Symfony Cache has been around for a few releases. But what is happening behind the scenes? Talk focuses on how is it working, down to detail level on Redis for things like datatypes, Redis Cluster sharding logic, how it differs from Memcached and more.
Hopefully you’ll learn how you can make sure to get optimal performance, what opportunities exists, and which pitfalls to try to avoid.
https://amsterdam2019.symfony.com/speakers
NOTE: This talk is recorded and available on SymfonyCasts, in the future it will also be uploaded to youtube.
Demystifying the Distributed Database Landscape (DevOps) (1).pdfScyllaDB
What is the state of high-performance, distributed databases as we head deeper into 2022, and which options are best suited for your own development projects?
The data-intensive applications leading this next tech cycle are typically powered by multiple types of databases and data stores—each satisfying specific needs and often interacting with a broader data ecosystem. Even the very notion of a database is evolving as new hardware architectures and methodologies allow for ever-greater capabilities and expectations for horizontal and vertical scalability, performance and reliability.
In this webinar, Peter Corless, ScyllaDB’s director of technology advocacy, will survey the current landscape of distributed database systems and highlight new directions in the industry.
This talk will cover different database and database-adjacent technologies as well as describe their appropriate use cases, patterns and anti-patterns with a focus on:
- Distributed SQL, NewSQL and NoSQL
- In-memory datastores and caches
- Streaming technologies with persistent data storage
Meta/Facebook's database serving social workloads is running on top of MyRocks (MySQL on RocksDB). This means our performance and reliability depends a lot on RocksDB. Not just MyRocks, but also we have other important systems running on top of RocksDB. We have learned many lessons from operating and debugging RocksDB at scale.
In this session, we will offer an overview of RocksDB, key differences from InnoDB, and share a few interesting lessons learned from production.
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
Big Data means big hardware, and the less of it we can use to do the job properly, the better the bottom line. Apache Kafka makes up the core of our data pipelines at many organizations, including LinkedIn, and we are on a perpetual quest to squeeze as much as we can out of our systems, from Zookeeper, to the brokers, to the various client applications. This means we need to know how well the system is running, and only then can we start turning the knobs to optimize it. In this talk, we will explore how best to monitor Kafka and its clients to assure they are working well. Then we will dive into how to get the best performance from Kafka, including how to pick hardware and the effect of a variety of configurations in both the broker and clients. We’ll also talk about setting up Kafka for no data loss.
Free Load Testing Tools for Oracle Database – Which One Do I Use?Christian Antognini
It regularly happens to me that for testing purposes I have to generate load on an Oracle Database. The three most common situations leading to such a task are when I need to: assess the performance of a new platform or storage subsystem; verify whether a set of SQL statements executed on a specific environment and/or configuration fulfils the expected performance requirements; perform usability and functionality checks of tools or utilities that require a non-trivial load to be carried out. The aim of this presentation is to introduce the freely available tools that I use, to explain how I use them, and to present real-world use cases of their utilization.
Demystifying the Distributed Database Landscape (DevOps) (1).pdfScyllaDB
What is the state of high-performance, distributed databases as we head deeper into 2022, and which options are best suited for your own development projects?
The data-intensive applications leading this next tech cycle are typically powered by multiple types of databases and data stores—each satisfying specific needs and often interacting with a broader data ecosystem. Even the very notion of a database is evolving as new hardware architectures and methodologies allow for ever-greater capabilities and expectations for horizontal and vertical scalability, performance and reliability.
In this webinar, Peter Corless, ScyllaDB’s director of technology advocacy, will survey the current landscape of distributed database systems and highlight new directions in the industry.
This talk will cover different database and database-adjacent technologies as well as describe their appropriate use cases, patterns and anti-patterns with a focus on:
- Distributed SQL, NewSQL and NoSQL
- In-memory datastores and caches
- Streaming technologies with persistent data storage
Meta/Facebook's database serving social workloads is running on top of MyRocks (MySQL on RocksDB). This means our performance and reliability depends a lot on RocksDB. Not just MyRocks, but also we have other important systems running on top of RocksDB. We have learned many lessons from operating and debugging RocksDB at scale.
In this session, we will offer an overview of RocksDB, key differences from InnoDB, and share a few interesting lessons learned from production.
