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Pure1 Feature Request: Assets > Genealogy milestones overlaid on Analytics > Performance graphs
It would be helpful in analyzing and correlating array performance profile changes if genealogy events for an array (either controller replacement via Evergreen NDU or even PurityOE upgrades vis SSU) could be overlaid on the Analytics > Performance graph data for the array and time period selected. I attached a crude mockup of the idea, but I'm sure it could be done much more elegantly and less visually intrusively with a small floating tick mark with a hover effect showing the genealogy change to the array. It could get complicated when graphing performance data for multiple arrays, but could be handled in the same way it is for the performance graphing with a different color per array. Or instead of overlaying the genealogy data as default, perhaps add a "Display Genealogy" checkbox option to the performance graphs. And I'll use this opportunity to once again beat the drums for extending the data retention in Pure1 to >30 days to help identify longer performance trends, which are sometimes not easily visible in a max 30-day performance data windows.Flashblade & Ansible collection
Hello community, We are facing some issues with our FlashBlade when provisioning policies with Ansible. We have updated all the components (py-pure-client and the collection), and we are failing on a simple policy deployment. - name: Test purestorage.flashblade.purefb_policy: name: "filesystem_export_policy" enabled: True policy_type: "nfs" at: True client: "mynfsexport.flashblade.local" secure: True security: "sys" permission: "rw" access: "no-squash" fb_url: "{{ fb_host }}" api_token: "{{ fb_api_token }}" The complete stack trace is : The full traceback is: Traceback (most recent call last): File "/home/user/.ansible/tmp/ansible-tmp-1778656887.1852276-3067455-48057529014464/AnsiballZ_purefb_policy.py", line 107, in <module> _ansiballz_main() File "/home/user/.ansible/tmp/ansible-tmp-1778656887.1852276-3067455-48057529014464/AnsiballZ_purefb_policy.py", line 99, in _ansiballz_main invoke_module(zipped_mod, temp_path, ANSIBALLZ_PARAMS) File "/home/user/.ansible/tmp/ansible-tmp-1778656887.1852276-3067455-48057529014464/AnsiballZ_purefb_policy.py", line 47, in invoke_module runpy.run_module(mod_name='ansible_collections.purestorage.flashblade.plugins.modules.purefb_policy', init_globals=dict(_module_fqn='ansible_collections.purestorage.flashblade.plugins.modules.purefb_policy', _modlib_path=modlib_path), File "<frozen runpy>", line 226, in run_module File "<frozen runpy>", line 98, in _run_module_code File "<frozen runpy>", line 88, in _run_code File "/opt/tmp/tmp/ansible_purestorage.flashblade.purefb_policy_payload_xaietsz7/ansible_purestorage.flashblade.purefb_policy_payload.zip/ansible_collections/purestorage/flashblade/plugins/modules/purefb_policy.py", line 4233, in <module> File "/opt/tmp/tmp/ansible_purestorage.flashblade.purefb_policy_payload_xaietsz7/ansible_purestorage.flashblade.purefb_policy_payload.zip/ansible_collections/purestorage/flashblade/plugins/modules/purefb_policy.py", line 4008, in main File "/opt/tmp/tmp/ansible_purestorage.flashblade.purefb_policy_payload_xaietsz7/ansible_purestorage.flashblade.purefb_policy_payload.zip/ansible_collections/purestorage/flashblade/plugins/modules/purefb_policy.py", line 2392, in update_nfs_policy AttributeError: 'ErrorResponse' object has no attribute 'items' fatal: [localhost]: FAILED! => { "changed": false, "module_stderr": "Traceback (most recent call last):\n File \"/home/user/.ansible/tmp/ansible-tmp-1778656887.1852276-3067455-48057529014464/AnsiballZ_purefb_policy.py\", line 107, in <module>\n _ansiballz_main()\n File \"/home/user/.ansible/tmp/ansible-tmp-1778656887.1852276-3067455-48057529014464/AnsiballZ_purefb_policy.py\", line 99, in _ansiballz_main\n invoke_module(zipped_mod, temp_path, ANSIBALLZ_PARAMS)\n File \"/home/user/.ansible/tmp/ansible-tmp-1778656887.1852276-3067455-48057529014464/AnsiballZ_purefb_policy.py\", line 47, in invoke_module\n runpy.run_module(mod_name='ansible_collections.purestorage.flashblade.plugins.modules.purefb_policy', init_globals=dict(_module_fqn='ansible_collections.purestorage.flashblade.plugins.modules.purefb_policy', _modlib_path=modlib_path),\n File \"<frozen runpy>\", line 226, in run_module\n File \"<frozen runpy>\", line 98, in _run_module_code\n File \"<frozen runpy>\", line 88, in _run_code\n File \"/opt/tmp/tmp/ansible_purestorage.flashblade.purefb_policy_payload_xaietsz7/ansible_purestorage.flashblade.purefb_policy_payload.zip/ansible_collections/purestorage/flashblade/plugins/modules/purefb_policy.py\", line 