Un set di repliche in MongoDB è un gruppo di processi che mantengono copie dei dati su diversi server di database. Assicurano ridondanza e disponibilità elevata e sono la base di tutte le distribuzioni in produzione di MongoDB.
Understanding and tuning WiredTiger, the new high performance database engine...Ontico
MongoDB 3.0 introduced the concept of different storage engine. The new engine known as WiredTiger introduces document level MVCC locking, compression and a choice between Btree or LSM indexes. In this talk you will learn about the storage engine architecture and specifically WiredTiger, and how to tune and monitor it for best performance.
MongoDB 3.0 представил новый концепт движков хранения. Новый движок известен как WiredTiger и предоставляет новый уровень документов MVCC фикс, компрессию и выбор между Btree или индексами LSM. В этом докладе вы поймете, как тюнить и мониторить архитектуры движка базы данных, а точнее WiredTiger для получения максимальной производительности.
Back to Basics Webinar 6: Production DeploymentMongoDB
This is the final webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will guide you through production deployment.
Optimizing MongoDB: Lessons Learned at Localyticsandrew311
Tips, tricks, and gotchas learned at Localytics for optimizing MongoDB installs. Includes information about document design, indexes, fragmentation, migration, AWS EC2/EBS, and more.
This document provides an overview of MongoDB sharding, including how it partitions and distributes data across shards, maintains balanced clusters, and routes queries. The key aspects covered are MongoDB's approach to automatic sharding with minimal configuration needed, the sharding architecture involving config servers, mongos routers and shards, and considerations for choosing an appropriate shard key like cardinality and query patterns.
Back to Basics Spanish 4 Introduction to shardingMongoDB
Cómo MongoDB amplía el rendimiento de las operaciones de escritura y maneja grandes tamaño de datos
Cómo crear un sharded cluster básico
Cómo elegir una clave de sharding
Back to Basics 2017: Introduction to ShardingMongoDB
Sharding is a method for distributing data across multiple machines. MongoDB uses sharding to support deployments with very large data sets and high throughput operations by providing the capability for horizontal scaling.
Sharding allows you to distribute load across multiple servers and keep your data balanced across those servers. This session will review MongoDB’s sharding support, including an architectural overview, design principles, and automation.
Back to Basics 2017: Mí primera aplicación MongoDBMongoDB
Descubra:
Cómo instalar MongoDB y usar el shell de MongoDB
Las operaciones básicas de CRUD
Cómo analizar el rendimiento de las consultas y añadir un índice
Understanding and tuning WiredTiger, the new high performance database engine...Ontico
MongoDB 3.0 introduced the concept of different storage engine. The new engine known as WiredTiger introduces document level MVCC locking, compression and a choice between Btree or LSM indexes. In this talk you will learn about the storage engine architecture and specifically WiredTiger, and how to tune and monitor it for best performance.
MongoDB 3.0 представил новый концепт движков хранения. Новый движок известен как WiredTiger и предоставляет новый уровень документов MVCC фикс, компрессию и выбор между Btree или индексами LSM. В этом докладе вы поймете, как тюнить и мониторить архитектуры движка базы данных, а точнее WiredTiger для получения максимальной производительности.
Back to Basics Webinar 6: Production DeploymentMongoDB
This is the final webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will guide you through production deployment.
Optimizing MongoDB: Lessons Learned at Localyticsandrew311
Tips, tricks, and gotchas learned at Localytics for optimizing MongoDB installs. Includes information about document design, indexes, fragmentation, migration, AWS EC2/EBS, and more.
This document provides an overview of MongoDB sharding, including how it partitions and distributes data across shards, maintains balanced clusters, and routes queries. The key aspects covered are MongoDB's approach to automatic sharding with minimal configuration needed, the sharding architecture involving config servers, mongos routers and shards, and considerations for choosing an appropriate shard key like cardinality and query patterns.
Back to Basics Spanish 4 Introduction to shardingMongoDB
Cómo MongoDB amplía el rendimiento de las operaciones de escritura y maneja grandes tamaño de datos
Cómo crear un sharded cluster básico
Cómo elegir una clave de sharding
Back to Basics 2017: Introduction to ShardingMongoDB
Sharding is a method for distributing data across multiple machines. MongoDB uses sharding to support deployments with very large data sets and high throughput operations by providing the capability for horizontal scaling.
Sharding allows you to distribute load across multiple servers and keep your data balanced across those servers. This session will review MongoDB’s sharding support, including an architectural overview, design principles, and automation.
