MongoDB Engines: Demystified

May 3, 20171 like454 views

"MongoDB Engines: Demystified" session at https://www.percona.com/live/17/sessions/mongodb-engines-demystified

MongoDB Engines
Demystiﬁed
April, 26, 2017
Sveta Smirnova

• MySQL Support engineer
• Author of
• MySQL Troubleshooting
• JSON UDF functions
• FILTER clause for MySQL
• Speaker
• Percona Live, OOW, Fosdem,
DevConf, HighLoad...
Sveta Smirnova
2

• First version had only one engine: MMAP
History
3

• First version had only one engine: MMAP
• MMAP was not ideal for all workloads
• Collection-level locking
• No ACID
• No MVCC
• Performance limited
History
3

• First version had only one engine: MMAP
• MMAP was not ideal for all workloads
• TokuMX
• Drop-in replacement
• Document-level locking
• ACID
• MVCC
• Scalability
History
3

• First version had only one engine: MMAP
• MMAP was not ideal for all workloads
• TokuMX
• Percona
• Acquired Tokutek in 2015
• Percona Server for MongoDB
History
3

• Not everyone was happy with MMAP
Why Other Engines?
4

• Not everyone was happy with MMAP
• Other solutions on market
• TokuMX
• WiredTiger
• RocksDB
Why Other Engines?
4

• Not everyone was happy with MMAP
• Other solutions on market
• Version 3.0
• MongoDB acquires WiredTiger in 2014
• New Storage Engine API
• WiredTiger is MongoDB storage engine
Why Other Engines?
4

• Not everyone was happy with MMAP
• Other solutions on market
• Version 3.0
• Percona Server for MongoDB
• Released TokuMX as PerconaFT engine
• Later discontinued
Limitations of SE API
• Added MongoRocks
• Added Memory
Why Other Engines?
4

• Only one per mongod instance
Engines in MongoDB
5

• Only one per mongod instance
• Many in multiple-instance setup
• Replica Set
• Sharding
Engines in MongoDB
5

Memory
WiredTiger
Memory
Multiple Engines in Replica Set
6

MongoRocks
WiredTiger
MongoRocks
Multiple Engines in Replica Set
7

Shard 1 Shard 2
MR
MR
MR WT
WT
WT
Multiple Engines In Sharded Cluster
8

• Default since 3.2
• Document-level locking
WiredTiger
9

• Default since 3.2
• Document-level locking
• MVCC/ACID
WiredTiger
9

• Default since 3.2
• Document-level locking
• MVCC/ACID
• Own Journal
WiredTiger
9

• Default since 3.2
• Document-level locking
• MVCC/ACID
• Own Journal
• Compression
WiredTiger
9

• Default since 3.2
• Document-level locking
• MVCC/ACID
• Own Journal
• Compression
• Own memory management
WiredTiger
9

• Default since 3.2
• Document-level locking
• MVCC/ACID
• Own Journal
• Compression
• Own memory management
• Online binary backups in Percona Server
WiredTiger
9

• Default since 3.2
• Document-level locking
• MVCC/ACID
• Own Journal
• Compression
• Own memory management
• Online binary backups in Percona Server
• Suites for any workload
WiredTiger
9

• 3.2+
• MongoDB Enterprise only
Encrypted
10

• 3.2+
• MongoDB Enterprise only
• Encryption at Rest
Encrypted
10

• 3.2+
• MongoDB Enterprise only
• Encryption at Rest
• Based on WiredTiger
Encrypted
10

• 3.2+
• MongoDB Enterprise only
• Encryption at Rest
• Based on WiredTiger
• Percona suggests to use FS encryption
Encrypted
10

• All data stored in memory
In-Memory and Memory
11

• All data stored in memory
• No persistence after restart
In-Memory and Memory
11

• All data stored in memory
• No persistence after restart
• Can be combined with persistent storage
In-Memory and Memory
11

• All data stored in memory
• No persistence after restart
• Can be combined with persistent storage
• Based on WiredTiger
In-Memory and Memory
11

