These are the slides that the Redis Labs team had used to accompany the session that we gave during the first ever Redis Developers Day on October 2nd, 2014, London. It includes some of the ideas we've come up with to tackle operational challenges in the hyper-dense, multi-tenants Redis deployments that our service - Redis Cloud - consists of.
Redis Day Keynote Salvatore Sanfillipo Redis LabsRedis Labs
Redis' seventh birthday was recently celebrated with the community, several contributors and users. This is Salvatore's keynote as he kicked off Redis Day in Tel Aviv.
HIgh Performance Redis- Tague Griffith, GoProRedis Labs
High Performance Redis looks at a wide range of techniques - from programming to system tuning - to deploy and maintain an extremely high performing Redis cluster. From the operational
perspective, the talk lays out multiple techniques for clustering (sharding) Redis systems and examines how the different
approaches impact performance time. The talk further examines system settings (Linux network parameters, Redis
system) and how they impact performance (both good and bad). Finally, for the developer, we look at how different ways of structuring data actually demonstrate different performance characteristics
Redis is an advanced in memory key-value store designed for a world where "Memory is the new disk and disk is the new tape". Redis has some unique properties -- like blazing read and write speed, rich atomic operations and asynchronous persistence -- which make it ideally suited for a number of situations.
The tutorial includes an introduction to redis, data types used for redis, performance related to redis, sweet spots of redis, design consideration/best practices, adopters of redis. Begins with a section giving an introduction to redis which includes an introduction to redis and the features of redis. It also includes a brief history of redis, characteristics of redis, language support of redis. Following is a data types section. It includes the data types of redis like strings, lists, hashes, sets, sorted sets. It also includes not thinking of redis as an RDBMS, installation, atomicity of commands, key expiration.
Moreover, it also includes operations on lists, sets, sorted sets, hashes, keys, redis administration command. Alongside there is a section about performance of redis which includes the performance given by redis like hardware, payload size, batch size. It also includes benchmarks attained by redis, a demo version of redis, data durability and advantages of persistence. Then comes a section about sweet spots of redis. It includes sweet spots like cache server, tag cloud, auto completion, activity feeds and many more sweet spots. It also includes case studies as a video marketing platform, content publishing app etc.
A neighbouring section to this is about best practices which includes the design considerations and best practices of redis like avoid excessive long keys, have human readable keys, all data must fit in memory, polygot persistence is a smart choice and many more practices and design considerations. The last section of this tutorial includes some adopters of redis like stack overflow, craiglist, github, Instagram, blizzard entertainment.
Redis Day Keynote Salvatore Sanfillipo Redis LabsRedis Labs
Redis' seventh birthday was recently celebrated with the community, several contributors and users. This is Salvatore's keynote as he kicked off Redis Day in Tel Aviv.
HIgh Performance Redis- Tague Griffith, GoProRedis Labs
High Performance Redis looks at a wide range of techniques - from programming to system tuning - to deploy and maintain an extremely high performing Redis cluster. From the operational
perspective, the talk lays out multiple techniques for clustering (sharding) Redis systems and examines how the different
approaches impact performance time. The talk further examines system settings (Linux network parameters, Redis
system) and how they impact performance (both good and bad). Finally, for the developer, we look at how different ways of structuring data actually demonstrate different performance characteristics
Redis is an advanced in memory key-value store designed for a world where "Memory is the new disk and disk is the new tape". Redis has some unique properties -- like blazing read and write speed, rich atomic operations and asynchronous persistence -- which make it ideally suited for a number of situations.
The tutorial includes an introduction to redis, data types used for redis, performance related to redis, sweet spots of redis, design consideration/best practices, adopters of redis. Begins with a section giving an introduction to redis which includes an introduction to redis and the features of redis. It also includes a brief history of redis, characteristics of redis, language support of redis. Following is a data types section. It includes the data types of redis like strings, lists, hashes, sets, sorted sets. It also includes not thinking of redis as an RDBMS, installation, atomicity of commands, key expiration.
Moreover, it also includes operations on lists, sets, sorted sets, hashes, keys, redis administration command. Alongside there is a section about performance of redis which includes the performance given by redis like hardware, payload size, batch size. It also includes benchmarks attained by redis, a demo version of redis, data durability and advantages of persistence. Then comes a section about sweet spots of redis. It includes sweet spots like cache server, tag cloud, auto completion, activity feeds and many more sweet spots. It also includes case studies as a video marketing platform, content publishing app etc.