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
Big Data means big hardware, and the less of it we can use to do the job properly, the better the bottom line. Apache Kafka makes up the core of our data pipelines at many organizations, including LinkedIn, and we are on a perpetual quest to squeeze as much as we can out of our systems, from Zookeeper, to the brokers, to the various client applications. This means we need to know how well the system is running, and only then can we start turning the knobs to optimize it. In this talk, we will explore how best to monitor Kafka and its clients to assure they are working well. Then we will dive into how to get the best performance from Kafka, including how to pick hardware and the effect of a variety of configurations in both the broker and clients. We’ll also talk about setting up Kafka for no data loss.
Free Load Testing Tools for Oracle Database – Which One Do I Use?Christian Antognini
It regularly happens to me that for testing purposes I have to generate load on an Oracle Database. The three most common situations leading to such a task are when I need to: assess the performance of a new platform or storage subsystem; verify whether a set of SQL statements executed on a specific environment and/or configuration fulfils the expected performance requirements; perform usability and functionality checks of tools or utilities that require a non-trivial load to be carried out. The aim of this presentation is to introduce the freely available tools that I use, to explain how I use them, and to present real-world use cases of their utilization.
MySQL Replication Performance Tuning for Fun and Profit!Vitor Oliveira
MySQL Replication, in addition to bringing high-availability, is the foundation to build high-performance MySQL database systems. Using read scale-out and sharding one can design systems that go from the capacity of a single server to supporting the largest internet sites. But to design and operate high-performance, efficient, manageable and reliable deployments requires knowing the intricacies of the underlying technologies.
This session will provide insights on the main factors that affect the performance of Asynchronous Replication and Group Replication, and how to configure them to make the most out of the underlying computing system. It will also show the latest developments in MySQL 5.7 and 8.0, in areas spanning from group communication to the multi-threaded slave applier, and how effective they are in helping meet the performance requirements in terms of throughput, latency and durability to support the most demanding workload types.
PostgreSQL is a very popular and feature-rich DBMS. At the same time, PostgreSQL has a set of annoying wicked problems, which haven't been resolved in decades. Miraculously, with just a small patch to PostgreSQL core extending this API, it appears possible to solve wicked PostgreSQL problems in a new engine made within an extension.
Slides from my talk at Cassandra Summit 2016 on troubleshooting Cassandra. This is a reprise of my popular talk from last summit, reorganized, expanded, and updated for Cassandra 3.0. In it I share the secrets I've learned in four years of supporting hundreds of customers using Apache Cassandra and DataStax Enterprise. Be sure to check out presenter notes for additional tips and links to further resources.
Webinar: Detecting row patterns with Flink SQL - Dawid WysakowiczVerverica
Apache Flink is one of the first open source stream processors that was able to address the full spectrum of stream processing applications, ranging from applications with low latency requirements to applications that process millions of events per second. On top of this powerful processing engine, the Flink community built APIs for complex event processing and streaming analytics, namely the CEP library and support for streaming SQL.
Since recently, the Flink community is integrating both APIs by extending Flink SQL to support the MATCH RECOGNIZE clause for row pattern matching that was introduced with the SQL:2016 standard.
I will discuss the new MATCH RECOGNIZE feature and present use cases that benefit from pattern matching support in streaming SQL, such as process monitoring or anomaly detection. I will demonstrate the feature with a few example queries.
All about Zookeeper and ClickHouse Keeper.pdfAltinity Ltd
ClickHouse clusters depend on ZooKeeper to handle replication and distributed DDL commands. In this Altinity webinar, we’ll explain why ZooKeeper is necessary, how it works, and introduce the new built-in replacement named ClickHouse Keeper. You’ll learn practical tips to care for ZooKeeper in sickness and health. You’ll also learn how/when to use ClickHouse Keeper. We will share our recommendations for keeping that happy as well.
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
MySQL and MariaDB though they share the same roots for replication .They support parallel replication , but they diverge the way the parallel replication is implemented.
The Proxy Wars - MySQL Router, ProxySQL, MariaDB MaxScaleColin Charles
As proxies (and database routers) go, the first one I ever used was the now deprecated MySQL Proxy. Since then, I've managed to use MariaDB MaxScale quite a bit (including its fork AirBnB MaxScale), played around with ProxySQL in recent time, and also started taking a look at MySQL Router. In this quick 20-minute overview, we'll discuss why these three exist, a feature comparison, and reasons when to use the right tool for the job.