Back to Basics 2017: Mí primera aplicación MongoDBMongoDB
Descubra:
Cómo instalar MongoDB y usar el shell de MongoDB
Las operaciones básicas de CRUD
Cómo analizar el rendimiento de las consultas y añadir un índice
Setting up mongodb sharded cluster in 30 minutesSudheer Kondla
The document describes how to configure and deploy a MongoDB sharded cluster with 6 virtual machines in 30 minutes. It provides step-by-step instructions on installing MongoDB, setting up the config servers, adding shards, and enabling sharding for databases and collections. Key aspects include designating MongoDB instances as config servers, starting mongos processes connected to the config servers, adding shards by hostname and port, and enabling sharding on specific databases and collections with shard keys.
The document provides an overview of MongoDB sharding, including:
- Sharding allows horizontal scaling of data by partitioning a database across multiple servers or shards.
- The MongoDB sharding architecture consists of shards to hold data, config servers to store metadata, and mongos processes to route requests.
- Data is partitioned into chunks based on a shard key and chunks can move between shards as the data distribution changes.
This document provides an overview of MongoDB sharding. It discusses how MongoDB addresses the need for horizontal scalability as data and throughput needs exceed the capabilities of a single machine. MongoDB uses sharding to partition data across multiple machines or shards. The key points are:
- MongoDB shards or partitions data by a shard key, distributing data ranges across shards for scalability.
- A configuration server stores metadata about sharding setup and chunk distribution. Mongos instances route queries to appropriate shards.
- MongoDB automatically splits and migrates chunks as data grows to balance load across shards.
- Setting up sharding in MongoDB requires minimal configuration and provides a consistent interface like a single database.
Back to Basics Spanish Webinar 3 - Introducción a los replica setsMongoDB
Cómo crear un clúster de producción
Cómo crear un replica set
Cómo MongoDB gestiona la persistencia de los datos y cómo un conjunto de réplicas se recupera automáticamente de todo tipo de fallos
A New MongoDB Sharding Architecture for Higher Availability and Better Resour...leifwalsh
Most modern databases concern themselves with their ability to scale a workload beyond the power of one machine. But maintaining a database across multiple machines is inherently more complex than it is on a single machine. As soon as scaling out is required, suddenly a lot of scaling out is required, to deal with new problems like index suitability and load balancing.
Write optimized data structures are well-suited to a sharding architecture that delivers higher efficiency than traditional sharding architectures. This talk describes a new sharding architecture for MongoDB applications that can be achieved with write optimized storage like TokuMX's Fractal Tree indexes.
Historically, sharing a Linux server entailed all kinds of untenable compromises. In addition to the security concerns, there was simply no good way to keep one application from hogging resources and messing with the others. The classic “noisy neighbor” problem made shared systems the bargain-basement slums of the Internet, suitable only for small or throwaway projects.
Serious use-cases traditionally demanded dedicated systems. Over the past decade virtualization (in conjunction with Moore’s law) has democratized the availability of what amount to dedicated systems, and the result is hundreds of thousands of websites and applications deployed into VPS or cloud instances. It’s a step in the right direction, but still has glaring flaws.
Most of these websites are just piles of code sitting on a server somewhere. How did that code got there? How can it can be scaled? Secured? Maintained? It’s anybody’s guess. There simply isn’t enough SysAdmin talent in the world to meet the demands of managing all these apps with anything close to best practices without a better model.
Containers are a whole new ballgame. Unlike VMs, you skip the overhead of running an entire OS for every application environment. There’s also no need to provision a whole new machine to have a place to deploy, meaning you can spin up or scale your application with orders of magnitude more speed and accuracy.
MongoDB World 2016: From the Polls to the Trolls: Seeing What the World Think...MongoDB
YouGov uses MongoDB to store semi-structured survey response data across a globally distributed sharded cluster. They implement tag-aware sharding to partition data by region and leverage migration managers to update schemas across versions. This allows them to provide low-latency reads, scale throughput, and support dynamic surveys worldwide through MongoDB-as-a-Service.
Basic Sharding in MongoDB presented by Shaun VerchMongoDB
This document provides an introduction to MongoDB sharding. It discusses how sharding allows scaling of data and MongoDB's approach to sharding including architecture, configuration, and mechanics. Key points include how sharding partitions data and distributes it across multiple servers, the role of config servers, mongos routers, and shards, and considerations for choosing a shard key to effectively distribute data and queries.