A neighbouring section to this is about best practices which includes the design considerations and best practices of redis like avoid excessive long keys, have human readable keys, all data must fit in memory, polygot persistence is a smart choice and many more practices and design considerations. The last section of this tutorial includes some adopters of redis like stack overflow, craiglist, github, Instagram, blizzard entertainment.
Managing 50K+ Redis Databases Over 4 Public Clouds ... with a Tiny Devops TeamRedis Labs
A presentation by Redis Labs' CTO, Yiftach Shoolman, given at the July 2nd meet up, hosted by I am OnDemand and IGT Cloud at the Microsoft ILDC Auditorium.
See the video at: https://www.youtube.com/watch?v=eymqHZaUOH4
In this In this session Yiftach shares tips on how the company manages 50,000+ scalable and highly avaliable Redis databases over the 4 largest public clouds, 8 leading Platforms-as-a-Service, and across 10 geographical regions. He explains the service's back-end architecture, the open-source projects it uses, and which tools the company builds in-house. Shoolman also shares what Redis Labs' small DevOps team does automatically, and what it still does manually. Finally, he offers advice on how to build a strong R&D team that lives and breathes DevOps.
Since the company launched its Redis Cloud service, it has dealt with 150+ node failure events and a half-dozen complete data-center outages. In addition, its team has experienced many interesting scenarios, such as hard to believe scaling patterns like 0 to a few hundreds gigabytes of in-memory data in just a few minutes, and 0 to 300K+ ops/sec in just a few seconds.
Counting image views using redis clusterRedis Labs
Streaming Logs and Processing View Counts using Redis Cluster
Seandon Mooy
(Imgur)
When you browse through Imgur, you notice that each user's post includes the number of views for that particular post. Imgur processes over 3 billion views per month and powers our view count feature using Redis. In this talk, we cover our current architecture for streaming logs and processing view counts using Redis Cluster, as well as some of the alternatives we explored and why we chose Redis.
What's new with enterprise Redis - Leena Joshi, Redis LabsRedis Labs
Redis Labs manages over 160k+ HA databases, 10k clustered databases, without data loss in spite of one node failure a day and one data center outage per month. Using Enterprise
Redis(RLEC), Redis Labs delivers seamless zero downtime scaling, true high availability with persistence, cross-rack/zone/
datacenter replication and instant automatic failover. Learn how. Join this session for a deep dive into how enterprise Redis makes for no-hassle Redis deployments and the roadmap for new Redis capabilities. Discover new cost savings with Redis on Flash for cost-effective high performance operations and analytics
Redis : Database, cache, pub/sub and more at Jelly button gamesRedis Labs
Nir Shney-Dor of Jelly button games talks about how he uses Redis across many different use cases - as a persistent db, cache, for pub/sub, leaderboards etc etc..Fun walkthrough of all the uses one can put Redis to.
Walmart & IBM Revisit the Linear Road Benchmark- Roger Rea, IBMRedis Labs
The Linear Road benchmark was devised in 2004 to
compare Stream Data Management Systems. Walmart selected Linear Road to compare performance of streaming analytic
offerings. IBM implemented the benchmark application using Redis to maintain state, and IBM Streams to handle the
incoming events and queries. Walmart had to completely revamp the data drivers and test verification to take advantage
of multicore multithreaded servers available today. Tests were run on Microsoft Azure cloud to ensure fair comparison of
vendors. Redis and IBM Streams handled nearly 1 billion events in a 3 hour test on a single 16 core Azure node, and 3.8 billion
when scaled out to 4 nodes. Come learn about the application and near linear scalability of Redis and IBM Streams.
How we got to 1 millisecond latency in 99% under repair, compaction, and flus...ScyllaDB
Scylla is an open source reimplementation of Cassandra which performs up to 10X with drop in-replacement compatibility. At ScyllaDB, performance matters but even more importantly, stable performance under any circumstances.
A key factor for our consistent performance is our reliance on userspace schedulers. Scheduling in userspace allows the application, the database in our case to have better control on the different priorities each task has and to provide an SLA to selected operations. Scylla used to have an I/O scheduler and recently won a CPU scheduler.
