Horizontal scaling of databases can increase performance and capacity, but adding nodes also increases infrastructure and management complexities. Cluster management can challenge even the most seasoned IT professional. While vertical scaling is easier to implement, it has traditionally been limited by memory and disk throughput. As both SSD latency and price continue to improve, the MongoDB database scaling equation changes. This session will review a number of SSD technologies that Intel employs (SATA, NVMe) and their impacts on I/O performance and database scaling. We will look at various architectural options for optimizing I/O based on our discussions with real world users. We will also provide attendees a glimpse at our future plans in terms of technologies in the storage area.
In the Cloud Native community, eBPF is gaining popularity, which can often be the best solution for solving different challenges with deep observability of system. Currently, eBPF is being embraced by major players.
Mydbops co-Founder, Kabilesh P.R (MySQL and Mongo Consultant) illustrates on debugging linux issues with eBPF. A brief about BPF & eBPF, BPF internals and the tools in actions for faster resolution.
CloudNative Days Tokyo 2020での、lazypullに関する発表資料です。https://event.cloudnativedays.jp/cndt2020/talks/16
Stargz Snapshotterのリポジトリ:
https://github.com/containerd/stargz-snapshotter
Talk for SCaLE13x. Video: https://www.youtube.com/watch?v=_Ik8oiQvWgo . Profiling can show what your Linux kernel and appliacations are doing in detail, across all software stack layers. This talk shows how we are using Linux perf_events (aka "perf") and flame graphs at Netflix to understand CPU usage in detail, to optimize our cloud usage, solve performance issues, and identify regressions. This will be more than just an intro: profiling difficult targets, including Java and Node.js, will be covered, which includes ways to resolve JITed symbols and broken stacks. Included are the easy examples, the hard, and the cutting edge.
Velocity 2017 Performance analysis superpowers with Linux eBPFBrendan Gregg
Talk by for Velocity 2017 by Brendan Gregg: Performance analysis superpowers with Linux eBPF.
"Advanced performance observability and debugging have arrived built into the Linux 4.x series, thanks to enhancements to Berkeley Packet Filter (BPF, or eBPF) and the repurposing of its sandboxed virtual machine to provide programmatic capabilities to system tracing. Netflix has been investigating its use for new observability tools, monitoring, security uses, and more. This talk will investigate this new technology, which sooner or later will be available to everyone who uses Linux. The talk will dive deep on these new tracing, observability, and debugging capabilities. Whether you’re doing analysis over an ssh session, or via a monitoring GUI, BPF can be used to provide an efficient, custom, and deep level of detail into system and application performance.
This talk will also demonstrate the new open source tools that have been developed, which make use of kernel- and user-level dynamic tracing (kprobes and uprobes), and kernel- and user-level static tracing (tracepoints). These tools provide new insights for file system and storage performance, CPU scheduler performance, TCP performance, and a whole lot more. This is a major turning point for Linux systems engineering, as custom advanced performance instrumentation can be used safely in production environments, powering a new generation of tools and visualizations."
In the Cloud Native community, eBPF is gaining popularity, which can often be the best solution for solving different challenges with deep observability of system. Currently, eBPF is being embraced by major players.
Mydbops co-Founder, Kabilesh P.R (MySQL and Mongo Consultant) illustrates on debugging linux issues with eBPF. A brief about BPF & eBPF, BPF internals and the tools in actions for faster resolution.
CloudNative Days Tokyo 2020での、lazypullに関する発表資料です。https://event.cloudnativedays.jp/cndt2020/talks/16
Stargz Snapshotterのリポジトリ:
https://github.com/containerd/stargz-snapshotter
Talk for SCaLE13x. Video: https://www.youtube.com/watch?v=_Ik8oiQvWgo . Profiling can show what your Linux kernel and appliacations are doing in detail, across all software stack layers. This talk shows how we are using Linux perf_events (aka "perf") and flame graphs at Netflix to understand CPU usage in detail, to optimize our cloud usage, solve performance issues, and identify regressions. This will be more than just an intro: profiling difficult targets, including Java and Node.js, will be covered, which includes ways to resolve JITed symbols and broken stacks. Included are the easy examples, the hard, and the cutting edge.
Velocity 2017 Performance analysis superpowers with Linux eBPFBrendan Gregg
Talk by for Velocity 2017 by Brendan Gregg: Performance analysis superpowers with Linux eBPF.
"Advanced performance observability and debugging have arrived built into the Linux 4.x series, thanks to enhancements to Berkeley Packet Filter (BPF, or eBPF) and the repurposing of its sandboxed virtual machine to provide programmatic capabilities to system tracing. Netflix has been investigating its use for new observability tools, monitoring, security uses, and more. This talk will investigate this new technology, which sooner or later will be available to everyone who uses Linux. The talk will dive deep on these new tracing, observability, and debugging capabilities. Whether you’re doing analysis over an ssh session, or via a monitoring GUI, BPF can be used to provide an efficient, custom, and deep level of detail into system and application performance.
This talk will also demonstrate the new open source tools that have been developed, which make use of kernel- and user-level dynamic tracing (kprobes and uprobes), and kernel- and user-level static tracing (tracepoints). These tools provide new insights for file system and storage performance, CPU scheduler performance, TCP performance, and a whole lot more. This is a major turning point for Linux systems engineering, as custom advanced performance instrumentation can be used safely in production environments, powering a new generation of tools and visualizations."
Performance Wins with BPF: Getting StartedBrendan Gregg
Keynote by Brendan Gregg for the eBPF summit, 2020. How to get started finding performance wins using the BPF (eBPF) technology. This short talk covers the quickest and easiest way to find performance wins using BPF observability tools on Linux.
Talk by Brendan Gregg for USENIX LISA 2019: Linux Systems Performance. Abstract: "
Systems performance is an effective discipline for performance analysis and tuning, and can help you find performance wins for your applications and the kernel. However, most of us are not performance or kernel engineers, and have limited time to study this topic. This talk summarizes the topic for everyone, touring six important areas of Linux systems performance: observability tools, methodologies, benchmarking, profiling, tracing, and tuning. Included are recipes for Linux performance analysis and tuning (using vmstat, mpstat, iostat, etc), overviews of complex areas including profiling (perf_events) and tracing (Ftrace, bcc/BPF, and bpftrace/BPF), and much advice about what is and isn't important to learn. This talk is aimed at everyone: developers, operations, sysadmins, etc, and in any environment running Linux, bare metal or the cloud."
Video: https://www.youtube.com/watch?v=FJW8nGV4jxY and https://www.youtube.com/watch?v=zrr2nUln9Kk . Tutorial slides for O'Reilly Velocity SC 2015, by Brendan Gregg.