Sharding in MongoDB allows for horizontal scaling of data and operations across multiple servers. When determining if sharding is needed, factors like available storage, query throughput, and response latency on a single server are considered. The number of shards can be calculated based on total required storage, working memory size, and input/output operations per second across servers. Different types of sharding include range, tag-aware, and hashed sharding. Choosing a high cardinality shard key that matches query patterns is important for performance. Reasons to shard include scaling to large data volumes and query loads, enabling local writes in a globally distributed deployment, and improving backup and restore times.
Redis is an open source, in-memory data structure store that can be used as a database, cache, or message broker. It supports data structures like strings, hashes, lists, sets, sorted sets with ranges and pagination. Redis provides high performance due to its in-memory storage and support for different persistence options like snapshots and append-only files. It uses client/server architecture and supports master-slave replication, partitioning, and failover. Redis is useful for caching, queues, and other transient or non-critical data.
Development to Production with Sharded MongoDB ClustersSeveralnines
Severalnines presentation at MongoDB Stockholm Conference.
Presentation covers:
- mongoDB sharding/clustering concepts
- recommended dev/test/prod setups
- how to verify your deployment
- how to avoid downtime
- what MongoDB metrics to watch
- when to scale
In this webinar, we will be covering general best practices for running MongoDB on AWS.
Topics will range from instance selection to storage selection and service distribution to ensure service availability. We will also look at any specific best practices related to using WiredTiger. We will then shift gears and explore recommended strategies for managing your MongoDB instance on AWS.
This session also includes a live Q&A portion during which you are encouraged to ask questions of our team.
This document discusses MongoDB replication, sharding, and aggregation. It explains that replica sets allow for high availability and redundancy through primary-secondary replication across multiple servers. Sharding partitions data by shard key across multiple replica sets to scale databases horizontally. The aggregation framework provides data aggregation capabilities through pipelines of operations like match, project, group, and sort.
Redundancy and high availability are the basis for all production deployments. With MongoDB this can be achieved by deploying replica set. In this slides we are exploring how the replication works with MongoDB, why you should use replication, what are the features and go over different deployment use cases. At the end we are comparing some features with MySQL replication and what are the differences between the two
Redis is a key-value store that can be used as a database, cache, and message broker. It supports basic data structures like strings, hashes, lists, sets, sorted sets with operations that are fast thanks to storing the entire dataset in memory. Redis also provides features like replication, transactions, pub/sub messaging and can be used for caching, queueing, statistics and inter-process communication.
Optimizing MongoDB: Lessons Learned at LocalyticsBenjamin Darfler
Benjamin Darfler presented lessons learned from optimizing MongoDB at Localytics to handle their increasing data and query loads. Some key optimizations included shortening document names, using binary data types for IDs, pre-aggregating data, creating covering indexes, and choosing a temporal field as the shard key. Hardware optimizations involved using larger EC2 instance types and RAIDing multiple large EBS volumes to reduce fragmentation during migrations. Testing changes thoroughly was emphasized.
MongoDB San Francisco 2013: Basic Sharding in MongoDB presented by Brandon Bl...MongoDB
Sharding allows you to distribute load across multiple servers and keep your data balanced across those servers. This session will review MongoDB’s sharding support, including an architectural overview, design principles, and automation.
Understanding how memory is managed with MongoDB is instrumental in maximizing database performance and hardware utilisation. This talk covers the workings of low level operating system components like the page cache and memory mapped files. We will examine the differences between RAM, SSD and hard disk drives to help you choose the right hardware configuration. Finally, we will learn how to monitor and analyze memory and disk usage using the MongoDB Management Service, linux administration commands and MongoDB commands.
MongoDB auto sharding allows data to be automatically partitioned and distributed across multiple servers (shards) in a MongoDB cluster. The sharding process distributes data by a shard key, automatically balancing data as the system load changes. Queries are routed to the appropriate shards and can be executed in parallel across shards to improve performance. The config servers store metadata about shards and chunk distribution to enable auto sharding functionality.
The document discusses several challenges with MongoDB, including:
1. MongoDB uses a global write lock, which can negatively impact write performance.
2. Auto-sharding in MongoDB is not always reliable, as the balancer can get into deadlocks and MongoDB has trouble determining the number of documents after sharding.
3. Being schema-less is overrated, as it means repeating the schema in each document, increasing storage size. Possible solutions discussed include using shorter key names to reduce document sizes.