At ScyllaDB, we make architectural decisions that provide not only low latencies but consistently low latencies at higher percentiles. This begins with our choice of language and key architectural decisions such as not using the Linux page-cache, and is fulfilled by autonomous database control, a set of algorithms, which guarantees that the system will adapt to changes in the workload. In the last year, we have made changes to Scylla that provide latencies that are consistent in every percentile. In this talk, Dor Laor will recap those changes and discuss what ScyllaDB is doing in the future.
Using Redis as Distributed Cache for ASP.NET apps - Peter Kellner, 73rd Stre...Redis Labs
I will build from scratch in this session a Microsoft ASP.NET website that caches WebAPI REST calls with both MSOpenTech’s Redis implementation for running while developing in Visual Studio as well as running on a Windows server running IIS. I will show you how to build a safe reusable caching library in c# that can be used in any .net project. I will also demonstrate how to use the Redis cache services that are available on Microsoft’s Azure cloud platform. Further, I’ll demonstrate a real world web site that uses Azure Redis cache and show statistics on how Redis improves performance consistently and reliably.
Scylla Summit 2018: Consensus in Eventually Consistent DatabasesScyllaDB
Eventually consistent databases choose to remain available under failure, allowing for conflicting data to be stored in different replicas (later repaired by background processes). Weakening the consistency guarantees improves not only availability, but also performance, as the number of replicas involved in a given operation can be minimized. There are, however, use-cases that require the opposite trade-off. Indeed, Apache Cassandra and Scylla provide Lightweight Transactions (LWT), which allow single-key linearizable updates. The mechanism underlying LWT is asynchronous consensus. In this talk, we'll describe the characteristics and requirements of Scylla's consensus implementation, and how it enables strongly consistent updates. We will also cover how consensus can be applied to other aspects of the system, such as schema changes, node membership, and range movements, in order to improve their reliability and safety. We will thus show that an eventually consistent database can leverage consensus without compromising either availability or performance.
Perforce BTrees: The Arcane and the ProfanePerforce
"Get a tour of Perforce BTree history, its behaviors and configuration. Learn about performance alternatives, space management tools and future projects, too."
hbaseconasia2017: Large scale data near-line loading method and architectureHBaseCon
Shuaifeng Zhou
When we do real-time data loading to HBase, we use put/putlist interface. After receiving put request, regionserver will write WAL, write data into memory store, flush memory store to disk-store, then compact files again and again. That precedure occupies too much resource and causing read/write performance decrease. To solve the problem, we provide a kind of near-line loading method and architecture, greatly increase the loading bandwidth, and decrease the influence to read operations.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
Running Analytics at the Speed of Your BusinessRedis Labs
The speed at which you can extract insights from your data is increasingly a competitive edge for your business. Data and analytics have to be at lightning fast speeds to seriously impact your user acquisition.
Join this webinar featuring Forrester analyst Noel Yuhanna and Leena Joshi, VP Product Marketing at Redis Labs to learn how you can glean insights faster with new open source data processing frameworks like Spark and Redis.
In this webinar you will learn:
* Why analytics has to run at the real time speed of business
* How this can be achieved with next generation Big Data tools
* How data structures can optimize your hybrid transaction-analytics processing scenarios
Scylla Summit 2022: New AWS Instances Perfect for ScyllaDBScyllaDB
In this talk AWS’ Ken Krupa, Head of Specialized Solutions Architecture, will describe the architecture and capabilities of two new AWS EC2 instance types perfect for data-intensive storage and IO-heavy workloads like ScyllaDB: the Intel-based I4i and the Graviton2-based I4g series.
The Intel Xeon Ice Lake-based I4i series provides unparalleled raw horsepower for your most demanding workloads. Meanwhile, the Graviton2-powered I4g instances provide lower cost per storage on a power-efficient platform to deploy your cloud-native applications.
Ken will also describe the AWS Nitro SSD, a new form of high-speed NVMe storage with a Flash Translation Layer built with Nitro controllers, which powers both of these instance families.
ScyllaDB VP of Product Tzach Livyatan will then share benchmarking results showing how ScyllaDB behaves under load on these two instance types, providing maximum system utility and efficiency.