There are many performance tools nowadays for Linux, but how do they all fit together, and when do we use them? This tutorial explains methodologies for using these tools, and provides a tour of four tool types: observability, benchmarking, tuning, and static tuning. Many tools will be discussed, including top, iostat, tcpdump, sar, perf_events, ftrace, SystemTap, sysdig, and others, as well observability frameworks in the Linux kernel: PMCs, tracepoints, kprobes, and uprobes.
This tutorial is updated and extended on an earlier talk that summarizes the Linux performance tool landscape. The value of this tutorial is not just learning that these tools exist and what they do, but hearing when and how they are used by a performance engineer to solve real world problems — important context that is typically not included in the standard documentation.
talked by CI/CD Conference 2021 by CloudNative Days https://event.cloudnativedays.jp/cicd2021
re-upload: https://speakerdeck.com/whywaita/cyberagent-oss-cicd-myshoes-cicd2021
Tracing Summit 2014, Düsseldorf. What can Linux learn from DTrace: what went well, and what didn't go well, on its path to success? This talk will discuss not just the DTrace software, but lessons from the marketing and adoption of a system tracer, and an inside look at how DTrace was really deployed and used in production environments. It will also cover ongoing problems with DTrace, and how Linux may surpass them and continue to advance the field of system tracing. A world expert and core contributor to DTrace, Brendan now works at Netflix on Linux performance with the various Linux tracers (ftrace, perf_events, eBPF, SystemTap, ktap, sysdig, LTTng, and the DTrace Linux ports), and will summarize his experiences and suggestions for improvements. He has also been contributing to various tracers: recently promoting ftrace and perf_events adoption through articles and front-end scripts, and testing eBPF.
Tracing MariaDB server with bpftrace - MariaDB Server Fest 2021Valeriy Kravchuk
Bpftrace is a relatively new eBPF-based open source tracer for modern Linux versions (kernels 5.x.y) that is useful for analyzing production performance problems and troubleshooting software. Basic usage of the tool, as well as bpftrace one liners and advanced scripts useful for MariaDB DBAs are presented. Problems of MariaDB Server dynamic tracing with bpftrace and some possible solutions and alternative tracing tools are discussed.
Video: https://www.youtube.com/watch?v=JRFNIKUROPE . Talk for linux.conf.au 2017 (LCA2017) by Brendan Gregg, about Linux enhanced BPF (eBPF). Abstract:
A world of new capabilities is emerging for the Linux 4.x series, thanks to enhancements that have been included in Linux for to Berkeley Packet Filter (BPF): an in-kernel virtual machine that can execute user space-defined programs. It is finding uses for security auditing and enforcement, enhancing networking (including eXpress Data Path), and performance observability and troubleshooting. Many new open source tools that have been written in the past 12 months for performance analysis that use BPF. Tracing superpowers have finally arrived for Linux!
For its use with tracing, BPF provides the programmable capabilities to the existing tracing frameworks: kprobes, uprobes, and tracepoints. In particular, BPF allows timestamps to be recorded and compared from custom events, allowing latency to be studied in many new places: kernel and application internals. It also allows data to be efficiently summarized in-kernel, including as histograms. This has allowed dozens of new observability tools to be developed so far, including measuring latency distributions for file system I/O and run queue latency, printing details of storage device I/O and TCP retransmits, investigating blocked stack traces and memory leaks, and a whole lot more.
This talk will summarize BPF capabilities and use cases so far, and then focus on its use to enhance Linux tracing, especially with the open source bcc collection. bcc includes BPF versions of old classics, and many new tools, including execsnoop, opensnoop, funcccount, ext4slower, and more (many of which I developed). Perhaps you'd like to develop new tools, or use the existing tools to find performance wins large and small, especially when instrumenting areas that previously had zero visibility. I'll also summarize how we intend to use these new capabilities to enhance systems analysis at Netflix.
BPF of Berkeley Packet Filter mechanism was first introduced in linux in 1997 in version 2.1.75. It has seen a number of extensions of the years. Recently in versions 3.15 - 3.19 it received a major overhaul which drastically expanded it's applicability. This talk will cover how the instruction set looks today and why. It's architecture, capabilities, interface, just-in-time compilers. We will also talk about how it's being used in different areas of the kernel like tracing and networking and future plans.
Linux Performance Analysis: New Tools and Old SecretsBrendan Gregg
Talk for USENIX/LISA2014 by Brendan Gregg, Netflix. At Netflix performance is crucial, and we use many high to low level tools to analyze our stack in different ways. In this talk, I will introduce new system observability tools we are using at Netflix, which I've ported from my DTraceToolkit, and are intended for our Linux 3.2 cloud instances. These show that Linux can do more than you may think, by using creative hacks and workarounds with existing kernel features (ftrace, perf_events). While these are solving issues on current versions of Linux, I'll also briefly summarize the future in this space: eBPF, ktap, SystemTap, sysdig, etc.
Cost-Based Optimizer Framework for Spark SQL: Spark Summit East talk by Ron H...Spark Summit
In Spark SQL’s Catalyst optimizer, many rule based optimization techniques have been implemented, but the optimizer itself can still be improved. For example, without detailed column statistics information on data distribution, it is difficult to accurately estimate the filter factor, cardinality, and thus output size of a database operator. With the inaccurate and/or misleading statistics, it often leads the optimizer to choose suboptimal query execution plans.
We added a Cost-Based Optimizer framework to Spark SQL engine. In our framework, we use Analyze Table SQL statement to collect the detailed column statistics and save them into Spark’s catalog. For the relevant columns, we collect number of distinct values, number of NULL values, maximum/minimum value, average/maximal column length, etc. Also, we save the data distribution of columns in either equal-width or equal-height histograms in order to deal with data skew effectively. Furthermore, with the number of distinct values and number of records of a table, we can determine how unique a column is although Spark SQL does not support primary key. This helps determine, for example, the output size of join operation and multi-column group-by operation.
In our framework, we compute the cardinality and output size of each database operator. With reliable statistics and derived cardinalities, we are able to make good decisions in these areas: selecting the correct build side of a hash-join operation, choosing the right join type (broadcast hash-join versus shuffled hash-join), adjusting multi-way join order, etc. In this talk, we will show Spark SQL’s new Cost-Based Optimizer framework and its performance impact on TPC-DS benchmark queries.
Bluemix provides developers with multiple open-source compute options to run their apps, chief among them Cloud Foundry, the world’s leading platform-as-a-service (PaaS) offering. Cloud Foundry enables teams to practice continuous delivery by supporting the full software development lifecycle, from dev to deployment. One of the key advantages of the platform is the ability it gives developers to easily configure and start using a MongoDB datastore for their application. In this lightning talk, Bluemix developer advocate Jake Peyser will go over Cloud Foundry and best practices for data storage when using the platform. He will then take attendees through a live demo where he will show users how to quickly configure a MongoDB instance in Bluemix and connect it to an application.