Back to Basics German 3: Einführung in Replica SetsMongoDB
Wie Sie ein Cluster für eine Produktionsumgebung erstellen
Wie Sie ein Replica Set anlegen
Wie MongoDB für die Datenpersistenz sorgt und wie ein Replica Set den Betrieb nach einem Ausfall automatisch wieder aufnimmt
Back to Basics Webinar 3: Introduction to Replica SetsMongoDB
This document provides an introduction to MongoDB replica sets, which allow for data redundancy and high availability. It discusses how replica sets work, including the replica set life cycle and how applications should handle writes and queries when using a replica set. Specifically, it explains that the MongoDB driver is responsible for server discovery and monitoring, retry logic, and handling topology changes in a replica set to provide a consistent view of the data to applications.
Setting up mongodb sharded cluster in 30 minutesSudheer Kondla
The document describes how to configure and deploy a MongoDB sharded cluster with 6 virtual machines in 30 minutes. It provides step-by-step instructions on installing MongoDB, setting up the config servers, adding shards, and enabling sharding for databases and collections. Key aspects include designating MongoDB instances as config servers, starting mongos processes connected to the config servers, adding shards by hostname and port, and enabling sharding on specific databases and collections with shard keys.
The document provides an overview of MongoDB sharding, including:
- Sharding allows horizontal scaling of data by partitioning a database across multiple servers or shards.
- The MongoDB sharding architecture consists of shards to hold data, config servers to store metadata, and mongos processes to route requests.
- Data is partitioned into chunks based on a shard key and chunks can move between shards as the data distribution changes.
This document provides an overview of MongoDB sharding. It discusses how MongoDB addresses the need for horizontal scalability as data and throughput needs exceed the capabilities of a single machine. MongoDB uses sharding to partition data across multiple machines or shards. The key points are:
- MongoDB shards or partitions data by a shard key, distributing data ranges across shards for scalability.
- A configuration server stores metadata about sharding setup and chunk distribution. Mongos instances route queries to appropriate shards.
- MongoDB automatically splits and migrates chunks as data grows to balance load across shards.
- Setting up sharding in MongoDB requires minimal configuration and provides a consistent interface like a single database.
Back to Basics Spanish Webinar 3 - Introducción a los replica setsMongoDB
Cómo crear un clúster de producción
Cómo crear un replica set
Cómo MongoDB gestiona la persistencia de los datos y cómo un conjunto de réplicas se recupera automáticamente de todo tipo de fallos
A New MongoDB Sharding Architecture for Higher Availability and Better Resour...leifwalsh
Most modern databases concern themselves with their ability to scale a workload beyond the power of one machine. But maintaining a database across multiple machines is inherently more complex than it is on a single machine. As soon as scaling out is required, suddenly a lot of scaling out is required, to deal with new problems like index suitability and load balancing.
Write optimized data structures are well-suited to a sharding architecture that delivers higher efficiency than traditional sharding architectures. This talk describes a new sharding architecture for MongoDB applications that can be achieved with write optimized storage like TokuMX's Fractal Tree indexes.
Historically, sharing a Linux server entailed all kinds of untenable compromises. In addition to the security concerns, there was simply no good way to keep one application from hogging resources and messing with the others. The classic “noisy neighbor” problem made shared systems the bargain-basement slums of the Internet, suitable only for small or throwaway projects.
Serious use-cases traditionally demanded dedicated systems. Over the past decade virtualization (in conjunction with Moore’s law) has democratized the availability of what amount to dedicated systems, and the result is hundreds of thousands of websites and applications deployed into VPS or cloud instances. It’s a step in the right direction, but still has glaring flaws.
Most of these websites are just piles of code sitting on a server somewhere. How did that code got there? How can it can be scaled? Secured? Maintained? It’s anybody’s guess. There simply isn’t enough SysAdmin talent in the world to meet the demands of managing all these apps with anything close to best practices without a better model.
Containers are a whole new ballgame. Unlike VMs, you skip the overhead of running an entire OS for every application environment. There’s also no need to provision a whole new machine to have a place to deploy, meaning you can spin up or scale your application with orders of magnitude more speed and accuracy.
MongoDB World 2016: From the Polls to the Trolls: Seeing What the World Think...MongoDB
YouGov uses MongoDB to store semi-structured survey response data across a globally distributed sharded cluster. They implement tag-aware sharding to partition data by region and leverage migration managers to update schemas across versions. This allows them to provide low-latency reads, scale throughput, and support dynamic surveys worldwide through MongoDB-as-a-Service.