• All data stored in memory
• No persistence after restart
• Can be combined with persistent storage
• Based on WiredTiger
• In-Memory
• 3.2.6+
• MongoDB Enterprise only
In-Memory and Memory
11

• All data stored in memory
• No persistence after restart
• Can be combined with persistent storage
• Based on WiredTiger
• In-Memory
• Memory
• 3.2.8-2.0+
• Percona Server for MongoDB
• Open Source
In-Memory and Memory
11

• 3.0+
• Percona Server for MongoDB
MongoRocks
12

• 3.0+
• Percona Server for MongoDB
• Compaction
MongoRocks
12

• 3.0+
• Percona Server for MongoDB
• Compaction
• Compression
MongoRocks
12

• 3.0+
• Percona Server for MongoDB
• Compaction
• Compression
• Online binary backups
MongoRocks
12

• 3.0+
• Percona Server for MongoDB
• Compaction
• Compression
• Online binary backups
• MVCC/ACID
MongoRocks
12

• 3.0+
• Percona Server for MongoDB
• Compaction
• Compression
• Online binary backups
• MVCC/ACID
• Best suited for
• Large data
• Write-intensive workloads
MongoRocks
12

• Based on original MMAP
• Default < 3.2
MMAPv1
13

• Based on original MMAP
• Default < 3.2
• Memory-mapped ﬁles
MMAPv1
13

• Based on original MMAP
• Default < 3.2
• Memory-mapped ﬁles
• Collection-level locking
MMAPv1
13

• Based on original MMAP
• Default < 3.2
• Memory-mapped ﬁles
• Collection-level locking
• Memory management delegated to system
MMAPv1
13

• Based on original MMAP
• Default < 3.2
• Memory-mapped ﬁles
• Collection-level locking
• Memory management delegated to system
• No compression
MMAPv1
13

• Based on original MMAP
• Default < 3.2
• Memory-mapped ﬁles
• Collection-level locking
• Memory management delegated to system
• No compression
• No MVCC/ACID
MMAPv1
13

• Storage engines provide great ﬂexibility
• They can be used in combinations
• MongoDB architecture allows easily to test
if engine suits your needs
• Percona Server for MongoDB
• 100 % Open Source
• Includes Enterprise-grade features
• Has reach monitoring
Summary
15

• MMAPv1
• WiredTiger
• MongoRocks
• Percona Memory Engine
• PMM Demo
More informaiton
16

http://www.slideshare.net/SvetaSmirnova
https://twitter.com/svetsmirnova
Thank you!
18

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MySQL Database Monitoring: Must, Good and Nice to HaveSveta Smirnova

It is very easy to find if a database installation is having issues. You only need to enable Operating System monitoring. A disk, memory, or CPU usage change will alert you about the problems. But they would not show *why* the trouble happens. You need the help of database-specific monitoring tools. As a Support Engineer, I am always very upset when handling complaints about the database behavior lacking specific database monitoring data because I cannot help! There are two reasons database and system administrators do not enable necessary instrumentation. The first is a natural or expected performance impact. Second is the lack of knowledge on what needs to be on to resolve a particular issue. In this talk, I will cover both concerns. I will show which monitoring instruments will give information on what causes disk, memory, or CPU problems. I will teach you how to use them. I will uncover which performance impact these instruments have. I will use both MySQL command-line client and open-source graphical instrument Percona Monitoring and Management (PMM) for the examples.

MySQL Cookbook: Recipes for DevelopersSveta Smirnova

This document provides an overview of the MySQL Cookbook by O'Reilly. It discusses the intended audience of database administrators and developers. It also demonstrates different ways of interacting with MySQL, including through the command line interface, MySQL Shell, and X DevAPI. Examples are provided for common tasks like reading, writing, and updating data in both standard SQL and the object-oriented X DevAPI.