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
Scylla Summit 2018: Keeping Your Latency SLAs No Matter What!ScyllaDB
As a real time Big Data database, there are few things more important than keeping latencies low and bounded. Scylla has been delivering great tail latencies from our day one, but the job of making them better never ends and there is always more to do. In this talk we will explore some of the changes made to Scylla in the past few releases to help keep latencies down.
BDM37: Hadoop in production – the war stories by Nikolaï Grigoriev, Principal...Big Data Montreal
Sharing of Hadoop cluster deployment experience in production from scratch on real hardware. Brief overview of Hadoop stack, its components, major deployment and configuration challenges, performance tuning and application tuning experience. Some “war stories” about the issues we have faced while operating, the benefits of DevOps approach for running Hadoop apps.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1eyam5s.
Howard Chu covers highlights of the LMDB design and discusses some of the internal improvements in slapd due to LMDB, as well as the impact of LMDB on other projects. Filmed at qconlondon.com.
Howard Chu has been writing Free/Open Source software since the 1980s. His work has spanned a wide range of computing topics, including most of the GNU utilities (gcc, gdb, gmake, etc.), networking protocols and tools, kernel and filesystem drivers, and focused on maximizing the useful work from a system.
Managing 50K+ Redis Databases Over 4 Public Clouds ... with a Tiny Devops TeamRedis Labs
A presentation by Redis Labs' CTO, Yiftach Shoolman, given at the July 2nd meet up, hosted by I am OnDemand and IGT Cloud at the Microsoft ILDC Auditorium.
See the video at: https://www.youtube.com/watch?v=eymqHZaUOH4
In this In this session Yiftach shares tips on how the company manages 50,000+ scalable and highly avaliable Redis databases over the 4 largest public clouds, 8 leading Platforms-as-a-Service, and across 10 geographical regions. He explains the service's back-end architecture, the open-source projects it uses, and which tools the company builds in-house. Shoolman also shares what Redis Labs' small DevOps team does automatically, and what it still does manually. Finally, he offers advice on how to build a strong R&D team that lives and breathes DevOps.
Since the company launched its Redis Cloud service, it has dealt with 150+ node failure events and a half-dozen complete data-center outages. In addition, its team has experienced many interesting scenarios, such as hard to believe scaling patterns like 0 to a few hundreds gigabytes of in-memory data in just a few minutes, and 0 to 300K+ ops/sec in just a few seconds.
Counting image views using redis clusterRedis Labs
Streaming Logs and Processing View Counts using Redis Cluster
Seandon Mooy
(Imgur)
When you browse through Imgur, you notice that each user's post includes the number of views for that particular post. Imgur processes over 3 billion views per month and powers our view count feature using Redis. In this talk, we cover our current architecture for streaming logs and processing view counts using Redis Cluster, as well as some of the alternatives we explored and why we chose Redis.
What's new with enterprise Redis - Leena Joshi, Redis LabsRedis Labs
Redis Labs manages over 160k+ HA databases, 10k clustered databases, without data loss in spite of one node failure a day and one data center outage per month. Using Enterprise
Redis(RLEC), Redis Labs delivers seamless zero downtime scaling, true high availability with persistence, cross-rack/zone/
datacenter replication and instant automatic failover. Learn how. Join this session for a deep dive into how enterprise Redis makes for no-hassle Redis deployments and the roadmap for new Redis capabilities. Discover new cost savings with Redis on Flash for cost-effective high performance operations and analytics
Redis : Database, cache, pub/sub and more at Jelly button gamesRedis Labs
Nir Shney-Dor of Jelly button games talks about how he uses Redis across many different use cases - as a persistent db, cache, for pub/sub, leaderboards etc etc..Fun walkthrough of all the uses one can put Redis to.
Walmart & IBM Revisit the Linear Road Benchmark- Roger Rea, IBMRedis Labs
The Linear Road benchmark was devised in 2004 to
compare Stream Data Management Systems. Walmart selected Linear Road to compare performance of streaming analytic
offerings. IBM implemented the benchmark application using Redis to maintain state, and IBM Streams to handle the
incoming events and queries. Walmart had to completely revamp the data drivers and test verification to take advantage
of multicore multithreaded servers available today. Tests were run on Microsoft Azure cloud to ensure fair comparison of
vendors. Redis and IBM Streams handled nearly 1 billion events in a 3 hour test on a single 16 core Azure node, and 3.8 billion
when scaled out to 4 nodes. Come learn about the application and near linear scalability of Redis and IBM Streams.