Basic Sharding in MongoDB presented by Shaun VerchMongoDB
This document provides an introduction to MongoDB sharding. It discusses how sharding allows scaling of data and MongoDB's approach to sharding including architecture, configuration, and mechanics. Key points include how sharding partitions data and distributes it across multiple servers, the role of config servers, mongos routers, and shards, and considerations for choosing a shard key to effectively distribute data and queries.
Sharding in MongoDB allows for horizontal scaling of data and operations across multiple servers. When determining if sharding is needed, factors like available storage, query throughput, and response latency on a single server are considered. The number of shards can be calculated based on total required storage, working memory size, and input/output operations per second across servers. Different types of sharding include range, tag-aware, and hashed sharding. Choosing a high cardinality shard key that matches query patterns is important for performance. Reasons to shard include scaling to large data volumes and query loads, enabling local writes in a globally distributed deployment, and improving backup and restore times.
Redis is an open source, in-memory data structure store that can be used as a database, cache, or message broker. It supports data structures like strings, hashes, lists, sets, sorted sets with ranges and pagination. Redis provides high performance due to its in-memory storage and support for different persistence options like snapshots and append-only files. It uses client/server architecture and supports master-slave replication, partitioning, and failover. Redis is useful for caching, queues, and other transient or non-critical data.
Development to Production with Sharded MongoDB ClustersSeveralnines
Severalnines presentation at MongoDB Stockholm Conference.
Presentation covers:
- mongoDB sharding/clustering concepts
- recommended dev/test/prod setups
- how to verify your deployment
- how to avoid downtime
- what MongoDB metrics to watch
- when to scale
In this webinar, we will be covering general best practices for running MongoDB on AWS.
Topics will range from instance selection to storage selection and service distribution to ensure service availability. We will also look at any specific best practices related to using WiredTiger. We will then shift gears and explore recommended strategies for managing your MongoDB instance on AWS.
This session also includes a live Q&A portion during which you are encouraged to ask questions of our team.
This document discusses MongoDB replication, sharding, and aggregation. It explains that replica sets allow for high availability and redundancy through primary-secondary replication across multiple servers. Sharding partitions data by shard key across multiple replica sets to scale databases horizontally. The aggregation framework provides data aggregation capabilities through pipelines of operations like match, project, group, and sort.
Redundancy and high availability are the basis for all production deployments. With MongoDB this can be achieved by deploying replica set. In this slides we are exploring how the replication works with MongoDB, why you should use replication, what are the features and go over different deployment use cases. At the end we are comparing some features with MySQL replication and what are the differences between the two
Redis is a key-value store that can be used as a database, cache, and message broker. It supports basic data structures like strings, hashes, lists, sets, sorted sets with operations that are fast thanks to storing the entire dataset in memory. Redis also provides features like replication, transactions, pub/sub messaging and can be used for caching, queueing, statistics and inter-process communication.
Optimizing MongoDB: Lessons Learned at LocalyticsBenjamin Darfler
Benjamin Darfler presented lessons learned from optimizing MongoDB at Localytics to handle their increasing data and query loads. Some key optimizations included shortening document names, using binary data types for IDs, pre-aggregating data, creating covering indexes, and choosing a temporal field as the shard key. Hardware optimizations involved using larger EC2 instance types and RAIDing multiple large EBS volumes to reduce fragmentation during migrations. Testing changes thoroughly was emphasized.
MongoDB San Francisco 2013: Basic Sharding in MongoDB presented by Brandon Bl...MongoDB
Sharding allows you to distribute load across multiple servers and keep your data balanced across those servers. This session will review MongoDB’s sharding support, including an architectural overview, design principles, and automation.
Understanding how memory is managed with MongoDB is instrumental in maximizing database performance and hardware utilisation. This talk covers the workings of low level operating system components like the page cache and memory mapped files. We will examine the differences between RAM, SSD and hard disk drives to help you choose the right hardware configuration. Finally, we will learn how to monitor and analyze memory and disk usage using the MongoDB Management Service, linux administration commands and MongoDB commands.
MongoDB auto sharding allows data to be automatically partitioned and distributed across multiple servers (shards) in a MongoDB cluster. The sharding process distributes data by a shard key, automatically balancing data as the system load changes. Queries are routed to the appropriate shards and can be executed in parallel across shards to improve performance. The config servers store metadata about shards and chunk distribution to enable auto sharding functionality.
The document discusses several challenges with MongoDB, including:
1. MongoDB uses a global write lock, which can negatively impact write performance.
2. Auto-sharding in MongoDB is not always reliable, as the balancer can get into deadlocks and MongoDB has trouble determining the number of documents after sharding.