MongoDB Linux Porting, Performance Measurements and and Scaling Advantage usi...MongoDB
MongoDB has been ported onto Linux on z Systems. MongoDB Performance benefits from the superior single thread performance of System z processor and system design. The goal of the presentation is to demonstrate the value of running MongoDB on Linux for Systems z by comparing scaling behavior of MongoDB sharding on x86 and mainframe. The presentation will give details on performance numbers and scaling behavior of MongoDB on Systems z versus Intel based servers. The presentation will also sketch how MongoDB sharding on Linux on z Systems can be dockerized to facilitate the setup.
Performance Wins with BPF: Getting StartedBrendan Gregg
Keynote by Brendan Gregg for the eBPF summit, 2020. How to get started finding performance wins using the BPF (eBPF) technology. This short talk covers the quickest and easiest way to find performance wins using BPF observability tools on Linux.
Talk by Brendan Gregg for USENIX LISA 2019: Linux Systems Performance. Abstract: "
Systems performance is an effective discipline for performance analysis and tuning, and can help you find performance wins for your applications and the kernel. However, most of us are not performance or kernel engineers, and have limited time to study this topic. This talk summarizes the topic for everyone, touring six important areas of Linux systems performance: observability tools, methodologies, benchmarking, profiling, tracing, and tuning. Included are recipes for Linux performance analysis and tuning (using vmstat, mpstat, iostat, etc), overviews of complex areas including profiling (perf_events) and tracing (Ftrace, bcc/BPF, and bpftrace/BPF), and much advice about what is and isn't important to learn. This talk is aimed at everyone: developers, operations, sysadmins, etc, and in any environment running Linux, bare metal or the cloud."
Video: https://www.youtube.com/watch?v=FJW8nGV4jxY and https://www.youtube.com/watch?v=zrr2nUln9Kk . Tutorial slides for O'Reilly Velocity SC 2015, by Brendan Gregg.
There are many performance tools nowadays for Linux, but how do they all fit together, and when do we use them? This tutorial explains methodologies for using these tools, and provides a tour of four tool types: observability, benchmarking, tuning, and static tuning. Many tools will be discussed, including top, iostat, tcpdump, sar, perf_events, ftrace, SystemTap, sysdig, and others, as well observability frameworks in the Linux kernel: PMCs, tracepoints, kprobes, and uprobes.
This tutorial is updated and extended on an earlier talk that summarizes the Linux performance tool landscape. The value of this tutorial is not just learning that these tools exist and what they do, but hearing when and how they are used by a performance engineer to solve real world problems — important context that is typically not included in the standard documentation.
talked by CI/CD Conference 2021 by CloudNative Days https://event.cloudnativedays.jp/cicd2021
re-upload: https://speakerdeck.com/whywaita/cyberagent-oss-cicd-myshoes-cicd2021
Tracing Summit 2014, Düsseldorf. What can Linux learn from DTrace: what went well, and what didn't go well, on its path to success? This talk will discuss not just the DTrace software, but lessons from the marketing and adoption of a system tracer, and an inside look at how DTrace was really deployed and used in production environments. It will also cover ongoing problems with DTrace, and how Linux may surpass them and continue to advance the field of system tracing. A world expert and core contributor to DTrace, Brendan now works at Netflix on Linux performance with the various Linux tracers (ftrace, perf_events, eBPF, SystemTap, ktap, sysdig, LTTng, and the DTrace Linux ports), and will summarize his experiences and suggestions for improvements. He has also been contributing to various tracers: recently promoting ftrace and perf_events adoption through articles and front-end scripts, and testing eBPF.
Tracing MariaDB server with bpftrace - MariaDB Server Fest 2021Valeriy Kravchuk
Bpftrace is a relatively new eBPF-based open source tracer for modern Linux versions (kernels 5.x.y) that is useful for analyzing production performance problems and troubleshooting software. Basic usage of the tool, as well as bpftrace one liners and advanced scripts useful for MariaDB DBAs are presented. Problems of MariaDB Server dynamic tracing with bpftrace and some possible solutions and alternative tracing tools are discussed.
Video: https://www.youtube.com/watch?v=JRFNIKUROPE . Talk for linux.conf.au 2017 (LCA2017) by Brendan Gregg, about Linux enhanced BPF (eBPF). Abstract:
A world of new capabilities is emerging for the Linux 4.x series, thanks to enhancements that have been included in Linux for to Berkeley Packet Filter (BPF): an in-kernel virtual machine that can execute user space-defined programs. It is finding uses for security auditing and enforcement, enhancing networking (including eXpress Data Path), and performance observability and troubleshooting. Many new open source tools that have been written in the past 12 months for performance analysis that use BPF. Tracing superpowers have finally arrived for Linux!
For its use with tracing, BPF provides the programmable capabilities to the existing tracing frameworks: kprobes, uprobes, and tracepoints. In particular, BPF allows timestamps to be recorded and compared from custom events, allowing latency to be studied in many new places: kernel and application internals. It also allows data to be efficiently summarized in-kernel, including as histograms. This has allowed dozens of new observability tools to be developed so far, including measuring latency distributions for file system I/O and run queue latency, printing details of storage device I/O and TCP retransmits, investigating blocked stack traces and memory leaks, and a whole lot more.
This talk will summarize BPF capabilities and use cases so far, and then focus on its use to enhance Linux tracing, especially with the open source bcc collection. bcc includes BPF versions of old classics, and many new tools, including execsnoop, opensnoop, funcccount, ext4slower, and more (many of which I developed). Perhaps you'd like to develop new tools, or use the existing tools to find performance wins large and small, especially when instrumenting areas that previously had zero visibility. I'll also summarize how we intend to use these new capabilities to enhance systems analysis at Netflix.
BPF of Berkeley Packet Filter mechanism was first introduced in linux in 1997 in version 2.1.75. It has seen a number of extensions of the years. Recently in versions 3.15 - 3.19 it received a major overhaul which drastically expanded it's applicability. This talk will cover how the instruction set looks today and why. It's architecture, capabilities, interface, just-in-time compilers. We will also talk about how it's being used in different areas of the kernel like tracing and networking and future plans.