ClickHouse Monitoring 101: What to monitor and howAltinity Ltd
Webinar. Presented by Robert Hodges and Ned McClain, April 1, 2020
You are about to deploy ClickHouse into production. Congratulations! But what about monitoring? In this webinar we will introduce how to track the health of individual ClickHouse nodes as well as clusters. We'll describe available monitoring data, how to collect and store measurements, and graphical display using Grafana. We'll demo techniques and share sample Grafana dashboards that you can use for your own clusters.
A generic layer that can be used with many key-value storage engines like Redis, Memcached, LMDB, etc
Focus: performance, cross-datacenter active-active replication and high availability
Features: node warmup (cold bootstrapping), tunable consistency, S3 backups/restores
Status: Open source, fully integrated with existing NetflixOSS ecosystem
A talk about Open Source logging and monitoring tools, using the ELK stack (ElasticSearch, Logstash, Kibana) to aggregate logs, how to track metrics from systems and logs, and how Drupal.org uses the ELK stack to aggregate and process billions of logs a month.
Battle Of The Microservice Frameworks: Micronaut versus Quarkus edition! Michel Schudel
Micronaut and Quarkus are two cool emerging Java backend frameworks that aim to solve some problems that exist in current frameworks, like faster startup, low memory footprint, and support for ahead-of-time compilation using GraalVM. In this session, we'll square off both frameworks against each other.
How do they compare, what are the stronger and weaker points of both frameworks?
We'll compare the following features:
Initializing your project
Building your first restcontroller / programming model
Startup time
Database support
Integration test support
Building native images
Memory usage and JAR sizes
Ease of cloud deployment
In the end, we might have a clear winner! ... or will we?
Container Network Interface: Network Plugins for Kubernetes and beyondKubeAcademy
With the rise of modern containers comes new problems to solve – especially in networking. Numerous container SDN solutions have recently entered the market, each best suited for a particular environment. Combined with multiple container runtimes and orchestrators available today, there exists a need for a common layer to allow interoperability between them and the network solutions.
As different environments demand different networking solutions, multiple vendors and viewpoints look to a specification to help guide interoperability. Container Network Interface (CNI) is a specification started by CoreOS with the input from the wider open source community aimed to make network plugins interoperable between container execution engines. It aims to be as common and vendor-neutral as possible to support a wide variety of networking options — from MACVLAN to modern SDNs such as Weave and flannel.
CNI is growing in popularity. It got its start as a network plugin layer for rkt, a container runtime from CoreOS. Today rkt ships with multiple CNI plugins allowing users to take advantage of virtual switching, MACVLAN and IPVLAN as well as multiple IP management strategies, including DHCP. CNI is getting even wider adoption with Kubernetes adding support for it. Kubernetes accelerates development cycles while simplifying operations, and with support for CNI is taking the next step toward a common ground for networking. For continued success toward interoperability, Kubernetes users can come to this session to learn the CNI basics.
This talk will cover the CNI interface, including an example of how to build a simple plugin. It will also show Kubernetes users how CNI can be used to solve their networking challenges and how they can get involved.
KubeCon schedule link: http://sched.co/4VAo
SfDay 2019: Head first into Symfony Cache, Redis & Redis ClusterAndré Rømcke
Symfony Cache has been around for a few releases. But what has been happening behind the scenes? Talk focuses on how is it working, down to detail level on Redis for things like datatypes, Redis Cluster sharding logic, how it differs from Memcached and more.
MySQL Replication Performance Tuning for Fun and Profit!Vitor Oliveira
MySQL Replication, in addition to bringing high-availability, is the foundation to build high-performance MySQL database systems. Using read scale-out and sharding one can design systems that go from the capacity of a single server to supporting the largest internet sites. But to design and operate high-performance, efficient, manageable and reliable deployments requires knowing the intricacies of the underlying technologies.
This session will provide insights on the main factors that affect the performance of Asynchronous Replication and Group Replication, and how to configure them to make the most out of the underlying computing system. It will also show the latest developments in MySQL 5.7 and 8.0, in areas spanning from group communication to the multi-threaded slave applier, and how effective they are in helping meet the performance requirements in terms of throughput, latency and durability to support the most demanding workload types.