4233, in <module>\n File \"/opt/tmp/tmp/ansible_purestorage.flashblade.purefb_policy_payload_xaietsz7/ansible_purestorage.flashblade.purefb_policy_payload.zip/ansible_collections/purestorage/flashblade/plugins/modules/purefb_policy.py\", line 4008, in main\n File \"/opt/tmp/tmp/ansible_purestorage.flashblade.purefb_policy_payload_xaietsz7/ansible_purestorage.flashblade.purefb_policy_payload.zip/ansible_collections/purestorage/flashblade/plugins/modules/purefb_policy.py\", line 2392, in update_nfs_policy\nAttributeError: 'ErrorResponse' object has no attribute 'items'\n", "module_stdout": "", "msg": "MODULE FAILURE: No start of json char found\nSee stdout/stderr for the exact error", "rc": 1 } If the policy is already set, the playbook runs perfectly fine. The problem occurs when a new policy is injected. However, we have to install the collection from GitHub. It seems that the artifact built and installed by Ansible Galaxy is not correct. Some commits are not propagated. For example, with the FlashBlade collection 1.24 (installed from Galaxy), we have: >>> from pypureclient.flashblade import ( ... PolicyRuleObjectAccessCondition, ... PolicyRuleObjectAccessPost, ... PolicyRuleObjectAccess, ... NfsExportPolicy, ... NfsExportPolicyRule, ... Policy, ... PolicyPatch, ... PolicyRule, ... SmbSharePolicyRule, ... SmbSharePolicy, ... SmbClientPolicyRule, ... SmbClientPolicy, ... ObjectStoreAccessPolicyPost, ... NetworkAccessPolicy, ... NetworkAccessPolicyRule, ... WormDataPolicy, ... ) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/user/conda/envs/ansible/lib/python3.12/site-packages/pypureclient/flashblade/__init__.py", line 40, in __getattr__ raise ImportError(f'module {__package__} has no attribute {name}') ImportError: module pypureclient.flashblade has no attribute PolicyPatch We have compared the archive from GitHub and there are many differences with the upstream collection code. To help, find my my env package : Package Version ----------------- ----------- annotated-types 0.7.0 ansible-core 2.18.5 bcrypt 5.0.0 certifi 2026.4.22 cffi 2.0.0 cryptography 44.0.0 dnspython 2.8.0 invoke 3.0.3 Jinja2 3.1.4 MarkupSafe 2.1.1 packaging 24.1 paramiko 5.0.0 pip 24.2 py-pure-client 1.88.0 pycparser 2.22 pydantic 2.13.4 pydantic_core 2.46.4 PyJWT 2.12.1 PyNaCl 1.6.2 python-dateutil 2.9.0.post0 PyYAML 6.0.2 resolvelib 1.0.1 setuptools 75.8.2 six 1.17.0 typing_extensions 4.15.0 typing-inspection 0.4.2 urllib3 2.7.0 wheel 0.45.0 And our flashblades are running the Purity//FB 4.6.9. If anyone has a lead to help us get our Ansible back up and running, I thank them in advance. We appreciate your help. Matth
Veeam v13 Integration and Plugin
Hi Everyone, We're new Pure customers this year and have two Flasharray C models, one for virtual infrastructure and the other will be used solely as a storage repository to back up those virtual machines using Veeam Backup and Replication. Our plan is to move away from the current windows-based Veeam v12 in favor of Veeam v13 hardened Linux appliances. We're in the design phase now but have Veeam v13 working great in separate environment with VMware and HPE Nimble. Our question is around Pure Storage and Veeam v13 integration and Plugin support. Veeam's product team mentions there is native integrations in v12, but that storage vendors should be "adopting USAPI" going forward. Is this something that Pure is working on, or maybe already has completed with Veeam Backup and Replication v13?
Azure Local now works with FlashArray
A little bit of catch up here, but Azure Local is now fully GA with support for on-premises FlashArray. That means you can easily expand your current environment with FA external storage, or start a net new deployment. And yes, you can use an existing FlashArray to do it. Azure Local is shaping up to be a good VMware alternative as well as a strong solution for those needing soverign clouds, as Microsoft has a lot of support for regulatory frameworks and in-country data centers. You can learn more about it in the blog and feel free to post any questions here. We also have a webinar coming up on May 21, 9:00am PT, 12:00pm ET, so you can learn more about it.Everpure Accelerate 2026
Hey gang, Everpure Accelerate is approaching fast. June 16-18th in Las Vegas. Chime in if you will be attending. I've been fortunate to have attended the last few years and always find this event to be full of great information. Not to mention the training classes and certification tests are included. Hope to see some of you out there!