Linux Performance Analysis: New Tools and Old SecretsBrendan Gregg
Talk for USENIX/LISA2014 by Brendan Gregg, Netflix. At Netflix performance is crucial, and we use many high to low level tools to analyze our stack in different ways. In this talk, I will introduce new system observability tools we are using at Netflix, which I've ported from my DTraceToolkit, and are intended for our Linux 3.2 cloud instances. These show that Linux can do more than you may think, by using creative hacks and workarounds with existing kernel features (ftrace, perf_events). While these are solving issues on current versions of Linux, I'll also briefly summarize the future in this space: eBPF, ktap, SystemTap, sysdig, etc.
Cost-Based Optimizer Framework for Spark SQL: Spark Summit East talk by Ron H...Spark Summit
In Spark SQL’s Catalyst optimizer, many rule based optimization techniques have been implemented, but the optimizer itself can still be improved. For example, without detailed column statistics information on data distribution, it is difficult to accurately estimate the filter factor, cardinality, and thus output size of a database operator. With the inaccurate and/or misleading statistics, it often leads the optimizer to choose suboptimal query execution plans.
We added a Cost-Based Optimizer framework to Spark SQL engine. In our framework, we use Analyze Table SQL statement to collect the detailed column statistics and save them into Spark’s catalog. For the relevant columns, we collect number of distinct values, number of NULL values, maximum/minimum value, average/maximal column length, etc. Also, we save the data distribution of columns in either equal-width or equal-height histograms in order to deal with data skew effectively. Furthermore, with the number of distinct values and number of records of a table, we can determine how unique a column is although Spark SQL does not support primary key. This helps determine, for example, the output size of join operation and multi-column group-by operation.
In our framework, we compute the cardinality and output size of each database operator. With reliable statistics and derived cardinalities, we are able to make good decisions in these areas: selecting the correct build side of a hash-join operation, choosing the right join type (broadcast hash-join versus shuffled hash-join), adjusting multi-way join order, etc. In this talk, we will show Spark SQL’s new Cost-Based Optimizer framework and its performance impact on TPC-DS benchmark queries.
Bluemix provides developers with multiple open-source compute options to run their apps, chief among them Cloud Foundry, the world’s leading platform-as-a-service (PaaS) offering. Cloud Foundry enables teams to practice continuous delivery by supporting the full software development lifecycle, from dev to deployment. One of the key advantages of the platform is the ability it gives developers to easily configure and start using a MongoDB datastore for their application. In this lightning talk, Bluemix developer advocate Jake Peyser will go over Cloud Foundry and best practices for data storage when using the platform. He will then take attendees through a live demo where he will show users how to quickly configure a MongoDB instance in Bluemix and connect it to an application.
MongoDB Linux Porting, Performance Measurements and and Scaling Advantage usi...MongoDB
MongoDB has been ported onto Linux on z Systems. MongoDB Performance benefits from the superior single thread performance of System z processor and system design. The goal of the presentation is to demonstrate the value of running MongoDB on Linux for Systems z by comparing scaling behavior of MongoDB sharding on x86 and mainframe. The presentation will give details on performance numbers and scaling behavior of MongoDB on Systems z versus Intel based servers. The presentation will also sketch how MongoDB sharding on Linux on z Systems can be dockerized to facilitate the setup.
<b>Blending Hadoop and MongoDB with Pentaho </b>[11:10 am - 11:30 am]<br />For eCommerce companies, knowing how promoted wish-lists can spark consumer spending is an analytics goldmine. In this lightning talk, Bo Borland will demonstrate how Pentaho analytics can blend click-stream data about promoted wish-lists with sales transaction records using Hadoop, MongoDB and Pentaho to reveal patterns in online shopping behavior. Regardless of your industry or specific use model, come to this session to learn how to blend MongoDB data with any data source for greater business insight. Pentaho offers the first end-to-end analytic solution for MongoDB. From data ingestion to pixel perfect reporting and ad hoc “slice and dice” analysis, the solution meets today’s growing demand for a 360-degree view of your business.
Transforming your Business with Scale-Out Flash: How MongoDB & Flash Accelera...MongoDB
<b>Transforming your Business with Scale-Out Flash: How MongoDB & Flash Accelerate Application Performance </b>[1:40 pm - 2:00 pm]<br />MongoDB lets you build next-generation applications that require new levels of performance and latency. Flash has become a critical component to meeting these needs and this session will focus on how to best leverage Flash in a MongoDB deployment, covering key best practices and approaches. Armed with these best practices, as your environment scales, the on-going management of Flash within a traditional DAS architecture may still introduce some fundamental challenges. In addition, we will introduce EMC’s XtremIO platform which fully automates and offloads this overhead, allowing MongoDB administrators and architects to focus on driving new capabilities into their applications, all while scaling infinitely. In addition, key features like data-reduction, agile copy services, and free encryption extend the value of Flash well beyond what can be done with traditional DAS architectures.
In today's businesses, an application going down can mean millions of dollars in lost revenue. Learn how to optimize the performance of your enterprise applications powered by MongoDB with IBM Application Performance Management (APM). IBM APM will give you full visibility into your application stack and infrastructure, track every transaction going through it, and help you diagnose problems in mere minutes. With built-in analytics to predict outages before they occur and integration directly into MMS, IBM APM is a must-have solution to keep your business-critical applications up and your revenue flowing.
Consolidate and Simplify MongoDB Infrastructure with All-flashMongoDB
<b>Consolidate and Simplify MongoDB Infrastructure with all-flash </b>[3:40 pm - 4:00 pm]<br />Even the most well-written MongoDB applications can be limited by legacy infrastructure, which is why so many MongoDB customers have migrated their internal storage to an all-flash SAN. Join us in this session as we profile two example customers in their migration to all-flash, where benefits include breakthrough performance, dramatic, reduction in datacenter footprint, and simplified management.
Redis & MongoDB: Stop Big Data Indigestion Before It StartsMongoDB
<b>MongoDB @ Redis Labs </b>[10:40 am - 11:00 am]<br />Efficiently digesting data in large volumes can prove to be challenging for any database. The challenges are compounded when this influx must be analyzed on the fly, or ""tasted"", to satisfy the sophisticated palates of modern apps. Luckily, there are several proven remedies you can concoct with Redis to help with potential indigestion.
Teradata QueryGrid to MongoDB Lightning IntroductionMongoDB
<b>Teradata QueryGrid to MongoDB Lightning Introduction </b> [2:10 pm - 2:30 pm]<br />This is where SQL and NoSQL work together. This session demonstrates the joining MongoDB documents with data warehouse tables to perform new levels of analytics. Seamless self-service dataaccess will be accomplished via a simple SQL JSON notation from Teradata to MongoDB. Now, no more time and effort will be required to co-locate data from both platforms in order to analyze it! Using theTeradata QueryGrid connector to MongoDB enables users to access data on two systems transparently in a self-service manner. This session introduces Teradata’s new capability.