PostgreSQL is a very popular and feature-rich DBMS. At the same time, PostgreSQL has a set of annoying wicked problems, which haven't been resolved in decades. Miraculously, with just a small patch to PostgreSQL core extending this API, it appears possible to solve wicked PostgreSQL problems in a new engine made within an extension.
Slides from my talk at Cassandra Summit 2016 on troubleshooting Cassandra. This is a reprise of my popular talk from last summit, reorganized, expanded, and updated for Cassandra 3.0. In it I share the secrets I've learned in four years of supporting hundreds of customers using Apache Cassandra and DataStax Enterprise. Be sure to check out presenter notes for additional tips and links to further resources.
Webinar: Detecting row patterns with Flink SQL - Dawid WysakowiczVerverica
Apache Flink is one of the first open source stream processors that was able to address the full spectrum of stream processing applications, ranging from applications with low latency requirements to applications that process millions of events per second. On top of this powerful processing engine, the Flink community built APIs for complex event processing and streaming analytics, namely the CEP library and support for streaming SQL.
Since recently, the Flink community is integrating both APIs by extending Flink SQL to support the MATCH RECOGNIZE clause for row pattern matching that was introduced with the SQL:2016 standard.
I will discuss the new MATCH RECOGNIZE feature and present use cases that benefit from pattern matching support in streaming SQL, such as process monitoring or anomaly detection. I will demonstrate the feature with a few example queries.
All about Zookeeper and ClickHouse Keeper.pdfAltinity Ltd
ClickHouse clusters depend on ZooKeeper to handle replication and distributed DDL commands. In this Altinity webinar, we’ll explain why ZooKeeper is necessary, how it works, and introduce the new built-in replacement named ClickHouse Keeper. You’ll learn practical tips to care for ZooKeeper in sickness and health. You’ll also learn how/when to use ClickHouse Keeper. We will share our recommendations for keeping that happy as well.
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
MySQL and MariaDB though they share the same roots for replication .They support parallel replication , but they diverge the way the parallel replication is implemented.
The Proxy Wars - MySQL Router, ProxySQL, MariaDB MaxScaleColin Charles
As proxies (and database routers) go, the first one I ever used was the now deprecated MySQL Proxy. Since then, I've managed to use MariaDB MaxScale quite a bit (including its fork AirBnB MaxScale), played around with ProxySQL in recent time, and also started taking a look at MySQL Router. In this quick 20-minute overview, we'll discuss why these three exist, a feature comparison, and reasons when to use the right tool for the job.
ClickHouse Monitoring 101: What to monitor and howAltinity Ltd
Webinar. Presented by Robert Hodges and Ned McClain, April 1, 2020
You are about to deploy ClickHouse into production. Congratulations! But what about monitoring? In this webinar we will introduce how to track the health of individual ClickHouse nodes as well as clusters. We'll describe available monitoring data, how to collect and store measurements, and graphical display using Grafana. We'll demo techniques and share sample Grafana dashboards that you can use for your own clusters.
A generic layer that can be used with many key-value storage engines like Redis, Memcached, LMDB, etc
Focus: performance, cross-datacenter active-active replication and high availability
Features: node warmup (cold bootstrapping), tunable consistency, S3 backups/restores
Status: Open source, fully integrated with existing NetflixOSS ecosystem
A talk about Open Source logging and monitoring tools, using the ELK stack (ElasticSearch, Logstash, Kibana) to aggregate logs, how to track metrics from systems and logs, and how Drupal.org uses the ELK stack to aggregate and process billions of logs a month.
Battle Of The Microservice Frameworks: Micronaut versus Quarkus edition! Michel Schudel
Micronaut and Quarkus are two cool emerging Java backend frameworks that aim to solve some problems that exist in current frameworks, like faster startup, low memory footprint, and support for ahead-of-time compilation using GraalVM. In this session, we'll square off both frameworks against each other.
How do they compare, what are the stronger and weaker points of both frameworks?
We'll compare the following features:
Initializing your project
Building your first restcontroller / programming model
Startup time
Database support
Integration test support
Building native images
Memory usage and JAR sizes
Ease of cloud deployment
In the end, we might have a clear winner! ... or will we?