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Talk for TestDriven Conf: https://tdconf.ru/2022/abstracts/8763 MySQL Test Framework (MTR) — это фреймворк для регрессионных тестов MySQL. Тесты для него пишут разработчики MySQL и запускаются во время подготовки к новым релизам. MTR можно использовать и по-другому. Я его использую, чтобы тестировать проблемы, о которых сообщают клиенты, и подтверждать сообщения об ошибках (bug reports) одновременно на нескольких версиях MySQL. При помощи MTR можно: * программировать сложные развёртывания; * тестировать проблему на нескольких версиях MySQL/Percona/MariaDB-серверов при помощи одной команды; * тестировать несколько одновременных соединений; * проверять ошибки и возвращаемые значения; * работать с результатами запросов, хранимыми процедурами и внешними командами. Тест может быть запущен на любой машине с MySQL-, Percona- или MariaDB-сервером. Я покажу, как я работаю с MySQL Test Framework, и надеюсь, что вы тоже полюбите этот инструмент.

MySQL Cookbook: Recipes for Your BusinessSveta Smirnova

This document provides an overview of different ways to work with MySQL using standard SQL, X DevAPI, and MySQL Shell utilities. It discusses querying, updating, and exporting/importing data using these different approaches. It also covers topics like character encoding, generating summaries, storing errors, and retrieving metadata. Examples are provided to illustrate concepts like selecting, grouping, joining, changing data, common table expressions, and more using SQL and X DevAPI. MySQL Shell utilities for exporting/importing CSV, JSON, and working with collections are also demonstrated.

Introduction into MySQL Query Tuning for Dev[Op]sSveta Smirnova

Percona Live Online 2021 talk: https://www.percona.com/resources/videos/introduction-mysql-query-tuning-for-devops In this talk I will show how to get started with MySQL Query Tuning. I will make a short introduction into physical table structure and demonstrate how it may influence query execution time. Then we will discuss basic query tuning instruments and techniques, mainly EXPLAIN command with its latest variations. You will learn how to understand its output and how to rewrite queries or change table structure to achieve better performance.

Производительность MySQL для DevOpsSveta Smirnova

Talk for the DevOps Pro Moscow 2021: https://www.devopspro.ru/Sveta-Smirnova/ Производительность MySQL можно улучшить при помощи оптимизации запросов, настроек MySQL сервера и железа. Традиционно эти задачи распределялись между тремя ролями: Разработчик, Администратор баз данных и Системный Администратор. Теперь же все эти задачи решает DevOps, что непросто для одного человека. В этом докладе я расскажу об основных оптимизациях, которые решают большинство проблем производительности MySQL. Для иллюстраций я буду использовать реальные пользовательские истории и Percona Kubernetes Operator.

Modern solutions for modern database load: improvements in the latest MariaDB...Sveta Smirnova

Presented at MariaDB Server Fest 2020: https://mariadb.org/fest2020/improvements/ MariaDB is famous for working well in high-performance environments. But our view of what to call high-performance changes over time. Every year we get faster data transfer speed; more devices connected to the Internet; more users and, as a result, more data. Challenges, which developers have to solve, are getting harder. This session shows what engineers do to keep the product up to date, focusing on MariaDB improvements that make it different from its predecessor, MySQL.

Современному хайлоду - современные решения: MySQL 8.0 и улучшения PerconaSveta Smirnova

MySQL всегда использовали под высокой нагрузкой. Недаром эта база была и остаётся самым популярным бэкэндом для web. Однако наши представления о хайлоде с каждым годом расширяются. Большая скорость передачи данных -> больше устройств с подключением к интернет -> больше пользователей -> больше данных. Задачи, стоящие перед разработчиками MySQL, с каждым годом усложняются. В этом докладе я расскажу как менялись сценарии использования MySQL за [почти] 25 лет её истории и что делали инженеры, чтобы MySQL оставалась актуальной. Мы затронем такие темы, как работа с большим количеством активных соединений и высокими объёмами данных. Я покажу насколько современные версии лучше справляются с возросшими нагрузками. Я надеюсь, что после моего доклада те слушатели, которые используют старые версии, захотят обновиться и те, кто уже обновились, узнают как использовать современный MySQL на полную мощность. Прочитана на конференции OST 2020: https://ostconf.com/materials/2857#2857

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This document discusses different methods for performing schema upgrades in a Galera cluster, including Total Order Isolation (TOI), Rolling Schema Upgrade (RSU), and the pt-online-schema-change tool. TOI blocks the entire cluster during DDL but ensures consistency, while RSU allows upgrades without blocking the cluster but requires stopping writes and carries risks of inconsistency. Pt-online-schema-change performs non-blocking upgrades using triggers to copy rows to a new table in chunks.