<b>Elevate MongoDB with ODBC/JDBC </b>[4:05 pm - 4:25 pm]<br />Adoption for MongoDB is growing across the enterprise and disrupting existing business intelligence, analytics and data integration infrastructure. Join us to disrupt that disruption using ODBC and JDBC access to MongoDB for instant out-of-box integration with existing infrastructure to elevate and expand your organization’s MongoDB footprint. We'll talk about common challenges and gotchas that shops face when exposing unstructured and semi-structured data using these established data connectivity standards. Existing infrastructure requirements should not dictate developers’ freedom of choice in a database
This webinar will cover new security features in MongoDB 2.6 including x.509 authentication, user defined roles, collection level access control, enterprise features like LDAP authentication and auditing, and many other SSL features. We will first give a brief overview of security features through MongoDB 2.4 then cover new features in 2.6 and coming releases.
Connecting Teradata and MongoDB with QueryGridMongoDB
This is where SQL and NoSQL work together. This session will drill into the technical details on how to join MongoDB documents with data warehouse tables to perform new levels of analytics. And this can be done by business users using popular BI tools. Seamless self-service data access will be accomplished via a simple SQL JSON notation from Teradata to MongoDB. Now, no more time and effort will be required to co-locate data from both platforms in order to analyze it! Using the TeradataQueryGrid connector to MongoDB enables users to access data on two systems transparently in a self-service manner. We will explore how the shards and query routers exchange data with SQL based systems.
Tableau & MongoDB: Visual Analytics at the Speed of ThoughtMongoDB
Tableau enables people to ask questions of their data by bringing analysis and visualization together with revolutionary technology. In this session, you’ll learn how to leverage Tableau and MongoDB for visual analytics of rich JSON data at the speed of thought, dramatically reducing the time-to-insight for users. The talk will include interactive demos and best practices to drive smart and fast business insights.
Evgeniy Karelin. Mongo DB integration example solving performance and high lo...Vlad Savitsky
This presentation is about real life example of using MongoDB on our not specific project for Drupal which supports more than 25m pageviews per day, more than 500k registered users with page load time less than 1sec.
It will give an understanding how MongoDB can be easily used to increase performance of web-site.
My presentation will be as easy as possible with simple examples and schemas but it will require at least intermediate level of developers.
- project and tasks overview. http://freerice.com/ - quiz game site. There are a lot of dynamic info, users (registered and anonymous), groups and their different game statistis, user statuses etc.
- problems while using MySQL
- server optimization attempts. Memcache+Varnish. MySQL replication. Using game as separate script and AJAX blocks with "light" bootstrap.
- MongoDB overview and it's benefits on current project.
- PHP and MongoDB
- project's MongoDB architecture overview
- nodes and MongoDB
- users/groups, their statistics and MongoDB
- switching MySQL to MongoDB in Views.
- indexing problems and statistic calculations.
- multilingual support
- scalability and using MongoDB replica set.
- totals
Этот доклад о применении MongoDB в одном из наших реальных проектов, который на данный момент обслуживает более 25млн показов страниц в день, более 500тыс зарегистрированных пользователей с скоростью загрузки страниц менее 1сек.
Он позволит понять каким образом можно использовать MongoDB для увеличения производительности сайта.
Мой доклад будет на столько простым на сколько это возможно с несложными схемами и примерами, но он требует как минимум среднего уровня разработчиков для полного понимания.
- краткое описание проекта и поставленных задач. http://freerice.com/ - игра-викторина. много динамических данных, группы, игроки (зарегистрированные и анонимусы) и их статистики по разным параметрам, статусы игроков и т.д.
- возникшие проблемы с работой MySQL.
- попытки серверной оптимизации. Memcache+Varnish. Репликация MySQL. Перенос игры в отдельный скрипт и AJAX блоки с использованием "легкого" бутстрапа.
- краткое описание MongoDB и приимущества его применения в текущем проекте.
- PHP и MongoDB
- общее описание архитектуры MongoDB на проекте.
- работа с нодами в MongoDB.
- работа с юзерами/группами и их статистиками.
- переход с MySQL на MongoDB в Views.
- проблемы с индексами, пересчеты статистик.
- поддержка многоязычности.
- масштабируемость MongoDB. Использование реплики.
- итоги
Running Natural Language Queries on MongoDBMongoDB
One of the most sought-after features of any user centric web application is the search functionality. QBurst revamped the search interface by using NLP and integrating with MongoDB. Their solution is designed to identify key components and operators within a natural language query and use it against MongoDB to extract records. This session explains QBurst's technical solution in detail.
Spring Hill (NNP-I 1000): Intel's Data Center Inference Chipinside-BigData.com
Today at Hot Chips 2019, Intel revealed new details of upcoming high-performance AI accelerators: Intel Nervana neural network processors, with the NNP-T for training and the NNP-I for inference. Intel engineers also presented technical details on hybrid chip packaging technology, Intel Optane DC persistent memory and chiplet technology for optical I/O.
"To get to a future state of ‘AI everywhere,’ we’ll need to address the crush of data being generated and ensure enterprises are empowered to make efficient use of their data, processing it where it’s collected when it makes sense and making smarter use of their upstream resources," said Naveen Rao, Intel vice president and GM, Artificial Intelligence Products Group. "Data centers and the cloud need to have access to performant and scalable general purpose computing and specialized acceleration for complex AI applications. In this future vision of AI everywhere, a holistic approach is needed—from hardware to software to applications.”
Learn more: https://www.intel.ai/accelerating-for-ai/?elq_cid=1192980
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
HPC DAY 2017 | Accelerating tomorrow's HPC and AI workflows with Intel Archit...HPC DAY
HPC DAY 2017 - http://www.hpcday.eu/
Accelerating tomorrow's HPC and AI workflows with Intel Architecture
Atanas Atanasov | HPC solution architect, EMEA region at Intel
Accelerate Your Apache Spark with Intel Optane DC Persistent MemoryDatabricks
The capacity of data grows rapidly in big data area, more and more memory are consumed either in the computation or holding the intermediate data for analytic jobs. For those memory intensive workloads, end-point users have to scale out the computation cluster or extend memory with storage like HDD or SSD to meet the requirement of computing tasks. For scaling out the cluster, the extra cost from cluster management, operation and maintenance will increase the total cost if the extra CPU resources are not fully utilized. To address the shortcoming above, Intel Optane DC persistent memory (Optane DCPM) breaks the traditional memory/storage hierarchy and scale up the computing server with higher capacity persistent memory. Also it brings higher bandwidth & lower latency than storage like SSD or HDD. And Apache Spark is widely used in the analytics like SQL and Machine Learning on the cloud environment. For cloud environment, low performance of remote data access is typical a stop gap for users especially for some I/O intensive queries. For the ML workload, it's an iterative model which I/O bandwidth is the key to the end-2-end performance. In this talk, we will introduce how to accelerate Spark SQL with OAP (https://github.com/Intel-bigdata/OAP) to accelerate SQL performance on Cloud to archive 8X performance gain and RDD cache to improve K-means performance with 2.5X performance gain leveraging Intel Optane DCPM. Also we will have a deep dive how Optane DCPM for these performance gains.