3. Being schema-less is overrated, as it means repeating the schema in each document, increasing storage size. Possible solutions discussed include using shorter key names to reduce document sizes.
Back to Basics German 3: Einführung in Replica SetsMongoDB
Wie Sie ein Cluster für eine Produktionsumgebung erstellen
Wie Sie ein Replica Set anlegen
Wie MongoDB für die Datenpersistenz sorgt und wie ein Replica Set den Betrieb nach einem Ausfall automatisch wieder aufnimmt
Back to Basics Webinar 3: Introduction to Replica SetsMongoDB
This document provides an introduction to MongoDB replica sets, which allow for data redundancy and high availability. It discusses how replica sets work, including the replica set life cycle and how applications should handle writes and queries when using a replica set. Specifically, it explains that the MongoDB driver is responsible for server discovery and monitoring, retry logic, and handling topology changes in a replica set to provide a consistent view of the data to applications.
MongoDB World 2018: Active-Active Application Architectures: Become a MongoDB...MongoDB
MongoDB can be configured to meet the requirements of active-active applications across multiple data centers. There are three main deployment patterns: 1) active-passive with one data center as primary, 2) partitioned databases with each data center owning a partition, and 3) multi-master with each data center acting as a master. The document discusses how to tune MongoDB for performance, consistency, availability, and durability using features like sharding, read preference, write concern, and causal consistency.
Purpose of the session is to have a dive into Apache, Kafka, Data Streaming and Kafka in the cloud
- Dive into Apache Kafka
- Data Streaming
- Kafka in the cloud
Grokking Techtalk #40: Consistency and Availability tradeoff in database clusterGrokking VN
Những năm gần đây, cùng với sự bùng nổ của các startup cùng các loại công nghệ như máy học, lượng dữ liệu phát sinh cần thu thập và xử lý trong các hệ thống ngày càng tăng cao.
Chính vì vậy, đối với các hệ thống lớn thì việc lưu trữ và xử lý dữ liệu trên một node database đã không đáp ứng được nữa, đòi hỏi phải sử dụng nhiều node kết nối với nhau để hình thành database cluster.
Đối với các database cluster nói riêng và hệ thống Distributed System nói chung, có khá nhiều chủ đề thú vị để đào sâu. Trong buổi thảo luận này, chúng ta sẽ giới hạn trong việc khảo sát về cách ba hệ thống Redis, Elastic Search và Cassandra tổ chức cluster cũng như sự trade-off giữa tính nhất quán (consistency) và khả năng đáp ứng (availability) của ba hệ thống này.
- Speaker: Lộc Võ - Lead Software Engineer @ Grab
JDD2015: Make your world event driven - Krzysztof DębskiPROIDEA
MAKE YOUR WORLD EVENT DRIVEN
Just after you set up your first microservice you realize that the game has just started. You need to improve latency in your application and reduce unnecessary communication.
To make your architecture fully decoupled you need to embrace asynchronous communication. Good way to achieve that is to switch to Event Driven Architecture.
We will see how to use Kafka in your microservices. We will also cover some pitfalls you might face during using Kafka and how to deal with them.
After the talk you will know the toolset that are need to improve your microservice ecosystem.
This document discusses advanced replication in MongoDB replica sets. It begins by explaining the roles and configuration of replica set members, including primaries, secondaries, and arbiters. It then covers the implementation details of replication using an oplog and heartbeats between nodes. The rest of the document discusses best practices for maintenance, data center awareness using tagging, write concerns, and read preferences to control data distribution and failover.
This document summarizes techniques for scaling MongoDB deployments, including:
- Single server read/write scaling using techniques like denormalization, indexing, and restricting fields
- Scaling reads using master-slave replication and replica sets for improved availability and read scaling
- Scaling reads and writes using sharding to partition data across multiple servers and distribute load
The document discusses OrientDB, a multi-model NoSQL database that supports document, key-value, and graph structures. It highlights several of OrientDB's features, including its support for relationships without joins, complex types, ACID transactions, and its RESTful HTTP interface. The document also briefly describes OrientDB's indexing, security, multi-master replication, and use of a graph database model.
OrientDB is a multi-model NoSQL database that combines features of graph databases, document databases, and relational databases. It supports ACID transactions, SQL queries, and a native graph data model with features like relationships, complex types, and schema-less flexibility. OrientDB is open source, lightweight, and easy to configure and use.