Container Network Interface: Network Plugins for Kubernetes and beyondKubeAcademy
With the rise of modern containers comes new problems to solve – especially in networking. Numerous container SDN solutions have recently entered the market, each best suited for a particular environment. Combined with multiple container runtimes and orchestrators available today, there exists a need for a common layer to allow interoperability between them and the network solutions.
As different environments demand different networking solutions, multiple vendors and viewpoints look to a specification to help guide interoperability. Container Network Interface (CNI) is a specification started by CoreOS with the input from the wider open source community aimed to make network plugins interoperable between container execution engines. It aims to be as common and vendor-neutral as possible to support a wide variety of networking options — from MACVLAN to modern SDNs such as Weave and flannel.
CNI is growing in popularity. It got its start as a network plugin layer for rkt, a container runtime from CoreOS. Today rkt ships with multiple CNI plugins allowing users to take advantage of virtual switching, MACVLAN and IPVLAN as well as multiple IP management strategies, including DHCP. CNI is getting even wider adoption with Kubernetes adding support for it. Kubernetes accelerates development cycles while simplifying operations, and with support for CNI is taking the next step toward a common ground for networking. For continued success toward interoperability, Kubernetes users can come to this session to learn the CNI basics.
This talk will cover the CNI interface, including an example of how to build a simple plugin. It will also show Kubernetes users how CNI can be used to solve their networking challenges and how they can get involved.
KubeCon schedule link: http://sched.co/4VAo
SfDay 2019: Head first into Symfony Cache, Redis & Redis ClusterAndré Rømcke
Symfony Cache has been around for a few releases. But what has been happening behind the scenes? Talk focuses on how is it working, down to detail level on Redis for things like datatypes, Redis Cluster sharding logic, how it differs from Memcached and more.
A presentation focused on the latest Storage API from Docker and integrating with an EMC {code} project called Rexray to provide container storage from EBS volumes.
Apidays Paris 2023 - Forget TypeScript, Choose Rust to build Robust, Fast and...apidays
Apidays Paris 2023 - Software and APIs for Smart, Sustainable and Sovereign Societies
December 6, 7 & 8, 2023
Forget TypeScript, Choose Rust to build Robust, Fast and Cheap APIs
Zacaria Chtatar, Backend Software Engineer at HaveSomeCode
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Amazon EC2 deepdive and a sprinkel of AWS Compute | AWS Floor28Amazon Web Services
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session we provide an overview of the Amazon EC2 instance platform key platform features and the concept of instance generations. We dive into the current generation design choices of the different instance families including the General Purpose Compute Optimized Storage Optimized Memory Optimized and GPU instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Смарт-контракты: базовые инструменты для разработки и тестирования. Спикер: Д...Cyber Fund
Смарт-контракты на Ethereum, ключевые инструменты разработки и тестирования - вопросы, которые раскроет Дмитрий Дудин
Данная презентация была представлена на одном из еженедельных образовательных митапов от компании cyber•Fund.
Посмотреть полное видео с митапа можно здесь:
https://www.youtube.com/watch?v=mtvkFzW3o3Y&t=2164s
Прочитать текстовую версию выступления:
https://golos.io/ru--blokcheijn/@cyberevents/smart-kontrakty-bazovye-instrumenty-dlya-razrabotki-i-testirovaniya-spiker-dmitrii-dudin
Также вы можете задать интересующие вопросы непосредственно спикеру:
twitter.com/nedudi
facebook.com/nedudi
vk.com/nedudi
Информация о компании cyber•Fund
Мы инвестируем и развиваем блокчейн проекты, способные кардинально менять наш мир в лучшую сторону, создавая экономику роботов и самовыражения людей. Больше о нашей работе вы можете узнать из следующих ресурсов:
Наши проекты:
сyber•Fund - аналитика и разработка блокчейн систем
Golos.io - медийная блокчейн платформа
Satoshi•Fund - первый фонд инвестирующий в криптоактивы
Cyberstudio.io - помощь в проведении ICO
Мы ждем вас в наших сообществах:
Блог:
https://blog.cyber.fund/
Email Newsletter:
http://company.cyber.fund/#newsletter
Социальные сети:
https://golos.io/@cyberfund
https://steemit.com/@cyberfund
https://twitter.com/cyberfundio
https://www.reddit.com/r/cyber_Fund/
https://www.facebook.com/cyberfund - официальная страница сyber•Fund
https://www.facebook.com/blockchainmeetups/ - официальная страница cyber•Events (Блокчейн митапы, конференции, доклады)
https://www.slideshare.net/CyberFund-Official
Для разработчиков:
https://t.me/CyberFundDev - telegram чат для блокчейн разработчиков
https://github.com/cyberFund - наш репозиторий на Github с open source software
https://github.com/cyberFund/Library - библиотека знаний по блокчейн
We're building a storage engine for MongoDB that provides encryption at rest. When we first set out to do this, the questions were many: how do you protect database encryption keys in a distributed environment, where all the code is open source? Can you optimize performance despite the extra steps of encryption and decryption? And most importantly, how do you make the protection mechanisms easy-to-use yet secure? This talk covers the requirements we gathered, the issues we faced, and the design decisions we made. It is aimed at those interested in security, storage engines, and the engineering process.