Speakers: Cheng Xu, Piotr Balcer
Developing Software for Persistent Memory / Willhalm Thomas (Intel)Ontico
NVDIMMs provide applications the ability to access in-memory data that will survive reboots. This is a huge paradigm shift happening in the industry. Intel has announced new instructions to support persistence. In this presentation, we educate developers on how to take advantage of this new kind of persistent memory tier. Using simple practical examples [3] [4], we discuss how to identify which data structures that are suited for this new memory tier, and which data structures are not. We provide developers a systematic methodology to identify how their applications can be architected to take advantage of persistence in the memory tier. Furthermore, we will provide basic programming examples for persistent memory and present common pitfalls.
Tendências da junção entre Big Data Analytics, Machine Learning e Supercomput...Igor José F. Freitas
O objetivo desta palestra é apresentar aos desenvolvedores como o universo da Computação de Alto Desempenho (computação paralela) está se tornando cada vez mais acessível e se democratizando nos softwares de Big Data e Inteligência Artificial. Supercomputadores que até pouco tempo eram utilizados apenas em indústrias de nicho, setores do governo e pela ciência, estão contribuindo para a solução de grandes desafios da sociedade, da indústria e da ciência. Esta palestra terá uma abordagem técnica envolvendo conceitos de software e hardware com o intuito de provocar o desenvolvedor a fazer uso de grandes servidores para desenvolverem aplicações inovadoras.
Apache CarbonData & Spark meetup
"QATCodec: past, present and future" if from INTEL
Apache Spark™ is a unified analytics engine for large-scale data processing.
CarbonData is a high-performance data solution that supports various data analytic scenarios, including BI analysis, ad-hoc SQL query, fast filter lookup on detail record, streaming analytics, and so on. CarbonData has been deployed in many enterprise production environments, in one of the largest scenario it supports queries on single table with 3PB data (more than 5 trillion records) with response time less than 3 seconds!
E5 Intel Xeon Processor E5 Family Making the Business Case Intel IT Center
This presentation highlights cloud computing advantages of the Intel® Xeon® processor E5 family and helps you make the business case for investing. Includes access to an ROI calculator.
Intel and DataStax: 3D XPoint and NVME Technology Cassandra Storage ComparisonDataStax Academy
Does your choice of storage really matter in a Cassandra deployment? Intel and Datastax engineers will discuss results of recent performance testing on a variety of storage devices including classic spinning media, SATA SSD’s and NVMe SSD’s. Session will include an overview of the various storage types, and technology trends. Next we will discuss our recent testing and look at some preliminary results. Even if you are only at the early stages of considering a Cassandra deployment, fully understanding the impact storage choices have on your results can be critical to your projects success.
AWS Summit Singapore - Make Business Intelligence Scalable and AdaptableAmazon Web Services
Akanksha Bilani, APAC Director, Intel Software
Kapil Bansal, Alliance Head, APJ, Intel
Business, science, and academia are using AI applications — in the data center, the cloud, and at the edge — supported by a broad, growing portfolio of Intel technologies. Come join Kapil Bansal (Alliance Head – APJ) and Akanksha Bilani (APAC Director, Intel Software) to learn how Intel helps make AI initiatives practical and straightforward. Learn from the opportunities AI brings in and be part of the era of the convergence of data and the power of compute.
Optimizing Apache Spark Throughput Using Intel Optane and Intel Memory Drive...Databricks
Apache Spark is a popular data processing engine designed to execute advanced analytics on very large data sets which are common in today’s enterprise use cases. To enable Spark’s high performance for different workloads (e.g. machine-learning applications), in-memory data storage capabilities are built right in.
However, Spark’s in-memory capabilities are limited by the memory available in the server; it is common for computing resources to be idle during the execution of a Spark job, even though the system’s memory is saturated. To mitigate this limitation, Spark’s distributed architecture can run on a cluster of nodes, thus taking advantage of the memory available across all nodes. While employing additional nodes would solve the server DRAM capacity problem, it does so at an increased cost. Intel(R) Memory Drive Technology is a software-defned memory (SDM) technology, which combined with an Intel(R) Optane(TM) SSD, expands the system’s memory.
This combination of Intel(R) Optane(TM) SSD with Intel Memory Drive Technology alleviates those memory limitations that are inherent to Spark, by making more memory available to the operating system and to Spark jobs, transparently.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
Humana, like many companies, is tackling the challenge of creating real-time insights from data that is diverse and rapidly changing. This is our journey of how we used MongoDB to combined traditional batch approaches with streaming technologies to provide continues alerting capabilities from real-time data streams.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch".
This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
Query performance should be the unsung hero of an application, but without proper configuration, can become a constant headache. When used properly, MongoDB provides extremely powerful querying capabilities. In this session, we'll discuss concepts like equality, sort, range, managing query predicates versus sequential predicates, and best practices to building multikey indexes.
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB
Aggregation pipeline has been able to power your analysis of data since version 2.2. In 4.2 we added more power and now you can use it for more powerful queries, updates, and outputting your data to existing collections. Come hear how you can do everything with the pipeline, including single-view, ETL, data roll-ups and materialized views.
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business.
This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
Virtual assistants are becoming the new norm when it comes to daily life, with Amazon’s Alexa being the leader in the space. As a developer, not only do you need to make web and mobile compliant applications, but you need to be able to support virtual assistants like Alexa. However, the process isn’t quite the same between the platforms.
How do you handle requests? Where do you store your data and work with it to create meaningful responses with little delay? How much of your code needs to change between platforms?
In this session we’ll see how to design and develop applications known as Skills for Amazon Alexa powered devices using the Go programming language and MongoDB.
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB
aux Core Data, appréciée par des centaines de milliers de développeurs. Apprenez ce qui rend Realm spécial et comment il peut être utilisé pour créer de meilleures applications plus rapidement.
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
Il n’a jamais été aussi facile de commander en ligne et de se faire livrer en moins de 48h très souvent gratuitement. Cette simplicité d’usage cache un marché complexe de plus de 8000 milliards de $.
