. На самом деле это и логично — в решаемой задаче нет длинных и сложных цепочек взаимосвязей, которые могли бы сильно сказаться на результатах работы, например, как в случае обработки текстовых предложений.

Результаты

Посмотрим, насколько Balsa лучше предыдущих подходов. Машинка для тестов — 8 CPU, 64GB RAM, SSD и GPU Tesla M60 для обучения.

Во-первых, идут сравнения с оптимизаторами PostgreSQL 12.5 и не названной коммерческой CommDB:

6f03029615b92aa2829effe723fed381.png

Единица в данном случае — результат работы стандартного оптимизатора, а по столбцам — результаты для Balsa на train и ранее не виданном test соответственно. Видим 3 разных группы бенчмарков:

  1. JOB — скорость на всём бенчмарке JOB

  2. JOB Slow — скорость на медленных (краевых) запросах в бенчмарке JOB

  3. TPC-H — бенчмарк для тестирования на искусственно сгенерированных данных, взятых на основе равномерного распределения (параметр scale factor, отвечающий за объём генерации, у авторов равен 10)

Везде результат на train, очевидно, лучше чем на test. В любом случае сеть учится обобщать найденные закономерности и практически везде обходит экспертные системы.

Однако, мы с вами помним, что сходимость у RL-подходов очень долгая и Neo, например, нужно было порядка суток, чтобы обогнать существующий оптимизатор. У Balsa, в связи с пройденным симулятором, такой проблемы не возникает, и он выходит на неплохой уровень уже через 2-3 часа работы на всех бенчмарках:

a3f5b1df05dea335c18dc0b998c51fec.png

А что по сравнению с Neo? Как-никак Balsa отличает только использование симулятора и переход на on-policy RL-схему, когда текущая политика постепенно дообучается на основе своих же принятых решений.

44138546fefcb87fcc5b054b8562063b.png

Получается очень интересная картина. Во-первых, Balsa на первых итерациях генерирует планы примерно в 5 раз быстрее благодаря обучению на симуляторе. Далее на train они вроде бы сравниваются, но на тесте видно, что Balsa гораздо более стабильна и не позволяет себе строить катастрофически долгие планы выполнения запросов, что указывает на лучшую способность к обобщению. И ещё — поскольку Neo быстро копит Experience-датасет, на котором в процессе полностью переобучает модель, он постепенно замедляется. И, например, чтобы прогнать 100 итераций, Balsa потребовалось всего 2.6 часа, а Neo — 25 часов! Вот так казалось бы незначительные модификации заметно улучшают перформанс выбранной архитектуры.

Авторы метода не поленились и произвели завершающее сравнение с Bao:

35ac99531d83b4c0e3044e64698e9293.png

Не удивительно, что Balsa почти везде превосходит Bao, это следствие большего количества степеней свобод у RL-моделей, необходимых для поиска оптимума. Однако сохраняется естественный в этой связи недостаток Balsa, а именно невозможность быстро адаптироваться под изменения в БД.

AQO (Adaptive Query Optimization)

Раз уж мы разбираем production-ready решения в области нейросетей и всего машинного обучения в целом, не могу не упомянуть удачный пример адаптации и применения классического ML-алгоритма K ближайших соседей к задаче оценки кардинальности запросов. AQO, или адаптивный оптимизатор запросов, разработали мои коллеги из Postgres Professional ещё в 2017 году, и он до сих пор развивается в рамках СУБД Postgres Pro и отдельного репозитория на GitHub.

Если вкратце, в данном подходе формируется выборка объектов, признаками которых являются значения селективностей по каждому предикату выполняемого SQL-запроса (эти значения берутся из гистограмм, которые строит postgresql). Для всех таких объектов имеются истинные итоговые значения селективностей, собранные после выполнения соответствующих запросов. Вот и получаем задачу построения регрессионной предсказательной модели, способной неплохо улучшить работу базового оптимизатора с учётом уже прожитого опыта. Были опробованы разные архитектуры — и на основе бустингов, и на основе нейросетей, но на практике лучше себя показал именно KNN. Вообще, весь смысл в идее представления самой модели в виде некоторой неизвестной функции, а как именно её искать — вопрос имплементации.

На Хабре уже была статья, где детально разобраны все аспекты AQO 1.0 — для его изучения советую обращаться туда. На текущий момент актуальным является AQO 2.0, который был выпущен год назад. В нём улучшены производительность и удобство пользования в связи с оптимизацией и упрощением многих сложных элементов системы (соответствующие изменения можно посмотреть в презентации).

Вывод

Итог прост — ML в общем и нейросети в частности могут успешно справляться с вызовами, которые возникают у реального бизнеса. Многие из рассмотренных подходов уже развиваются как в open-source, так и в коммерческих СУБД, что не может не радовать. Остаётся разобрать несколько уникальных по своей природе моделей, которые делают ещё один важный шаг в развитии нейросетевых оптимизаторов запросов — об этом расскажем в новой статье.

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  • 07.09.23 16:24 CherryTeam

    Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/

  • 08.10.23 01:30 davec8080

    The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.

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  • 26.06.24 18:46 Jacobethannn098

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  • 23.09.24 18:56 matthewshimself

    At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.

  • 01.10.24 14:54 Sinewclaudia

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  • 02.10.24 22:27 Emily Hunter

    Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.

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  • 31.10.24 00:13 ytre89

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  • 21.11.24 08:02 Emily Hunter

    If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.

  • 22.11.24 04:41 [email protected]

    I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT

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  • 24.11.24 02:21 [email protected]

    I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT

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