Caching and tuning fun for high scalabilityWim Godden
Caching has been a 'hot' topic for a few years. But caching takes more than merely taking data and putting it in a cache : the right caching techniques can improve performance and reduce load significantly. But we'll also look at some major pitfalls, showing that caching the wrong way can bring down your site. If you're looking for a clear explanation about various caching techniques and tools like Memcached, Nginx and Varnish, as well as ways to deploy them in an efficient way, this talk is for you.
Symfony live London 2018 - Take your http caching to the next level with xke...André Rømcke
Joind.in: https://joind.in/event/symfonylive-london-2018/take-your-http-caching-to-the-next-level-with-xkey--fastly
Demo application using Varnish xkey:
https://github.com/andrerom/sf-london-2018-httpcache-demo
Example of Fastly Proxy Client:
https://github.com/andrerom/sf-london-2018-httpcache-demo/tree/fastly
Developer insight into why applications run amazingly Fast in CF 2018Pavan Kumar
One of the Release goals of ColdFusion 2018 is improve the out of the box performance of the server to an extent that it becomes the best performing CFML engine out there. This talk delves into the Overall strategy adopted in going about measuring & improving performance. The Design challenges we confronted & resolved . The optimization we have done across various CFML constructs . We shall also delve into how a developer can leverage server features and configuration to further improve application performance. We shall also discuss and share the our performance metrics collected across various applications. In spite of having a high performing server one can still face issues so help on that we shall also demonstrate how developers can go about cracking performance bottle necks faced in their applications using tools available.
Peek behind the scenes to learn about Amazon ElastiCache's design and architecture. See common design patterns of our Memcached and Redis offerings and how customers have used them for in-memory operations and achieved improved latency and throughput for applications. During this session, we review best practices, design patterns, and anti-patterns related to Amazon ElastiCache.
This presentation was prepared for a Webcast where John Yerhot, Engine Yard US Support Lead, and Chris Kelly, Technical Evangelist at New Relic discussed how you can scale and improve the performance of your Ruby web apps. They shared detailed guidance on issues like:
Caching strategies
Slow database queries
Background processing
Profiling Ruby applications
Picking the right Ruby web server
Sharding data
Attendees will learn how to:
Gain visibility on site performance
Improve scalability and uptime
Find and fix key bottlenecks
See the on-demand replay:
http://pages.engineyard.com/6TipsforImprovingRubyApplicationPerformance.html
Similar to SymfonyCon 2019: Head first into Symfony Cache, Redis & Redis Cluster (20)
Look Towards 2.0 and Beyond - eZ Conference 2016André Rømcke
eZ Platform and eZ Studio: Where We Are, Where We Are Going, and a Look Towards 2.0 and Beyond
eZ’s next gen content management platform continues to evolve and there’s plenty more to come. How has the platform transformed and what are the major plans for the future? This talk will cover the spectrum, from the repository, the UI and our API-driven architecture to deployment via container-based technology and the move to Symfony3.
See also blog post where this was first announced: http://ezplatform.com/Blog/2.x-and-beyond-what-to-expect-next
Getting instantly up and running with Docker and SymfonyAndré Rømcke
A look into how you can start to use Docker today with ready made setup with php7, nginx, redis, blackfire and so on. How you may extend it, and integrating it into your continuous integration workflow, and how you can setup a continuous deployment workflow using for instance Travis-CI.