La data est bien connu du monde de la Supply Chain (itinéraires, informations sur les marchandises, douanes,…), mais la valeur de ces données opérationnelles reste peu exploitée. En alliant expertise métier et Data Science, Upply redéfinit les fondamentaux de la Supply Chain en proposant à chacun des acteurs de surmonter la volatilité et l’inefficacité du marché.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
3. Intel Non-Volatile Memory Solutions Group
Agenda
What’s happening in the storage device market?
I thought you said this session is about scaling MongoDB*?!
What’s Next?
7. Intel Non-Volatile Memory Solutions Group
Whatis
NVM Express* is a
standardized high
performance
software interface
for PCI Express*
Solid-State Drives
Architected from
the ground up for
SSDs to be more
efficient, scalable,
and manageable
NVM Express is
industry driven to
be extensible for
the needs of both
the client and the
data center
?
If I had asked people
what they wanted,
they would have said
faster horses
- Henry Ford
“
”
8. Intel Non-Volatile Memory Solutions Group
NVM Express* (NVMe) Delivers Best in Class IOPs
0
100000
200000
300000
400000
500000
100% Read 70% Read 0% Read
IOPS 4K Random Workloads
PCIe/NVMe SAS 12Gb/s SATA 6Gb/s HE
Tests document performance of components on a particular test, in specific systems. Differences in hardware, software, or configuration will affect actual performance. Consult other sources of
information to evaluate performance as you consider your purchase. Test and System Configurations: PCI Express* (PCIe*)/NVM Express* (NVMe) Measurements made on Intel® Core™ i7-3770S
system @ 3.1GHz and 4GB Mem running Windows* Server 2012 Standard O/S, Intel PCIe/NVMe SSDs, data collected by IOmeter* tool. PCIe/NVMe SSD is under development. SAS
Measurements from HGST Ultrastar* SSD800M/1000M (SAS) Solid State Drive Specification. SATA Measurements from Intel Solid State Drive DC P3700 Series Product Specification. For more
complete information about performance and benchmark results, visit http://www.intel.com/performance. Source: Intel Internal Testing.
9. Intel Non-Volatile Memory Solutions Group
And Best in Class Sequential Performance
0
500
1000
1500
2000
2500
3000
100% Read 0% Read
MBPs
Sequential Workloads
PCIe/NVMe SAS 12Gb/s SATA 6Gb/s HE
Tests document performance of components on a particular test, in specific systems. Differences in hardware, software, or configuration will affect actual performance.
Consult other sources of information to evaluate performance as you consider your purchase. Test and System Configurations: PCI Express* (PCIe*)/NVM Express* (NVMe)
Measurements made on Intel® Core™ i7-3770S system @ 3.1GHz and 4GB Mem running Windows* Server 2012 Standard O/S, Intel PCIe/NVMe SSDs, data collected by
IOmeter* tool. PCIe/NVMe SSD is under development. SAS Measurements from HGST Ultrastar* SSD800M/1000M (SAS) Solid State Drive Specification. SATA Measurements
from Intel Solid State Drive DC P3700 Series Product Specification. For more complete information about performance and benchmark results, visit
http://www.intel.com/performance. Source: Intel Internal Testing.
11. Intel Non-Volatile Memory Solutions Group
SSD Market Dynamics: Conversion
NVM Express* (NVMe*), Source: Forward Insight and Intel
24 NVMe SSDs fit in 2U
Performance: 11M I/O per sec
Capacity: 48 TB
6 SATA SSD 1 NVMe PCIe
=
Performance & Storage Density
NVMe* SSDs are replacing SATA in the Data Center
Results have been estimated or simulated using internal Intel analysis or architecture simulation or modeling, and provided
to you for informational purposes. Any differences in your system hardware, software or configuration may affect your actual
performance.
12. Intel Non-Volatile Memory Solutions Group
P3700 P3600 P3500
Your Stuff Works Better w/ NVMe*!
Private Cloud DatabaseVirtualization Big data
NVMe SSDs lower
enterprise IT TCO by
enabling increased
Virtual Machine
scalability and
optimizing platform
utilization
P3700 P3600 P3500 P3700 P3600 P3500 P3700 P3600 P3500 P3700 P3600 P3500
Software Defined
Infrastructure or
hyper convergence
is made affordable
with high
performance SSDs
Consistent, low
latency, high
bandwidth
performance of
NVMe shines in
traditional relational
databases
Analytics and NoSQL
databases fully utilize
NVMe performance
to provide near real
time results
NVMe keeps up with
high bandwidth
demands of HPC
designed to speed
up overall workflow
times
HPC
13. Intel Non-Volatile Memory Solutions Group
Agenda
What’s happening in the storage device market?
I thought you said this session is about scaling MongoDB*?!
What’s Next?
14. Intel Non-Volatile Memory Solutions Group
Database Top SSD Use Cases
• DB Logs – promotes faster writes and replication
• Pure SSD for I/O intensive databases of all types (NoSQL*)
• DRAM augmentation. ex SAP HANA* dynamic tiering, Aerospike*
• Intel CAS/B-Cache & TempDB (Sort)
Database
P3700 P3600 P3500
15. Intel Non-Volatile Memory Solutions Group 15
You are outgrowing your fishtank? What now?
What I want… What they are selling me…
16. Intel Non-Volatile Memory Solutions Group 16
Scale out or scale up?
Scaling MongoDB* can be
complicated and expensive
Too much data?
Too many users?
17. Intel Non-Volatile Memory Solutions Group 17
What if I run the database “Out of Memory”?
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Throughputops/s
MongoDB 3.0.1
50% R/W Workload
SATA HDD
"In Memory"
SATA HDD
"Out of
Memory"
19x
Configuration: One “config server”, one “routhing server”, two data nodes. Each data node contains two Intel ® Xeon E5 2640 v2, 8GB ECC DDR3 DRAM, Seagate Barracuda ST2000DM001 2TB 7200 RPM 64MB Cache SATA 6.0Gb/s
2GB
working set
32GB
working set
?
18. Intel Non-Volatile Memory Solutions Group
0
2000
4000
6000
8000
10000
12000
14000
16000
Throughputops/s
MongoDB 3.0.1 "Out of Memory"
50% R/W Workload
NVMe DC P3700 SSD
SATA DC S3710 SSD
SATA HDD "In Memory"
SATA HDD
3.2x
1.7x
18
Scale MongoDB* UP with Intel NVMe SSDs
Configuration: One “config server”, one “routhing server”, two data nodes. Each data node contains two Intel ® Xeon E5 2640 v2, 8GB ECC DDR3 DRAM. HDD=Seagate Barracuda ST2000DM001 2TB 7200 RPM 64MB Cache SATA 6.0Gb/s
SATA SSD is 800GB Intel ® DC S3700. NVMe SSD is 2TB Intel ® DC P3700
32GB working set
2GB
working set
Running “Out of Memory” with SSD is 3.2x faster than “In Memory” with HDD
Can your deployment handle
#thedress-like situations?
Scaling with NVMe SSDs gives
60x out of memory
performance vs HDD!
19x
19. Intel Non-Volatile Memory Solutions Group
0
5000
10000
15000
20000
25000
Throughputops/s
MongoDB 3.0.1 Out of Memory w/SSD
50% R/W Workload
NVMe DC P3700 SSD
SATA DC S3710 SSD
SATA HDD "In Memory"
19
What about managing the scale of more users?
Configuration: One “config server”, one “routhing server”, two data nodes. Each data node contains two Intel ® Xeon E5 2640 v2, 8GB ECC DDR3 DRAM, Seagate Barracuda ST2000DM001 2TB 7200 RPM 64MB Cache SATA 6.0Gb/s
?
32GB working set
2GB
working
set
#thedress? Really?
Wikipedia averages more
than 143k page reads
per second!
20. Intel Non-Volatile Memory Solutions Group 20
Scale “In Memory” MongoDB* UP w/ Intel NVMe SSDs
Configuration: One “config server”, one “routhing server”, two data nodes. Each data node contains two Intel ® Xeon E5 2640 v2, 8GB ECC DDR3 DRAM. HDD=Seagate Barracuda ST2000DM001 2TB 7200 RPM 64MB Cache SATA 6.0Gb/s
SATA SSD is 800GB Intel ® DC S3700. NVMe SSD is 2TB Intel ® DC P3700
Running “In Memory” with SSD is 5x faster than “In Memory” with HDD
Why not just use SATA SSD?
The industry is transitioning to
NVMe SSDs, with even
faster & cheaper
options coming!
Don’t get caught by Dressgate!
0
5000
10000
15000
20000
25000
Throughputops/s
MongoDB 3.0.1 In Memory
50% R/W Workload
NVMe DC P3700 SSD
SATA DC S3710 SSD
NVMe DC P3700 SSD
"Out of Memory"
SATA HDD
5x
1.5x
32GB
working
set
2GB working set
21. Intel Non-Volatile Memory Solutions Group
0
0.25
0.5
0.75
1
RelativeCost
(Lowerisbetter)
Relative MongoDB Hardware Cost
for Wikipedia*-like Deployment
NVMe P3700 SSD
"In Memory"
SATA S3710 SSD
"In Memory"
NVMe P3700 SSD
"Out of Memory"
SATA S3710 SSD
"Out of Memory"
HDD "In Memory"
1/4th
21
Estimated Hardware Cost Savings with SSDs
Intel NVMe SSDs with MongoDB reduces scale out HW costs by ~75%*
Eliminating power draw from 80 data node servers makes an amazing TCO!
ISH
*Cost data estimated by Intel, which contains many assumptions.
Actual results may vary. Assumptions: current market pricing for
components per internet search, workload assumptions based on
traffic data at stats.wikimedia.org, same number of Mongod and
Mongoc nodes in each case. Configuration cost estimated using
SuperMicro SSG-2072R-E1CR24L data node servers. Each server
configured with two Intel ® Xeon E5-2640 v2 CPU, 16GB DDR3 1600
ECC DRAM; 2x 600GB 2.5" SAS HDD in RAID 1 (HDD) or 400GB Intel (r)
DC S3510 SSD (SATA SSD) or 400GB Intel (r) DC P3500 SSD (NVMe
SSD). Configuration intended to mimic Engilish Wikipedia*, assuming
160GB data set size, a load of 8.6 million page views per hour average
x3 to account for daily fluctuations in traffic. Measured 50% read /
50% write performance data included in this material for each
configuration. System costs per internet search May 2015. System and
component price may vary, consult your reseller for current prices.
Results have been estimated or simulated using internal Intel analysis or architecture simulation or modeling, and provided
to you for informational purposes. Any differences in your system hardware, software or configuration may affect your actual
performance.
22. Intel Non-Volatile Memory Solutions Group
Agenda
22
What’s happening in the storage device market?
I thought you said this session is about scaling MongoDB*?!
What’s Next?
23. Intel Non-Volatile Memory Solutions Group
20x to >200x
better than
others*
Replace 1300
Hard Disk
Drives w/1
NVMe SSD*
Intel Platform
Ingredients:
Better
Together
Why Intel SSDs…
Amazing
Reliability and
Data Integrity
Consistent
Scalable
Performance
Platform
Connected
Solutions
*Results have been estimated or simulated using internal Intel analysis or architecture simulation or modeling, and provided
to you for informational purposes. Any differences in your system hardware, software or configuration may affect your actual
performance.
24. Intel Non-Volatile Memory Solutions Group
Intel® SSD DC P3700 Series
Capacity
Performance
Intel® SSD DC P3600 Series Intel® SSD DC P3500 Series
800
GB
400
GB
1.6TB 2TB 800
GB
400
GB
1.6TB 2TB1.2TB
400
GB 2TB1.2TB
Endurance
10
DWPD
3
DWPD
0.3
DWPD
High Endurance
Technology
Mixed use Read
Intensive
Random 4k Read 450k IOPS
Random 4k Write 175k IOPS
Random 4k 70/30 R/W 265k IOPS
Sequential Read 2800 MB/s
Sequential Write 2000 MB/s
450k IOPS
56k IOPS
160k IOPS
2600 MB/s
1700 MB/s
450k IOPS
35k IOPS
85k IOPS
2500 MB/s
1700 MB/s
Sequential latency of 20µs
Tests document performance of components on a particular test, in specific systems. Differences in hardware, software, or configuration will affect actual performance. Consult other sources of
information to evaluate performance as you consider your purchase. For more complete information about performance and benchmark results, visit www.intel.com/benchmarks. Configurations:
Intel Core i7-3770K CPU @ 3.50GHz, 8GB of system memory, Windows* Server 2012, IOMeter. Random performance is collected with 4 workers each with 32 QD
25. Intel Non-Volatile Memory Solutions Group
Future Memory and Storage Hierarchy
NVM Solutions continue to bring data closer to the processor
Processor
L1/2 Cache
L3 Cache
Main
Memory
Fast HDD
~1 ns
~10 ns
~100 ns
~10,000,000 ns (10 ms)
On Core CPU
On Die
Direct Attach
SAS, SATA*
Interfaces Relative Delay
Costs
NAND SSD ~100,000 ns (100 us)SAS, SATA
NAND SSD
~10,000 ns (10us)
~100,000 ns (100 us)
PCIe*/NVMe*,
SAS, SATA
NVMe
25Source: Intel
NVM Express* (NVMe)
PCI Express* (PCIe)
Performance
Next Gen NVM
3D NAND