Introduction to MySQL Query Tuning for Dev[Op]sSveta Smirnova

To get data, we query the database. MySQL does its best to return requested bytes as fast as possible. However, it needs human help to identify what is important and should be accessed in the first place. Queries, written smartly, can significantly outperform automatically generated ones. Indexes and Optimizer statistics, not limited to the Histograms only, help to increase the speed of the query a lot. In this session, I will demonstrate by examples of how MySQL query performance can be improved. I will focus on techniques, accessible by Developers and DevOps rather on those which are usually used by Database Administrators. In the end, I will present troubleshooting tools which will help you to identify why your queries do not perform. Then you could use the knowledge from the beginning of the session to improve them.

Billion Goods in Few Categories: How Histograms Save a Life?Sveta Smirnova

We store data with an intention to use it: search, retrieve, group, sort... To do it effectively the MySQL Optimizer uses index statistics when compiles the query execution plan. This approach works excellently unless your data distribution is not even. Last year I worked on several tickets where data follow the same pattern: millions of popular products fit into a couple of categories and rest used the rest. We had a hard time to find a solution for retrieving goods fast. We offered workarounds for version 5.7. However new MariaDB and MySQL 8.0 feature: histograms, - would work better, cleaner and faster. The idea of the talk was born. Of course, histograms are not a panacea and do not help in all situations. I will discuss: how index statistics physically stored by the storage engine which data exchanged with the Optimizer why it is not enough to make correct index choice when histograms can help and when they cannot differences between MySQL and MariaDB histograms

A Billion Goods in a Few Categories: When Optimizer Histograms Help and When ...Sveta Smirnova

Last year this session’s speaker worked on several cases where data followed the same pattern: millions of popular products fit into a couple of categories, and the rest uses the rest. Her team had a hard time finding a solution for retrieving goods quickly. MySQL 8.0 has a feature that resolves such issues: optimizer histograms, storing statistics of an exact number of values in each data bucket. In real life, histograms don’t help with all queries accessing nonuniform data. How you write a statement, the number of rows in the table, data distribution: All of these may affect the use of histograms. This presentation shows examples demonstrating how the optimizer works in each case, describes how to create histograms, and covers differences between MySQL and Oracle implementations.

Что нужно знать о трёх топовых фичах MySQLSveta Smirnova

MySQL прочно удерживает второе по популярности место после Oracle в рейтинге DB-engines: https://db-engines.com/en/ranking_trend Репликация, табличные движки и поддержка NoSQL не дают MySQL сдавать позиции с 2012 года: года основания рейтинга. Что особенного в этих фичах? Что нужно знать, чтобы использовать их на полную мощность? Я расскажу про дизайн. Именно он отвечает за то, чтобы ваше приложение не достигло потолка производительности. Понимание архитектуры поможет при проектирование нового приложения, которое впоследствии будет легко масштабироваться. Доклад рассчитан для начинающих пользователей MySQL. Однако поможет освежить свои знания и более опытным.