How we got to 1 millisecond latency in 99% under repair, compaction, and flus...ScyllaDB
Scylla is an open source reimplementation of Cassandra which performs up to 10X with drop in-replacement compatibility. At ScyllaDB, performance matters but even more importantly, stable performance under any circumstances.
A key factor for our consistent performance is our reliance on userspace schedulers. Scheduling in userspace allows the application, the database in our case to have better control on the different priorities each task has and to provide an SLA to selected operations. Scylla used to have an I/O scheduler and recently won a CPU scheduler.
At ScyllaDB, we make architectural decisions that provide not only low latencies but consistently low latencies at higher percentiles. This begins with our choice of language and key architectural decisions such as not using the Linux page-cache, and is fulfilled by autonomous database control, a set of algorithms, which guarantees that the system will adapt to changes in the workload. In the last year, we have made changes to Scylla that provide latencies that are consistent in every percentile. In this talk, Dor Laor will recap those changes and discuss what ScyllaDB is doing in the future.
Using Redis as Distributed Cache for ASP.NET apps - Peter Kellner, 73rd Stre...Redis Labs
I will build from scratch in this session a Microsoft ASP.NET website that caches WebAPI REST calls with both MSOpenTech’s Redis implementation for running while developing in Visual Studio as well as running on a Windows server running IIS. I will show you how to build a safe reusable caching library in c# that can be used in any .net project. I will also demonstrate how to use the Redis cache services that are available on Microsoft’s Azure cloud platform. Further, I’ll demonstrate a real world web site that uses Azure Redis cache and show statistics on how Redis improves performance consistently and reliably.
Scylla Summit 2018: Consensus in Eventually Consistent DatabasesScyllaDB
Eventually consistent databases choose to remain available under failure, allowing for conflicting data to be stored in different replicas (later repaired by background processes). Weakening the consistency guarantees improves not only availability, but also performance, as the number of replicas involved in a given operation can be minimized. There are, however, use-cases that require the opposite trade-off. Indeed, Apache Cassandra and Scylla provide Lightweight Transactions (LWT), which allow single-key linearizable updates. The mechanism underlying LWT is asynchronous consensus. In this talk, we'll describe the characteristics and requirements of Scylla's consensus implementation, and how it enables strongly consistent updates. We will also cover how consensus can be applied to other aspects of the system, such as schema changes, node membership, and range movements, in order to improve their reliability and safety. We will thus show that an eventually consistent database can leverage consensus without compromising either availability or performance.
Perforce BTrees: The Arcane and the ProfanePerforce
"Get a tour of Perforce BTree history, its behaviors and configuration. Learn about performance alternatives, space management tools and future projects, too."
hbaseconasia2017: Large scale data near-line loading method and architectureHBaseCon
Shuaifeng Zhou
When we do real-time data loading to HBase, we use put/putlist interface. After receiving put request, regionserver will write WAL, write data into memory store, flush memory store to disk-store, then compact files again and again. That precedure occupies too much resource and causing read/write performance decrease. To solve the problem, we provide a kind of near-line loading method and architecture, greatly increase the loading bandwidth, and decrease the influence to read operations.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
Running Analytics at the Speed of Your BusinessRedis Labs
The speed at which you can extract insights from your data is increasingly a competitive edge for your business. Data and analytics have to be at lightning fast speeds to seriously impact your user acquisition.
Join this webinar featuring Forrester analyst Noel Yuhanna and Leena Joshi, VP Product Marketing at Redis Labs to learn how you can glean insights faster with new open source data processing frameworks like Spark and Redis.
In this webinar you will learn:
* Why analytics has to run at the real time speed of business
* How this can be achieved with next generation Big Data tools
* How data structures can optimize your hybrid transaction-analytics processing scenarios
Scylla Summit 2022: New AWS Instances Perfect for ScyllaDBScyllaDB
In this talk AWS’ Ken Krupa, Head of Specialized Solutions Architecture, will describe the architecture and capabilities of two new AWS EC2 instance types perfect for data-intensive storage and IO-heavy workloads like ScyllaDB: the Intel-based I4i and the Graviton2-based I4g series.