Quicklink: https://legacy.joind.in/19070
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Send an interactive Slack channel message (using buttons)
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And...
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Charlie Greenberg, Host
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2. Heatmap utilization for testing
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4. Demo
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Orchestrator execution result
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See how to accelerate model training and optimize model performance with active learning
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Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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SymfonyCon 2019: Head first into Symfony Cache, Redis & Redis Cluster
1. HEAD FIRST INTO SYMFONY
CACHE, REDIS & REDIS CLUSTER
André Rømcke (@andrerom)
VP Technical Services & Support @ eZ Systems (@ezsystems)
November 22nd 2019 - Amsterdam - SymfonyCon
2. So what is this talk about?
How Symfony 4.3/4.4 ended up
with new Adapters:
FilesystemTagAware &
RedisTagAware
Agenda:
1. Where eZ fits in
2. Some THEORY:
- Symfony Cache
- Cache tagging
- Redis & Redis Cluster
- Memcached vs Redis
5. New Adapters
6. Demo time
7. BONUS & Edge cases
3. Who?
๏André Rømcke | @andrerom
๏Economics, Consulting, Engineering, Lead, VP, now doing Services & Support
๏Tried to contribute to Symfony, FOS, Composer, PHP-FIG, Docker Compose
๏eZ Systems AS | ez.no
๏75+ people across 7+ countries. Partners & community in many many more
๏eZ Platform | ezplatform.com
๏Open Source CMS, feature rich, very extendable, flexible Full & Headless
๏On Symfony since 2012
๏Commercial with additional features: eZ Platform Enterprise & eZ Commerce
4. Symfony Cache in eZ Platform
๏Moved over from Stash in 2017
๏Heavily relies on Cache tagging feature
๏Contributed improvements on performance issues with Redis/Redis Cluster/…
๏… and for 4.3 /4.4: optimized TagAware adapters for Redis and FileSystem
6. Recap: Symfony Cache Component
๏PSR-6 compliant Cache component
๏Aims to be fast: Among others supports multi get calls to Redis and Memcached
๏Is progressively being used in several places in Symfony Framework, e.g.:
๏PropertyInfo
๏Serializer
๏Validator
๏(…)
๏.. maybe HTTP Cache at some point
9. Tags?
๏Data/Entities often has secondary indexes, e.g:
๏entity type id
๏placement id
๏variant type, …
๏Operations in your application sometimes affect those => Affecting bulk of entities
๏By tagging cache you can directly invalidate:
๏E.g. key: ez-content-66 tags: content-66, type-2, location-44 (…)
10. TagAware: Secondary index for invalidation
/**
* Interface for invalidating cached items using tags.
*
* @author Nicolas Grekas <p@tchwork.com>
*/
interface TagAwareAdapterInterface extends AdapterInterface
{
/**
* Invalidates cached items using tags.
*
* @param string[] $tags An array of tags to invalidate
*
* @return bool True on success
*
* @throws InvalidArgumentException When $tags is not valid
*/
public function invalidateTags(array $tags);
}
11. TagAwareAdapter
๏Wraps your normal adapter, stores item Tags in separate key
๏Does a separate lookup for expiry timestamp for tags => “2-RTT”
16. Redis Cluster: processe
๏Allows you to scale up Redis by running several instances
๏Multi process on same server and/or across servers
๏Coordinates cache across “cache slots", deals with replication, …
๏Unlike Memcached, several operations has limitations on Cluster
17. Redis Cluster: limitations
๏Does not support “pipline”: capability to perform several operations in one call
๏PHPRedis mainly supports multi operations on MGET and MSET with cluster
๏Examples of affected operations:
๏RENAME won’t work if the new key ends up in another “Cache Slot”
๏EVAL (Lua Script) likewise can only be given keys that maps to same node
ERR CROSSSLOT Keys in request don't hash to the same slot
19. Memcached vs Redis: Overview
๏Vivamus commodo ipsum in hendrerit iaculis.
๏Donec congue erat nibh, ac luctus erat accumsan tempor.
๏Mauris bibendum ac eros eu tempor. Duis libero libero, luctus quis posuere quis, porta vel
turpis.