Billion Goods in Few Categories: How Histograms Save a Life?Sveta Smirnova

We store data with an intention to use it: search, retrieve, group, sort... To do it effectively, the MySQL Optimizer uses index statistics when it compiles the query execution plan. This approach works excellently unless your data distribution is not even. Last year I worked on several support tickets where data follows the same pattern: millions of popular products fit into a couple of categories and the rest used the rest. We had a hard time finding a solution for retrieving goods fast. We offered workarounds for version 5.7. However, a new MariaDB and MySQL 8.0 feature - histograms - would work better, cleaner and faster. The idea of the talk was born. Of course, histograms are not a panacea and do not help in all situations. I will discuss - how index statistics physically stored by the storage engine - which data exchanged with the Optimizer - why it is not enough to make correct index choice - when histograms can help and when they cannot - differences between MySQL and MariaDB histograms Talk for Percona Live 2019 Austin: https://www.percona.com/live/19/sessions/billion-goods-in-few-categories-how-histograms-save-a-life

MySQL Performance Schema in ActionSveta Smirnova

Performance Schema is a powerful diagnostic instrument for: - Query performance - Complicated locking issues - Memory leaks - Resource usage - Problematic behavior, caused by inappropriate settings - More It comes with hundreds of options which allow precisely tuning what to instrument. More than 100 consumers store collected data. In this tutorial, we will try all the important instruments out. We will provide a test environment and a few typical problems which could be hardly solved without Performance Schema. You will not only learn how to collect and use this information but have experience with it. Tutorial at Percona Live Austin 2019

Optimizer Histograms: When they Help and When Do Not?Sveta Smirnova

Talk for pre-Fosdem MySQL Day on February 1, 2019. Last year I worked on several tickets where data follow the same pattern: millions of popular products fit into a couple of categories and rest used the rest. We had a hard time to find a solution for retrieving goods fast. MySQL 8.0 has a feature which resolves such issues: optimizer histograms, storing statistics of an exact number of values in each data bucket. However in real life histograms help not with all queries, accessing non-uniform data. How you write a query, the number of rows in the table, data distribution: all these may affect the use of histograms. In this session I show examples, demonstrating how Optimizer uses histograms.

Billion Goods in Few Categories: how Histograms Save a Life?Sveta Smirnova

We store data with the intention to use it: search, retrieve, group, sort... To perform these actions effectively MySQL storage engines index data and communicate statistics with the Optimizer when it compiles a query execution plan. This approach works perfectly well unless your data distribution is not even. Last year I worked on several tickets where data follow the same pattern: millions of popular products fit into a couple of categories and rest used the rest. We had a hard time to find a solution for retrieving goods fast. Workarounds for version 5.7 were offered. However new MySQL 8.0 feature: histograms, - would work better, cleaner and faster. This is how the idea of the talk was born. I will discuss - how index statistics physically stored - which data exchanged with the Optimizer - why it is not enough to make correct index choice In the end, I will explain which issues resolve histograms and why using index statistics is insufficient for fast retrieving of not evenly distributed data. https://www.percona.com/live/e18/sessions/billion-goods-in-few-categories-how-histograms-save-a-life

MySQL Performance Schema in Action: the Complete TutorialSveta Smirnova

Performance Schema is powerful diagnostic instrument for: - Query performance - Complicated locking issues - Memory leaks - Resource usage - Problematic behavior, caused by inappropriate settings - More It comes with hundreds of options which allow precisely tune what to instrument. More than 100 consumers store collected data. In this tutorial we will try all important instruments out. We will provide test environment and few typical problems which could be hardly solved without Performance Schema. You will not only learn how to collect and use this information, but have experience with it. Made it on PerconaLive Frankfurt, 2018: https://www.percona.com/live/e18/sessions/mysql-performance-schema-in-action-the-complete-tutorial

MySQL Performance Schema in ActionSveta Smirnova

The document describes various features of MySQL Performance Schema. It discusses how Performance Schema provides visibility into SQL statements, prepared statements, stored routines and locks. It provides examples of using Performance Schema tables and views to diagnose issues such as slow queries, full table scans, and locks preventing DDL statements from completing. Hands-on exercises are suggested to practice analyzing statements, prepared statements and stored routines using Performance Schema.

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Что нужно знать о трёх топовых фичах MySQLSveta Smirnova

Billion Goods in Few Categories: How Histograms Save a Life?Sveta Smirnova

MySQL Performance Schema in ActionSveta Smirnova

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MySQL Performance Schema in Action: the Complete TutorialSveta Smirnova

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