The Intel Xeon Ice Lake-based I4i series provides unparalleled raw horsepower for your most demanding workloads. Meanwhile, the Graviton2-powered I4g instances provide lower cost per storage on a power-efficient platform to deploy your cloud-native applications.
Ken will also describe the AWS Nitro SSD, a new form of high-speed NVMe storage with a Flash Translation Layer built with Nitro controllers, which powers both of these instance families.
ScyllaDB VP of Product Tzach Livyatan will then share benchmarking results showing how ScyllaDB behaves under load on these two instance types, providing maximum system utility and efficiency.
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
Scylla Summit 2018: Keeping Your Latency SLAs No Matter What!ScyllaDB
As a real time Big Data database, there are few things more important than keeping latencies low and bounded. Scylla has been delivering great tail latencies from our day one, but the job of making them better never ends and there is always more to do. In this talk we will explore some of the changes made to Scylla in the past few releases to help keep latencies down.
BDM37: Hadoop in production – the war stories by Nikolaï Grigoriev, Principal...Big Data Montreal
Sharing of Hadoop cluster deployment experience in production from scratch on real hardware. Brief overview of Hadoop stack, its components, major deployment and configuration challenges, performance tuning and application tuning experience. Some “war stories” about the issues we have faced while operating, the benefits of DevOps approach for running Hadoop apps.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1eyam5s.
Howard Chu covers highlights of the LMDB design and discusses some of the internal improvements in slapd due to LMDB, as well as the impact of LMDB on other projects. Filmed at qconlondon.com.
Howard Chu has been writing Free/Open Source software since the 1980s. His work has spanned a wide range of computing topics, including most of the GNU utilities (gcc, gdb, gmake, etc.), networking protocols and tools, kernel and filesystem drivers, and focused on maximizing the useful work from a system.
Gem Session on scaling AEM (CQ5). Topics include:
High Volume and High Performance Delivery
High Frequency Input Feed
High Processing Input Feed
High Volume Input Feed
Many Editors
Geo-distributed Editors
Many DAM assets
Geo-distributed disaster recovery
As cloud adoption has grown more rapidly in the last decade , how DBA's a can add more value to system and bring in more scalability to the DB server. This talk was presented at Open Source India 2018 conference by Kabilesh and Manosh of Mydbops. They share a few experience and value addition made to customers during their consulting process.
Running Production CDC Ingestion Pipelines With Balaji Varadarajan and Pritam...HostedbyConfluent
Running Production CDC Ingestion Pipelines With Balaji Varadarajan and Pritam K Dey | Current 2022
Robinhood’s mission is to democratize finance for all. Data driven decision making is key to achieving this goal. Data needed are hosted in various OLTP databases. Replicating this data near real time in a reliable fashion to data lakehouse powers many critical use cases for the company. In Robinhood, CDC is not only used for ingestion to data-lake but is also being adopted for inter-system message exchanges between different online micro services. .
In this talk, we will describe the evolution of change data capture based ingestion in Robinhood not only in terms of the scale of data stored and queries made, but also the use cases that it supports. We will go in-depth into the CDC architecture built around our Kafka ecosystem using open source system Debezium and Apache Hudi. We will cover online inter-system message exchange use-cases along with our experience running this service at scale in Robinhood along with lessons learned.
Despite all the buzz about it, building a horizontally scalable application for cloud deployment isn't all that different from building one for a physical deployment, except in its ability to change size on-the-fly. Bigger applications have been using commodity hardware and fault-tolerant design to achieve high availability and scalability for a while, but provisioning capacity remains troublesome there. The real addition the cloud brings architecturally is the ability to add new resources instantly, and even change your provisioning profile algorithmically.
Aujourd’hui, un nombre important de nos clients ont franchi le cap et utilisent Oracle 12c pour des applications en production. Parmi tous ces clients, nous constatons de plus en plus un intérêt prononcé pour l’option Multitenant.
Même si le passage à l’option Multitenant ne présente pas de difficultés en soi, il existe de nombreux pièges à éviter ainsi que quelques points à clarifier, notamment sur les aspects relatifs à la performance.
En abordant un cas concret avec l’un de nos clients, cette présentation vous apportera des éléments de réponses afin de vous permettre d’envisager sereinement la mise en œuvre de cette option qui constitue un changement important et très intéressant d’un point vue architecture.