๏Sed augue dolor, laoreet eget turpis eget, laoreet vehicula neque. Donec sit amet dolor vel
lorem ultrices facilisis ac sit amet velit.
๏Aenean nisi nisi, aliquet in pulvinar mollis, vulputate vitae nulla. Donec non ligula ac diam
volutpat dapibus.
๏Aliquam eleifend turpis id ligula accumsan luctus. Donec sem justo, scelerisque eget
condimentum ut, semper eget nulla.
๏Vivamus ultricies massa lectus, id varius orci sodales quis.
Memcached Redis
Multi operations (get, set, ..) V V
Datatypes String
String, List, Set, Hash, Sorted Set, …
Streams
Control over eviction X V
Persistance X V
Pipeline / Lua X V
Some limitations on Redis Cluster
Multiserver V V
Using e.g. Redis Cluster OR Redis Sentinel
Multithreaded V X
But multi process with Redis Cluster *
* Redis 6 is adding partly threading with background thread handling slow operations
20. New Adapters in Symfony 4.3/4.4:
FilesystemTagAware & RedisTagAware
21. Tags storage
๏Moves tags to be a “relation” to cache key, instead of expiry time
๏Avoids the lookup tags on getItem(s), instead does it on invalidation
➡ 1-RTT for lookups
๏FilesystemTagAwareAdapter: Uses a file for tag “relations”
๏RedisTagAwareAdapter: Uses “Set” for tag “relation” => w/o expiry
22. Why the efforts on 1-RTT lookups?
๏AWS ElasticCache Redis instances has latency of around 0.2-0.5ms
๏E.g. Simple page with 20 articles shown:
๏~40 lookups x latency = 5-20 ms
๏E.g. News site landing page with 1000 articles listed:
๏~2000 lookups x latency = 0.4 - 1 seconds
23. In Symfony Cache:
๏Pipeline used instead of MGET => Allows parallel lookups with Redis Cluster
๏Remove need for cache versioning lookups on Redis cluster
๏TagAwareAdapter micro ttl cache for tag lookups
Lookup optimizations done over the last year
24. On Application side (eZ Platform):
๏Changs take better advantage of Multi Get
๏Introduce optimized RedisTagAwareAdapter
๏Introduced Application specific in-memory cache
Lookup optimizations done over the last year
25. End result example:
๏Had 17.000 lookups in Symfony Cache on our Admin dashboard
๏Due to Symfony Cache logic this was ~40-60k lookups on Redis Cluster
๏30 seconds in worst case, just waiting for Redis Cluster
๏After all fixes it went down to 63 lookups in Symfony Cache
Lookup optimizations done over the last year
26. Demo time:
Lets adapt symfony/demo to make it cached
Demo code: https://github.com/andrerom/sfcon-amsterdam-2019-redis-cache-demo
28. Bonus: Edge cases in Caching
๏Race conditions
๏When caching entities: Transactions
๏Async / Stale cache
29. Bonus: Adapter recommendations
๏RedisTagAwareAdapter
Requirement: maxmemory-policy: volatile-ttl / volatile-lru / volatile-lfu (Redis 4.0+)
Pro: 1-RTT
Con: Consumes more memory, risk of running out of evictable memory
๏RedisAdapter + TagAwareAdapter:
Pro: Uses less memory then RedisTagAwareAdapter + all can be freed
Con: 2-RTT
๏MemcachedAdapter + TagAwareAdapter:
Pro: Uses less memory due to simpler data structure + all can be freed
Cable off handling more traffic
Con: 2-RTT
30. Bonus: Simulating RedisTagAware in bash
๏Save:
redis-cli set mykey data
redis-cli sadd type-article:tags mykey anotherkey
๏Read:
redis-cli mget mykey anotherkey
๏Invalidation:
tmp=$(cat /dev/urandom | tr -dc 'a-zA-Z0-9' | fold -w 32 | head -n 1)
redis-cli rename type-article:tags {type-article:tags}-$tmp
members=`redis-cli smembers {type-article:tags}-$tmp`
redis-cli del {type-article:tags}-$tmp $members
TIP:
To run these commands:
docker run --name myredis -p 6379:6379 -d redis
docker exec -ti myredis bash
After “exit”, you can clean up using:
docker rm -f myredis