Claude Mythos: The Next Frontier of Autonomous Cyber Intelligence
Model Performance and Capabilities Claude Mythos represents a significant performance leap for Anthropic, reportedly beating their current best Opus model by a large margin. This kind of improvement hasn't been seen since OpenAI released their reasoning model O1 in September 2024. Key performance metrics include: Coding ability: 77% on SWE-Bench Pro (compared to Opus at 53%) Terminal usage: Substantial improvements in the model's ability to use terminal commands General purpose: Despite the cybersecurity focus in marketing, Mythos is a general-purpose LLM like other Claude models Cybersecurity Focus and Access Restrictions Anthropic has positioned Mythos around cybersecurity concerns, emphasizing AI as a potential national security risk - similar to OpenAI's approach with GPT-2 in 2019. However, the model is not cybersecurity-specific but rather a general-purpose AI. Limited Release Strategy: Anthropic has restricted access to select partners, most of whom are investors in the company: Microsoft (Series C and G investor) NVIDIA (Series G) JP Morgan (conventional loan, May 2025) Google (Series C and E, plus convertible debt) Amazon (Series D and E) Cisco (Series E) Market Implications and Competitive Advantages This restricted access creates what the video calls "privatization of tokens," giving certain companies advantages in: Cybersecurity: Finding vulnerabilities (benefiting companies like Cisco, Palo Alto) Legal services: Discovering legal loopholes and litigation strategies Finance and software development: Enhanced capabilities across various domains The core issue isn't cybersecurity itself, but rather the rapid improvement in AI capabilities outpacing society's ability to adapt. Infrastructure and Pricing Infrastructure Dependencies: Despite committing $50 billion to data centers in Texas and New York, Anthropic still relies on partners (Amazon, Google, Microsoft) for training and inference. Pricing Structure: Mythos will cost $125 per million output tokens Available through cloud APIs (Amazon Bedrock, Google Cloud Vertex, Microsoft Foundry) Unlikely to be included in subsidized Pro and Max plans Comparable to OpenAI's GPT-4 Pro at $180 per million tokens Business Strategy and Market Position IPO Positioning: The Mythos release strategically positions Anthropic for a potential IPO, with the company recently surpassing OpenAI by achieving $30 billion in annualized run rate (ARR) - though this is run rate rather than the more conservative annual recurring revenue metric. Adoption Challenges: The rapid advancement creates both excitement and concern, highlighting the growing divide between companies that adopt AI quickly and those that don't. The key is matching the right level of AI intelligence to appropriate tasks rather than using premium models for basic workflows. Future Outlook Based on historical patterns (like DeepSeek R1 catching up to OpenAI's O1 within 5 months), the performance gap created by Mythos will likely be bridged by competitors relatively quickly. The real competitive advantage lies in how quickly companies can adopt and properly allocate AI intelligence to solve complex problems.Ask Us Everything about Pure1® Self-service!
💬 Get ready for our first May 2026 edition of Ask Us Everything, this Friday, May 1st at 9 AM Pacific. This month is all about Pure1® Self-service. If you have a burning question, feel free to ask it here early and we'll add it to the list to answer on Friday. Or if we can't get it answered live, our Everpure experts can follow up here. jclark, mbradford plus dpoorman dpoorman are the moderators and experts answering your questions during the conversation as well as here on the community. See you this Friday! (Oh, and if you haven't registered yet, there's still time!)Ask Us Everything Recap: Rethinking Storage with the Intelligent Control Plane
The latest Ask Us Everything session focused on a topic that’s quickly becoming central to Everpure’s strategy: the intelligent control plane. And based on the questions from the community, it’s clear that many teams are starting to think beyond individual arrays and toward managing storage as a unified platform. Here are the key takeaways—driven by the questions attendees asked and the answers from Everpure experts Don Poorman, Zane Allyn and Mike Nelson. “Do I need to rebuild my automation to use Everpure Fusion?” Most teams already have automation in place, whether it’s Terraform, Ansible, or years of scripts. The good news: you don’t have to start over. Everpure Fusion is API-driven, so existing workflows can stay intact. In practice, you’re simply shifting from targeting individual arrays to targeting the fleet as a whole. That often means adding a parameter, not rewriting everything. Everpure Fusion picks up where any existing automation gets bogged down, so tasks get simpler as you scale, not more complex. The takeaway: Everpure Fusion helps you scale your existing automation—it simplifies, standardizes and extends it across your data estate. “What does API-first really mean here?” At Everpure, API-first isn’t just a label. The APIs are built before the GUI, which means everything you can do in the GUI is already available programmatically. For practitioners, that translates to flexibility. Whether you’re scripting, using infrastructure-as-code, or experimenting with AI-driven