In the big data world, our data stores communicate over an asynchronous, unreliable network to provide a facade of consistency. However, to really understand the guarantees of these systems, we must understand the realities of networks and test our data stores against them.
Jepsen is a tool which simulates network partitions in data stores and helps us understand the guarantees of our systems and its failure modes. In this talk, I will help you understand why you should care about network partitions and how can we test datastores against partitions using Jepsen. I will explain what Jepsen is and how it works and the kind of tests it lets you create. We will try to understand the subtleties of distributed consensus, the CAP theorem and demonstrate how different data stores such as MongoDB, Cassandra, Elastic and Solr behave under network partitions. Finally, I will describe the results of the tests I wrote using Jepsen for Apache Solr and discuss the kinds of rare failures which were found by this excellent tool.
This document provides an overview of the Google File System (GFS). It describes the key components of GFS including the master server, chunkservers, and clients. The master manages metadata like file namespaces and chunk mappings. Chunkservers store file data in 64MB chunks that are replicated across servers. Clients read and write chunks through the master and chunkservers. GFS provides high throughput and fault tolerance for Google's massive data storage and analysis needs.
MongoDB 101 & Beyond: Get Started in MongoDB 3.0, Preview 3.2 & Demo of Ops M...MongoDB
This document summarizes new features in MongoDB versions 3.0, 3.2 and how Ops Manager can help manage MongoDB deployments. Key points include:
- MongoDB 3.0 introduces pluggable storage engines like WiredTiger which offers improved write performance over MMAPv1 through document-level concurrency and built-in compression.
- Ops Manager provides automation for tasks like zero downtime cluster upgrades, ensuring availability and best practices. It reduces management overhead.
- MongoDB 3.2 features include faster failovers, support for more data centers, new aggregation stages, encryption at rest, partial and document level validation indexes.
- Compass is a new GUI for visualizing data and performing common operations
A Deep Dive into Apache Cassandra for .NET DevelopersLuke Tillman
.NET developers have a lot of options when it comes to databases these days. Apache Cassandra is a scalable, fault-tolerant database that has already found its way into more than 25% of the Fortune 100 and continues to grow in popularity. But what makes it different from the myriad of other options available? In this talk, we’ll take a deep dive into Cassandra and learn about:
- Cassandra’s internals and how it works
- CQL (the SQL-like query language for Cassandra)
- Data Modeling like a pro
- Tools available for developers
- Writing .NET code that talks to Cassandra
If there’s time and interest, we’ll finish up with how some companies are already using Cassandra to power services you probably interact with in your daily life. You’ll leave with all the tools you need to start build highly available .NET applications and services on top of Cassandra.
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kevin Lynch
In this presentation I talk about our motivation to converting our microservices to run on Kubernetes. I discuss many of the technical challenges we encountered along the way, including networking issues, Java issues, monitoring and alerting, and managing all of our resources!
This document provides documentation for Percona XtraDB Cluster, an open-source high availability and scalability solution for MySQL users. It includes sections on installation from binaries or source code, key features like high availability and multi-master replication, FAQs, how-tos, limitations, and other documentation. Percona XtraDB Cluster provides synchronous replication across multiple MySQL/Percona Server nodes, allowing for high availability and the ability to write to any node.
BSides LV 2016 - Beyond the tip of the iceberg - fuzzing binary protocols for...Alexandre Moneger
This presentation shows that code coverage guided fuzzing is possible in the context of network daemon fuzzing.
Some fuzzers are blackbox while others are protocol aware. Even ones which are made protocol aware, fuzzer writers typically model the protocol specification and implement packet awareness logic in the fuzzer. Unfortunately, just because the fuzzer is protocol aware, it does not guarantee that sufficient code paths have been reached.
The presentation deals with specific scenarios where the target protocol is completely unknown (proprietary) and no source code or protocol specs are accessible. The tool developed builds a feedback loop between the client and the server components using the concept of "gate functions". A gate function triggers monitoring. The pintool component tracks the binary code coverage for all the functions untill it reaches an exit gate. By instrumenting such gated functions, the tool is able to measure code coverage during packet processing.
These are the slides I presented at the Nosql Night in Boston on Nov 4, 2014. The slides were adapted from a presentation given by Steve Francia in 2011. Original slide deck can be found here:
http://spf13.com/presentation/mongodb-sort-conference-2011
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Webinar Back to Basics 3 - Introduzione ai Replica Set
1.