Scaling up and accelerating Drupal 8 with NoSQLOSInet
Drupal 8 can scale well and serve pages fast to many users, especially by offloading parts of the work load from the main SQL database to NoSQL solutions.
This presentation describes the strategies and technologies usable to achieve such gains, including specific configuration, contributed modules and custom coding strategies.
Basics of Web App Systems Architecture
General Web Software Optimization Strategies
Defining a Goal for Performance
Performance Metrics, tools
Performance Debugging Techniques
What Can You Control?
What Is Caching?
Drupal Performance modules
Optimizing Drupal
Auto Europe's ongoing journey with MariaDB and open sourceMariaDB plc
Tom Girsch, Lead System Architect at Auto Europe, covers the use case that initially brought Auto Europe to MariaDB, as well as additional planned and ongoing projects. He goes on to discuss Auto Europe’s implementation of MariaDB using clustering, traditional replication and MaxScale. Next, he covers some of the problems and pitfalls encountered along the way, as well as some suggestions to further improve the product.
Similar to Redis Developers Day 2014 - Redis Labs Talks (20)
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
2. 1st talk: day to day challenges
● ±150 new databases every day (>60,000 total)
● A mix of very small (few MB) and extremely large (TB+) datasets
● PAYG model that can grow to any size by changing maxmemory
● ±250 node failure events (~10 every month) – w/o upgrades
● 5-6 complete data-center outages so far
● Supporting/managing DBs with zero knowledge of application
● Running a modified version of Redis from day 1
3. 2nd talk: Extending Redis to Flash
● Redis #1 hindering factor is the cost of deployment ➔
RAM costs x10 SSD and x250 HDD
● Advanced flash technologies:➔ 1M IOPS (4KB)
➔ Latency: DIMM – 1-2μsec; Plugable – 100μsec
4. Why do we modify Redis?
● Dense multi-tenant architecture and still maintaining
SLAs (single-tenant, dedicated instances supported)
● Efficient large-scale system administration
● Clustering (in production since 06/2012)
5. How do we manage changes?
● Many custom modifications are of no use for anyone else, so we
don’t bother
● Fixes, minor changes to upstream ASAP
● Periodic batches of more significant features
o Low hit rate, community view vs. enterprise/service provider
view
● Our “playground” fork https://github.com/redislabs/redis
6. Skin-of-our-teeth replication
● DB is growing, sync to a bigger instance
● Lots of writes, output buffers and COW bloat
● Redis has a point of no return
⇓
Off-load some of this to the slave
Flush output buffer to slave, Mux RDB
7. Improved Replication Performance
Master
Memory
Master Disk Network Slave Disk Slave
Memory
● Eliminate disk impact on replication performance (pain point)
● Current Redis scheme can still be better sometimes
8. Lua Replication
● Old debate: SCRIPT vs Commands
o In some cases performance is dramatic
o We think both options are needed (user-controlled)
9. External RDB processing
● We do it often, and not only us:
https://github.com/sripathikrishnan/redis-rdb-tools
● Python is too slow for serious work
● We want to avoid code duplication
● Built-in rdb.c was too hard-coded
● Alternative SAX-like (callback-based) parser
● Maybe even librdb.a?
10. Resource Management
● maxmemory can be confusing (think eviction/OOM because of
output buffers)
● How big can we expect the actual process? Master switches are
missing (e.g. max allocated network buffers)
● Graceful handling of output buffer limits
● Fragmentation issues
o jemalloc and the 12% magic
o active defrag and bigger ideas for bigger problems
11. More Resource Management
● Automatic process oom_score_adj settings to guard
data in low memory events
● Automatically reject BGSAVE and BGREWRITEAOF or
terminate child on low memory
13. Extending Redis to Flash - goals
● More (cheap) memory not better persistence
● 100% compatibility with Redis
● Pluggable storage back-ends / non-volatile DIMM
● Utilize storage concurrency
● Use case: limited set of hot objects over large keyspace
● Different from other approaches: Ledis, ARDB, SSDB
14. Extending Redis to Flash - blocks
Main thread
Clients
I/O job
queues
I/O
Threads
K/V storage
backend API
Storage
backend
15. Extending Redis to Flash - moar
● Still being prepared for prime time
● Planned to be open sourced once ready