workflows, you’re not waiting for features to be exposed—you already have access. It’s a subtle difference from legacy storage, where automation often lags behind the interface. “How do I approach automation without losing control?” Attendees raised a common concern: automation can feel risky. The advice was straightforward—start with outcomes, not everything at once. Automate a single workflow, apply guardrails, and expand gradually. Automation here isn’t about removing control. It’s about: Reducing repetitive work Minimizing human error Freeing up time for higher-value tasks For most admins juggling multiple systems, that shift is practical—not theoretical. “What does this look like in real workflows?” One of the most relatable examples discussed was a ServiceNow-style request flow. Instead of manually provisioning storage across multiple systems, a user submits a request describing what they need—performance, protection, and resiliency. From there, Everpure Fusion and Pure1 handle the process automatically. The result is faster, more consistent delivery with fewer manual steps. More importantly, it abstracts the complexity away from both the admin and the requester. That’s a major difference from legacy environments, where admins must manage each step across each array. “What do I actually need to install?” This answer surprised some people. Everpure Fusion isn’t a separate product. It’s built into Purity. Once you’re on the right version (Purity//FA 6.8.1 or later, Purity//FB 4.5.5 or later), getting started is simple: Create a fleet Add arrays That’s it. No additional infrastructure, no separate control plane to deploy. This lowers the barrier significantly and makes it easy to start small and build as your needs require. “How does this scale?” As expected, scale came up quickly. Instead of managing arrays individually, Everpure Fusion introduces fleet-level management. New capabilities like topology groups allow further organization within that fleet—by region, workload, or compliance requirements. This is where Everpure’s approach really diverges from legacy storage. You’re no longer limited to thinking in terms of hardware. You can organize storage in ways that reflect how your business actually operates. “What happens if something fails?” Everpure Fusion is distributed across the arrays in the fleet. There’s no single point of failure. If one system goes offline, the rest of the fleet continues operating normally. That design keeps management resilient while still enabling centralized control. Final thoughts The biggest shift highlighted in this session is simple: Stop managing arrays. Start managing outcomes. With the intelligent control plane—powered by Everpure Fusion and Pure1—Everpure enables: Policy-driven automation Fleet-scale visibility Simpler, faster operations For storage teams, that means less time on manual tasks and more time focused on how data supports the business. And based on the conversation, that’s exactly where our customers want to go. Find out more about the Everpure Intelligent Control Plane here. Check out this and all our other Ask Us Everything sessions. And, keep the conversation going by jumping into the Everpure Community.
GA: What's new in Purity//FA 6.10.5
We're excited to announce that the latest Purity//FA release is now GA! With the latest Purity//FA 6.10.5 release, customers get a tighter combination of performance, protection, and access control across their FlashArray estate. By combining ActiveCluster™ with near‑synchronous ActiveDR™, the platform now delivers a three‑site resilience capability that can help meet compliance expectations. You get zero‑RPO protection between two primary sites with ActiveCluster, replicating to a third site with ActiveDR. It’s engineered to deliver sub‑30‑second RPO and sub‑minute RTO. At the same time, support for FlashArray//ST™ R5 is here as part of the FlashArray family. It’s optimized to deliver consistent ultra low‑latency performance for OLTP, in‑memory, and real‑time workloads. Take advantage of extreme performance while maintaining consistent enterprise-grade data services. Support for NVMe/TCP with VMware 9.x and ActiveCluster means the most demanding VMware workloads don’t have to choose between performance and resilience. ActiveCluster provides zero-RPO protection, automatic failover, and active-active access for stretched datastores across NVMe/TCP. Rounding it out, Everpure Fusion now supports SAML 2.0 SSO, giving you a faster, simpler, and secure authentication experience across the Everpure Platform. Replace legacy local/LDAP logins with MFA‑backed SSO and IP‑based controls that harden your perimeter without adding operational friction while accessing FlashArray, FlashBlade, and Everpure Fusion fleets. Update to Purity//FlashArray 6.10.5 to leverage these latest innovations. Want to learn what else is new? Check out the Everpure What's New webpage today!
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Purity//FA 6.10.6 introduces NFS over TLS for FlashArray File Services: an in-transit encryption layer that wraps NFSv3 and NFSv4.1 RPC traffic in a TLS 1.3 session as defined by RFC 9289 - Towards R...
The latest Ask Us Everything session focused on a topic that’s quickly becoming central to Everpure’s strategy: the intelligent control plane. And based on the questions from the community, it...
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