2. Back to Basics 2017 : Webinar 3
Introduzione ai Replica Set
Massimo Brignoli
Principal Solutions Architect
MongoDB
@massimobrignoli
massimo@mongodb.com
V1.0
3. Riassunto della Parti 1 e 2
• Perché esistono i NoSQL
• I Tipi di database NoSQL
• Le funzionalità chiave di MongoDB
• Come installare MongoDB
• Come eseguire le operazioni CRUD di base
• Come creare gli indici
• Come usare la funzione explain()
• MongoDB Compass e MongoDB Atlas
4. Agenda
• Durabilità dei Dati
• L’approccio di MongoDB: i Replica Set
• Il ciclo di vita di un Replica Set
• Come scrivere codice quando si usa un replica set
5. Replica Sets
• Replica set – da 2 a 50 repliche
• Replica sets crea un self-healing ‘shard’
• Data center awareness
• Replica sets indirizza:
• Alta affidabilità
• Durabilità e Consistenza
• Manutenzione (e.g., HW swaps)
• Disaster Recovery
Application
Driver
Primary
Secondary
Secondary
Replication
6. Replica Sets – Workload Isolation
• Replica sets abilitano la separazione dei workload
• Esempio: workload operazionale sul nodo primario e il
workload analitico sui nodi secondari
eCommerce Application
MongoDB Primary
In-memory Storage Engine
MongoDB Secondary
WiredTiger Storage Engine
User Data
Sessions, Cart,
Recommendations
MongoDB Secondary
WiredTiger Storage Engine
Persisted
User Data
17. Improved Tunable Consistency
• maxStalenessMS
• Decide come e quando reindirizzare le query alle repliche
secondarie
• Legge dalle repliche solo se sono all’interno di una finestra di
consistenza definita
• Migliora la qualità dei dati mentre si scalano le letture sui
secondari
• readConcern “linearizable” per la consistenza più forte
• Assicura che un nodo sia il primario al momentEnsure that a o
della lettura
• Assicura che I dati ritornati non saranno mai rollbackati se un
altro nodo è eletto come primario
59. Approfondimenti
• Jess Jiryu Davis ha una versione approfondita di questo talk
https://emptysqua.re/blog/server-discovery-and-monitoring-in-mongodb-
drivers/
• Specifiche del server discovery e monitoring
https://github.com/mongodb/specifications/blob/master/source/server-
discovery-and-monitoring/server-discovery-and-monitoring.rst
60. Ultimo Webinar : Sharding
• Come costruire un cluster altamente scalabile e performante
• Come rimuovere i colli di bottiglia in scrittura
• Come scegliere la chiave di partizionamento
Martedì, 27 Giugno 2017, ore 11:00.
High Availability – Ensure application availability during many types of failures
Meet stringent SLAs with fast-failover algorithm
Under 2 seconds to detect and recover from replica set primary failure
Disaster Recovery – Address the RTO and RPO goals for business continuity
Maintenance – Perform upgrades and other maintenance operations with no application downtime
Secondaries can be used for a variety of applications – failover, hot backup, rolling upgrades, data locality and privacy and workload isolation
Present a native language interface - converts python types to BSON objects
Convert the JSON query language into commands for the database
Convert JSON data into BSON data and vice-versa
Handles interfacing to different MongoDB topologies
Helps recover from server side outages/network errors
Manages the client side connection pool
The pymongo driver code is on Github (Apache License)
Calls i
Calls i
State machine, full set of states defined in spec.
Calls i
Calls i
Calls i
Needs a primary to complete a write.
Needs a primary to complete a write.
Needs a primary to complete a write.
Needs a primary to complete a write.
Each thread wakes every 10 seconds. Runs ismaster, sleeps.
We use ismaster to check latency.
Keep topology description up to date.
Each thread wakes every 10 seconds. Runs ismaster, sleeps.
We use ismaster to check latency.
Keep topology description up to date.
Each thread wakes every 10 seconds. Runs ismaster, sleeps.
We use ismaster to check latency.
Keep topology description up to date.
Primary is marked as unknown
Wakes up all monitor threads to check for a primary.
Primary is marked as unknown
Wakes up all monitor threads to check for a primary every half second.
Primary is marked as unknown
Wakes up all monitor threads to check for a primary every half second.
Each thread wakes every 10 seconds. Runs ismaster, sleeps.
We use ismaster to check latency.
Keep topology description up to date.
Try once. This will accomdate elections. Other errore should be propagated.
Try once. This will accomdate elections. Other errore should be propagated.
Can you afford to over or under count.
Operations need to be idempotent.
Turn an update into a write of a document, cf EventSourcing.
Then aggregate on the server.