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Pure User Group Minnesota
Register Now => You are invited to a Pure Storage User Group event—the premier local gathering for our top technologists and strategists! This is an exclusive, interactive session built around peer-to-peer learning, where you can connect directly with local experts, share battle-tested insights, and walk away with actionable roadmaps for the future of your data environment. During this session, you’ll: Hear how leaders at C.H. Robinson are leveraging Pure to unlock extreme SQL performance and testing velocity, giving them a definitive edge in logistics speed. Get a strategic briefing on the 2026 Pure roadmap, focusing on Pure Fusion and how our platform is ready for the next wave of corporate AI adoption. Discover how Nutanix and Pure deliver the simplified, agile architecture necessary for mission-critical modern virtualization. Agenda: 2:00 PM: Check-In & Welcome 2:05 PM: Customer Spotlight with C.H. Robinson 2:30 PM: Road Map & Pure Fusion 3:00 PM: Break 3:15 PM: Modernizing the Datacenter: Nutanix & Pure 3:45 PM: Closing, Key Takeaways & Prizes 4:00 PM – 5:00 PM: Optional happy hour and bowling! Date & Time January 22, 2026 2:00 PM - 5:00 PM CT Location Pinstripes 3849 Gallagher Dr Edina, MN 55435Community Creative Corner: Holiday Edition 🎄
The holidays are the perfect time to get creative and as we are just about two weeks out from the big Christmas Holiday, we make this Wednesday Creative Corner all about what creative things you're pulling together for the season! Are you putting together a Christmas Light show the town is excited to see? Any new holiday recipes you'd like to show the community? Your Christmas tree or house interior decorated to the tee? We want to see it! Here's how to share: Share a photo, a link, a demo even (if you're feeling up to it!) or simply tell us about what you've been working on. Remember to comment on other community members' creations as well! Let's inspire each other and celebrate the diverse talents within our community. We can't wait to see what you've been up to!Pure Report Podcast: Nutanix and Pure Storage: Propelling Enterprise Virtualization Forward
Check out the latest edition of the Pure Report podcast where we unpack the GA announcement for the Nutanix and Pure Storage partnership. Hear from Cody_Hosterman and Nutanix VP Product Ketan Shah on the technical details of the integration and how this partnership came together.
How to Use Logstash to Send Directly to an S3 Object Store
3 MIN READ This article originally appeared on Medium.com and has been republished with permission from the author. To aggregate logs directly to an object store like FlashBlade, you can use the Logstash S3 output plugin. Logstash aggregates and periodically writes objects on S3, which are then available for later analysis. This plugin is simple to deploy and does not require additional infrastructure and complexity, such as a Kafka message queue. A common use-case is to leverage an existing Logstash system filtering out a small percentage of log lines that are sent to an Elasticsearch cluster. A second output filter to S3 would keep all log lines in raw (un-indexed) form for ad-hoc analysis and machine learning. The diagram below illustrates this architecture, which balances expensive indexing and raw data storage. Logstash Configuration An example Logstash config highlights the parts necessary to connect to FlashBlade S3 and send logs to the bucket “logstash,” which should already exist. The input section is a trivial example and should be replaced by your specific input sources (e.g., filebeats). input { file { path => [“/home/logstash/testdata.log”] sincedb_path => “/dev/null” start_position => “beginning” } } filter { <code”>} output { stdout { codec => rubydebug } s3{ access_key_id => “XXXXXXXX” secret_access_key => “YYYYYYYYYYYYYY” endpoint => “https://10.62.64.200" bucket => “logstash” additional_settings => { “force_path_style” => true } time_file => 5 codec => “plain” } } Note that the force_path_style setting is required; configuring a FlashBlade endpoint needs path style addressing instead of virtual host addressing. Path-style addressing does not require co-configuration with DNS servers and therefore is simpler in on-premises environments. For a more secure option, instead of specifying the access/secret key in the pipeline configuration file, they should also be specified as environment variables AWS_ACCESS_KEY and AWS_SECRET_ACCESS_KEY. Logstash can trade off efficiency of writing to S3 with the possibility of data loss through the two configuration options “time_file” and “size_file,” which control the frequency of flushing lines to an object. Larger flushes result in more efficient writes and object sizes, but result in a larger window of possible data loss if a node fails. The maximum amount of data loss is the smaller of “size_file” and “time_file” worth of data. Validation Test To test the flow of data through Logstash to FlashBlade S3, I use the public docker image for Logstash. Starting with the configuration file shown above, customize the fields for your specific FlashBlade environment and place them in ${PWD}/pipeline/ directory. We then volume-mount the configuration into the Logstash container at runtime. Start a Logstash server as a Docker container as follows: > docker run --rm -it -v ${PWD}/pipeline/:/usr/share/logstash/pipeline/ -v ${PWD}/logs/:/home/logstash/ docker.elastic.co/logstash/logstash:7.6.0 Note that I also volume-mounted the ${PWD}/logs/ directory, which is where Logstash will look for incoming data. In a second terminal, I generate synthetic data with the flog tool, writing into the shared “logs/” directory: > docker run -it --rm mingrammer/flog > logs/testdata.log Logstash will automatically pick up this new log data and start writing to S3. Then look at the output on S3 with s5cmd; in my example the result is three objects written (5MB, 5MB, and 17KB in size). > s5cmd ls s3://logstash/ + 2020/02/28 04:09:42 17740 ls.s3.03210fdc-c108–4e7d-8e49–72b614366eab.2020–02–28T04.04.part28.txt + 2020/02/28 04:10:21 5248159 ls.s3.5fe6d31b-8f61–428d-b822–43254d0baf57.2020–02–28T04.10.part30.txt + 2020/02/28 04:10:21 5256712 ls.s3.9a7f33e2-fba5–464f-8373–29e9823f5b3a.2020–02–28T04.09.part29.txt Making Use of Logs Data with Spark In Pyspark, the log lines can be loaded for a specific date as follows: logs = sc.textFile(“s3a://logstash/ls.s3.*.2020–02–29*.txt”) Because the ordering of the key places the uid before the date, each time a new Spark dataset is created it will require enumerating all objects. This is an unfortunate consequence of not having the key prefixes in the right order for sorting by date. Once loaded, you can perform custom parsing and analysis, use the Spark-Elasticsearch plugin to index the full set of data, or start machine learning experiments with SparkML.
Nutanix and Pure Storage are Changing Virtualization
Big news, virtualization fans! The combined Nutanix + Pure Storage solution is now available. You can read all about it in the blog and get further details on our Nutanix partner page. We’ve been talking to lots of folks about this offering, both Pure users and not, and the consensus is that people are glad to see some new stability being brought to the virtualization world, with a solution from two customer-centric organizations. To give you a sense of the value of using external storage with FlashArray, an early adopter (I’m not at liberty to name them) running a nearly 2 PB database workload will save about 50% on rack space with significant savings on power, cooling and operational costs. Please contact your Pure sales team if you want to learn more about this solution.Lyrical Memory Challenge: What Song Do You Know By Heart? 🎤
Happy Monday, Pure Storage Community! It's the beginning of the week during this December month, so why don't we kick things off with some fun to get to know you all a little better. Since last week we gave you a tidbit of music history, let's keep the music theme going and ask you this week's Monday Funday question: What is a song you know every word and lyric to? Is it a classic rock anthem, a 90s pop hit, a musical showstopper or maybe a more recent track that instantly lodged itself in your brain? We're talking about the song you can sing, rap or belt out from start to finish without missing a beat. Share your lyrical mementos in the comments below! Bonus points if you have a story behind why it's so memorable to you. Don't forget to give a 'like' to the songs that transport you back as well!
Proxmox VE
Hi all Hope you're all having a great day. We have several customers going down the Proxmox VE road. One of my colleagues was put onto https://github.com/kolesa-team/pve-purestorage-plugin as a possible solution (as using Pure behind Proxmox (using the native Proxmox release) is not a particularly Pure-like experience. Could someone from Pure comment on the plugin's validity/supportability?How to Leverage Object Storage via Fuse Filesystems
12 MIN READ This article originally appeared on Medium.com and is republished with permission from the author. Cloud-native applications must often co-exist with legacy applications. Those legacy applications are hardened and just work, so rewriting can seem hardly worth the trouble. For legacy applications to take advantage of new technology requires bridges, and fuse clients for object storage are a bridge that allow most (but not all) applications that expect to read and write files to work in the new world of object storage. I will focus on three different implementations of a fuse-based filesystem on top of object storage, s3fs, goofys, and rclone. Prior work on performance comparisons of s3fs and goofys include theoretical upper bounds and the goofys GitHub readme. General guidelines for when to use a fuse filesystem adaptor for object storage: The application expecting files requires only moderate performance and does not have complicated dependencies on POSIX semantics. You are using the filesystem adaptor for either reads or writes of the data, but not both. If your application is both reading and writing files, then it’s best to use a real filesystem for the working data and copy only the final results to an object store. You are using the adaptor because one part of your data pipeline is an application that expects files, whereas other applications expect objects. If you find yourself primarily copying data between local filesystems and remote object storage, then tools like s5cmd or rclone will provide better performance. There is also a Python library s3fs with similar functionality, but despite the names being the same, they are distinct pieces of software. The Python version indeed makes access to objects much easier than direct boto3code but is not as performant due to the nature of Python itself. Of the three choices, I personally suggest using goofys due to significantly better performance. It may have less POSIX compatibility, but if that difference matters to your use case, then a fuse client might not be the right answer. Fuse Best Practices and Limitations First, a FUSE client is a filesystem client written in userspace. This is in contrast to most standard filesystem clients, like EXT4 or NFS, which are implemented in the Linux kernel. This leads to more flexibility to implement filesystems, including ones that only roughly resemble a traditional filesystem. It also means you can more easily mount fuse filesystems without root privileges. Conceptually, these fuse clients are lightweight client-side gateways that translate between objects and files. You could also run a separate server that acts as a gateway, but that incurs the additional cost and complexity of an extra server. A fuse client is most useful when one part of a workflow requires simple reading or writing files, whereas the rest of your workflow directly accesses objects via native S3 API. In other words, a fuse client is a tactical choice for bringing a data set and associated workflow from filesystem to object storage, where the fuse client specifically bridges the gap where an application expects to read or write files. Things to avoid when using a fuse client: Do not expect ownership or permissions to work right. Control permissions with your S3 key policies instead. Do not use renames (‘mv’ command). Lots of directory listing operations. Write to files sequentially and avoid random writes or appending to existing files. Do not use symlinks or hard links. Do not expect consistency across clients; avoid sharing files through multiple clients with fuse mounts. No really large files (1TB or larger). Both s3fs and goofys publish their respective limitations. One advantage of s3fs is that it preserves file owner/group bits as object custom metadata. In short, the application using the fuse filesystem should be a simple reader or writer of files. If that does not match your use case, I would suggest careful consideration before proceeding. Installation and Mounting Instructions Basics Installing s3fs is straightforward on a variety of platforms such as ‘apt’ on Ubuntu. sudo apt install s3fs The mount operation uses two additional options to specify the endpoint as the FlashBlade® data VIP and to use path-style requests. sudo mkdir -p /mnt/fuse_s3fs && sudo chown $USER /mnt/fuse_s3fs s3fs $BUCKETNAME /mnt/fuse_s3fs -o url=https://10.62.64.200 -o use_path_request_style The FlashBlade’s data VIP is 10.62.64.200 in all the example commands. Install goofys by downloading the standalone binary from the GitHub release page: wget -N https://github.com/kahing/goofys/releases/latest/download/goofys chmod a+x goofys Then mount a bucket as a filesystem as follows: sudo mkdir -p /mnt/fuse_goofys && sudo chown $USER /mnt/fuse_goofys ./goofys --endpoint=https://10.62.64.200 $BUCKETNAME /mnt/fuse_goofys With goofys you can also mount specific prefixes, i.e., mount only a “subdirectory” and limit the visibility of data via fuse to just a certain key prefix. goofys <bucket:prefix> <mountpoint> Rclone-mount relies on the same installation and configuration as standard rclone. This means that if you’re already using rclone, then it is trivial to also mount a bucket as follows where “fb” refers to my FlashBlade’s rclone.conf s3 configuration: [fb] type = s3 env_auth = true region = us-east-1 endpoint = https://10.62.64.200 Replace the endpoint with the appropriate IP address and then mount with the following command: rclone --vfs-cache-mode writes mount fb:$BUCKETNAME /mnt/fuse_rclone & Note that I use the ampersand operator to background the mounting operation as the default is to keep rclone in the foreground. Simulating a Directory Structure with Object Keys When using a fuse client with S3, a “mkdir” operation corresponds to creating an empty object with a key that ends in a “/” character. In other words, the directory marker is explicitly created even though the “/” is not a special character in an object store. The “/” indicates a directory by convention. The other common approach leaves directories implicit in the key structure, meaning no extra empty placeholder objects. While this may complicate some tooling, it also means that the fuse client approach supports empty directories as you would expect in a filesystem. But if you are reading a file structure that was laid out using implicit directories, it will still work the same! Permissions One of the main challenges of using fuse clients is the fact that standard POSIX permissions no longer work as expected. Due to the mismatch between file and object permission models, I recommend restricting permissions by using access policies on the keys used by the fuse client. This means that regardless of how fuse clients apply or even ignore permissions bits (via “chmod”), the read/write/delete permissions are strictly enforced at the storage layer. Angle 1: Reader The following two FlashBlade Access Policies are required to configure the fuse client for read-only application usage: object-list and object-read. Note that if clients try to write files without permission, it is possible to see inconsistencies. For example, if I touch a file with read-only permission and goofys, an immediate listing (‘ls’) will see a phantom file which eventually goes away. The ‘touch’ command does fail, so many but not all programs or scripts that unexpectedly write should fail. $ touch foo touch: failed to close ‘foo’: Permission denied $ ls foo linux-5.12.13 … $ ls linux-5.12.13 Most operations fail without the “list” permission due to expectations of being able to browse directory structures, but, for example, it is still possible to read individual files with ‘cat’ without the object-list policy enabled. Alternatively, you can mount using goofys’s flag “-o r“ for read-only access, but using keys and access policies provides stronger protections than mounting in read-only mode. Restricting permission with keys avoids users simply re-mounting without “-o r” to work around an issue. And of course, without the object-read permission, the client can list directories and files but not access any of the file content. $ cat pod.yaml cat: pod.yaml: Permission denied Angle 2: Writer The second major way to use fuse clients for S3 access is for file-based applications to write data to an object store. For these applications, the required policies are object-list and object-write. With write and list permissions, I can write files and read them back locally for a short period of time due to local caching. Note that it appears to require ‘list’ permissions and also enables overwrites. Enabling Deletions Sometimes in addition to write permissions, the client also needs the ability to delete files. Enable the “pure:policy/object-delete” to allow for “rm” commands. See the following section on “undo” for more information about how to combine deletions with the ability to undo those deletions when necessary. Full Control For most flexible control of files within the mount, use the following policies: This avoids giving users more permissions than necessary, for example, the ability to create and delete buckets, etc., but they can still write, read, and delete files. