Using ZFS file system with MySQL
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The document discusses ZFS (Zettabyte File System) principles and its advantages over traditional storage systems, emphasizing pooled storage, data integrity, and self-healing capabilities. It details the setup of ZFS with MySQL, including configuration steps and performance metrics, highlighting significant improvements in efficiency and speed. Challenges such as fragmentation and tuning complexities are also acknowledged, alongside the benefits of ZFS for enterprise-grade transactional systems.
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Coordinated Disclosure for ML - What's Different and What's the Same.pdfPriyanka Aash
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Coordinated Disclosure for ML - What's Different and What's the SameFrom Manual to Auto Searching- FME in the Driver's Seat
From Manual to Auto Searching- FME in the Driver's SeatSafe Software
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Finding a specific car online can be a time-consuming task, especially when checking multiple dealer websites. A few years ago, I faced this exact problem while searching for a particular vehicle in New Zealand. The local classified platform, Trade Me (similar to eBay), wasnāt yielding any results, so I expanded my search to second-hand dealer sitesāonly to realise that periodically checking each one was going to be tedious. Thatās when I noticed something interesting: many of these websites used the same platform to manage their inventories. Recognising this, I reverse-engineered the platformās structure and built an FME workspace that automated the search process for me. By integrating API calls and setting up periodic checks, I received real-time email alerts when matching cars were listed. In this presentation, Iāll walk through how I used FME to save hours of manual searching by creating a custom car-finding automation system. While FME canāt buy a car for youāyetāit can certainly help you find the one youāre after!You are not excused! How to avoid security blind spots on the way to production
You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante
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We live in an ever evolving landscape for cyber threats creating security risk for your production systems. Mitigating these risks requires participation throughout all stages from development through production delivery - and by every role including architects, developers QA and DevOps engineers, product owners and leadership. No one is excused! This session will cover examples of common mistakes or missed opportunities that can lead to vulnerabilities in production - and ways to do better throughout the development lifecycle.Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines o...
Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines o...All Things Open
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Presented at All Things Open RTP Meetup
Presented by William Hill - Developer Advocate, NVIDIA
Title: Agentic AI for Developers and Data Scientists
Build an AI Agent in 10 Lines of Code and the Concepts Behind the Code
Abstract: In this talk we will demonstrate building a working data science AI agent in 10 lines of basic Python code in a Colab notebook. Our AI Agent will perform LLM prompt-driven visual analysis using open-source libraries. In this session we will show how to develop an AI Agent using GPUs through NVIDIAās developer program and Google Colab notebooks. After coding our AI Agent, we will break down the 10 lines of code. We will show the key components and open source library integrations that enable the agent's functionality, focusing on practical implementation details and then the theoretical concepts. The presentation concludes with a survey of current LLM technologies and the latest trends in developing AI applications for enthusiasts and enterprisesCurietech AI in action - Accelerate MuleSoft development
Curietech AI in action - Accelerate MuleSoft developmentshyamraj55
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CurieTech AI in Action ā Accelerate MuleSoft Development
Overview:
This presentation demonstrates how CurieTech AIās purpose-built agents empower MuleSoft developers to create integration workflows faster, more accurately, and with less manual effort
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Key Highlights:
Dedicated AI agents for every stage: Coding, Testing (MUnit), Documentation, Code Review, and Migration
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DataWeave automation: Generate mappings from tables or samplesā95%+ complete within minutes
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Integration flow generation: Auto-create Mule flows based on specificationsāspeeds up boilerplate development
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Efficient code reviews: Gain intelligent feedback on flows, patterns, and error handling
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Test & documentation automation: Auto-generate MUnit test cases, sample data, and detailed docs from code
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Why Now?
Achieve 10Ć productivity gains, slashing development time from hours to minutes
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Maintain high accuracy with code quality matching or exceeding manual efforts
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Ideal for developers, architects, and teams wanting to scale MuleSoft projects with AI efficiency
Conclusion:
CurieTech AI transforms MuleSoft development into an AI-accelerated workflowāletting you focus on innovation, not repetition."How to survive Black Friday: preparing e-commerce for a peak season", Yurii ...
"How to survive Black Friday: preparing e-commerce for a peak season", Yurii ...Fwdays
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We will explore how e-commerce projects prepare for the busiest time of the year, which key aspects to focus on, and what to expect. Weāll share our experience in setting up auto-scaling, load balancing, and discuss the loads that Silpo handles, as well as the solutions that help us navigate this season without failures.Cracking the Code - Unveiling Synergies Between Open Source Security and AI.pdf
Cracking the Code - Unveiling Synergies Between Open Source Security and AI.pdfPriyanka Aash
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Cracking the Code - Unveiling Synergies Between Open Source Security and AICapCut Pro Crack For PC Latest Version {Fully Unlocked} 2025
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CapCut Pro Crack is a powerful tool that has taken the digital world by storm, offering users a fully unlocked experience that unleashes their creativity. With its user-friendly interface and advanced features, itās no wonder why aspiring videographers are turning to this software for their projects.The Future of Product Management in AI ERA.pdf
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Hi, Iām Aly Owens, I have a special pleasure to stand here as over a decade ago I graduated from CityU as an international student with an MBA program. I enjoyed the diversity of the school, ability to work and study, the network that came with being here, and of course the price tag for students here has always been more affordable than most around.
Since then I have worked for major corporations like T-Mobile and Microsoft and many more, and I have founded a startup. I've also been teaching product management to ensure my students save time and money to get to the same level as me faster avoiding popular mistakes. Today as Iāve transitioned to teaching and focusing on the startup, I hear everybody being concerned about Ai stealing their jobsā¦ Weāll talk about it shortly.
But before that, I want to take you back to 1997. One of my favorite movies is āFifth Elementā. It wowed me with futuristic predictions when I was a kid and Iām impressed by the number of these predictions that have already come true. Self-driving cars, video calls and smart TV, personalized ads and identity scanning. Sci-fi movies and books gave us many ideas and some are being implemented as we speak. But we often get ahead of ourselves:
Flying cars,Colonized planets, Human-like AI: not yet, Time travel, Mind-machine neural interfaces for everyone: Only in experimental stages (e.g. Neuralink).
Cyberpunk dystopias: Some vibes (neon signs + inequality + surveillance), but not total dystopia (thankfully).
On the bright side, we predict that the working hours should drop as Ai becomes our helper and there shouldnāt be a need to work 8 hours/day. Nobody knows for sure but we can require that from legislation. Instead of waiting to see what the government and billionaires come up with, I say we should design our own future.
So, we as humans, when we donāt know something - fear takes over. The same thing happened during the industrial revolution. In the Industrial Era, machines didnāt steal jobsāthey transformed them but people were scared about their jobs. The AI era is making similar changes except it feels like robots will take the center stage instead of a human. First off, even when it comes to the hottest space in the military - drones, Ai does a fraction of work. AI algorithms enable real-time decision-making, obstacle avoidance, and mission optimization making drones far more autonomous and capable than traditional remote-controlled aircraft. Key technologies include computer vision for object detection, GPS-enhanced navigation, and neural networks for learning and adaptation. But guess what? There are only 2 companies right now that utilize Ai in drones to make autonomous decisions - Skydio and DJI.
UserCon Belgium: Honey, VMware increased my bill
UserCon Belgium: Honey, VMware increased my billstijn40
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VMwareās pricing changes have forced organizations to rethink their datacenter cost management strategies. While FinOps is commonly associated with cloud environments, the FinOps Foundation has recently expanded its framework to include Scopesāand Datacenter is now officially part of the equation. In this session, weāll map the FinOps Framework to a VMware-based datacenter, focusing on cost visibility, optimization, and automation. Youāll learn how to track costs more effectively, rightsize workloads, optimize licensing, and drive efficiencyāall without migrating to the cloud. Weāll also explore how to align IT teams, finance, and leadership around cost-aware decision-making for on-prem environments. If your VMware bill keeps increasing and you need a new approach to cost management, this session is for you!GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdf
GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdfPriyanka Aash
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GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now9-1-1 Addressing: End-to-End Automation Using FME
9-1-1 Addressing: End-to-End Automation Using FMESafe Software
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This session will cover a common use case for local and state/provincial governments who create and/or maintain their 9-1-1 addressing data, particularly address points and road centerlines. In this session, you'll learn how FME has helped Shelby County 9-1-1 (TN) automate the 9-1-1 addressing process; including automatically assigning attributes from disparate sources, on-the-fly QAQC of said data, and reporting. The FME logic that this presentation will cover includes: Table joins using attributes and geometry, Looping in custom transformers, Working with lists and Change detection.PyCon SG 25 - Firecracker Made Easy with Python.pdf
PyCon SG 25 - Firecracker Made Easy with Python.pdfMuhammad Yuga Nugraha
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Explore the ease of managing Firecracker microVM with the firecracker-python. In this session, I will introduce the basics of Firecracker microVM and demonstrate how this custom SDK facilitates microVM operations easily. We will delve into the design and development process behind the SDK, providing a behind-the-scenes look at its creation and features. While traditional Firecracker SDKs were primarily available in Go, this module brings a simplicity of Python to the table.OpenACC and Open Hackathons Monthly Highlights June 2025
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The OpenACC organization focuses on enhancing parallel computing skills and advancing interoperability in scientific applications through hackathons and training. The upcoming 2025 Open Accelerated Computing Summit (OACS) aims to explore the convergence of AI and HPC in scientific computing and foster knowledge sharing. This year's OACS welcomes talk submissions from a variety of topics, from Using Standard Language Parallelism to Computer Vision Applications. The document also highlights several open hackathons, a call to apply for NVIDIA Academic Grant Program and resources for optimizing scientific applications using OpenACC directives.āMPU+: A Transformative Solution for Next-Gen AI at the Edge,ā a Presentation...
āMPU+: A Transformative Solution for Next-Gen AI at the Edge,ā a Presentation...Edge AI and Vision Alliance
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You are not excused! How to avoid security blind spots on the way to production
You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante
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Using ZFS file system with MySQL
- 1. MySQL on ZFS Bajrang Panigrahi August, 2019
- 2. ZFS Principles ā Pooled storage ā Completely eliminates the antique notion of volumes ā Does for storage what VM did for memory ā Transactional object system ā Always consistent on disk ā no fsck, ever ā Provable end-to-end data integrity ā Detects and corrects silent data corruption ā Simple administration ā Concisely express your intent
- 3. FS/Volume Model vs Pooled Storage Traditional Volumes ā Abstraction: virtual disk ā Partition/volume for each FS ā Grow/shrink by hand ā Each FS has limited bandwidth ā Storage is fragmented, stranded ZFS Pooled Storage ā Abstraction: malloc/free ā No partitions to manage ā Grow/shrink automatically ā All bandwidth always available ā All storage in the pool is shared Storage PoolVolume FS Volume FS Volume FS ZFS ZFS ZFS
- 4. NFS SMB Local files VFS Filesystem (e.g. UFS, ext3) Volume Manager (e.g. LVM, SVM) NFS SMB Local files VFS DMU (Data Management Unit) SPA (Storage Pool Allocator) iSCSI FC SCSI target ZPL (ZFS POSIX Layer) ZVOL (ZFS Volume) Block interface ZFS Block allocate+write, read, free Atomic transactions on objects File interface
- 5. Beneļ¬ts of ZFS ā Copy-on-Write (CoW) File System. ā Throttles writes. ā Data integrity and resiliency. ā Self Healing of Data on ZFS. ā Block size matching.(Allows Variable Block size) ā Snapshots & Clones ā Active development community
- 6. Copy-On-Write Transactions 1. Initial block tree 2. COW some blocks 4. Rewrite uberblock (atomic)3. COW indirect blocks
- 7. Block Pointer Structure in ZFS First copy of data When the block was written Checksum of data this block points to padding physical birth txg logical birth txg fill count 256-bit checksum BDX lvl type PSIZEcomp LSIZE offset1 offset2 offset3 vdev1 vdev2 vdev3 ASIZE ASIZE ASIZE cksum Second copy of data (for metadata) Third copy of data (pool-wide metadata)
- 8. END-to-END Data Integrity in ZFS ZFS validates the entire I/O path ā Bit rot ā Phantom writes ā Misdirected reads and writes ā DMA parity errors ā Driver bugs ā Accidental overwrite Disk checksum only validates media ā Bit rot ā Phantom writes ā Misdirected reads and writes ā DMA parity errors ā Driver bugs ā Accidental overwrite Disk Block Checksums ā Checksum stored with data block ā Any self-consistent block will pass ā Can't detect stray writes ā Inherent FS/volume interface limitation Data Data Data Checksum Data Checksum ZFS Data Authentication ā Checksum stored in parent block pointer ā Fault isolation between data and checksum ā Checksum hierarchy forms self-validating Merkle tree Address Checksum Checksum Address ā¢ ā¢ ā¢ Address Checksum Checksum Address
- 9. Self Healing of Data in ZFS Application ZFS mirror Application ZFS mirror Application ZFS mirror 1. Application issues a read. Checksum reveals that the block is corrupt on disk. 2. ZFS tries the next disk. Checksum indicates that the block is good. 3. ZFS returns good data to the application and repairs the damaged block.
- 10. Initial Use case at Zeneļ¬ts We use AWS snapshot to rebuild a new DB for dev/ops; the first access to the data is slow because āNew volumes created from existing EBS snapshots load lazily in the backgroundā Multiple DB clusters data needed for generating the DB for dev/ops -- We use Multi-Source Replication.
- 11. Alternatives Multiple EBS Volume attached as Slave MySQL, and rotate on fresh snapshot request Con: Additional EBS volumes, will still have the problem of initial load of queries (Taking snap at every 15 mins) Use Percona Xtrabackup as an Incremental Data Copy to the Spoof Instance. Con: Requires an additional EBS volume and MySQL Service needs to be shutdown during the entire period the backup is restored. Use ZFS file system as a mechanism of taking a snapshot at the file system level
- 12. Setting up ZFS on MySQL ā Create a pool name āZP1ā zpool create -O compression=gzip -f -o autoexpand=on "zp1" mirror "/dev/xvdm" "/dev/xvdn" -o ashift=12 ā Create a new ļ¬lesystem named ādata2ā in POOL āZP1ā #Create the ZFS Filesystems - name: Create a new file system called data2 in pool zp1 zfs: name: zp1/mysql state: present extra_zfs_properties: setuid: off compression: gzip recordsize: 128k atime: off primarycache: metadata
- 13. Setting up ZFS on MySQL ā Create the required datasets to run MySQL zp1/mysql 1.19T 4.92T 100K /zp1/mysql zp1/mysql/data 1.18T 4.92T 1.17T /data2/data zp1/mysql/logs 9.97G 4.92T 8.84G /data2/logs zp1/mysql/tmp 216K 4.92T 152K /data2/tmp ā Conļ¬gurations on MySQL Innodb_doublewrite = 0 Innodb_checksum_algorithm = none Innodb_use_native_aio = 0
- 14. ZPOOL Status ā ZPOOL status zpool status pool: zp1 state: ONLINE scan: none requested config: NAME STATE READ WRITE CKSUM zp1 ONLINE 0 0 0 mirror-0 ONLINE 0 0 0 xvdm ONLINE 0 0 0 xvdn ONLINE 0 0 0 errors: No known data errors
- 15. ZFS List NAME USED AVAIL REFER MOUNTPOINT zp1 1.20T 4.92T 104K /zp1 zp1/mslave03 1.11G 4.92T 100K /zp1/mslave03 zp1/mslave03/data 1.11G 4.92T 1.17T /data3/data zp1/mslave03/logs 308K 4.92T 340K /data3/logs zp1/mslave03/tmp 96K 4.92T 128K /data3/tmp zp1/mslave04 686M 4.92T 100K /zp1/mslave04 zp1/mslave04/data 686M 4.92T 1.17T /data4/data zp1/mslave04/logs 300K 4.92T 332K /data4/logs zp1/mslave04/tmp 96K 4.92T 128K /data4/tmp zp1/mysql 1.19T 4.92T 100K /zp1/mysql zp1/mysql/data 1.18T 4.92T 1.17T /data2/data zp1/mysql/logs 10.2G 4.92T 8.78G /data2/logs zp1/mysql/tmp 216K 4.92T 152K /data2/tmp
- 16. Incremental Send and Receive zfs send zp1/mysql/data@monday | ssh host zfs receive zp1/recvd/fs zfs send -i @monday zp1/mysql/data@tuesday | ssh .. āFromSnapā āToSnapā
- 17. ZFS - Design - Local Clones
- 18. ZFS - Design - Remote Clones
- 19. ZFS - usage metrics KEY Old_ENV New_ENV Performance - Page Load 2-3 minutes ~15 secs Faster Data Snapshots 15 minutes ~2 - 4 secs Cloning / EBS attachment > 20 minutes ~ 3 - 5 secs Costs: Higher* Lower Monitoring / Alerting only Slack messages Jenkins + PagerDuty
- 20. ZFS - Performance Benchmarking
- 21. ZFS - Challenges ā Fragmentation. ā Complex to tweak and tune. ā Requires extra free space or pool performance can suffer.
- 22. Further ... ā High Read throughput (>= 83.88 million) ā MySQL / sec upto 76.2 K ā InnoDB file I/O write upto 150K ā Enterprise-grade transactional file system. ā Automatically reconstructs data after detecting an error. ā Multiple physical media devices into one logical volume using ZPOOL. ā Snapshot and Mirroring capabilities, and can quickly compress data. (LZ4) Enjoy a user-friendly, high-volume storage system.
- 23. Thank you.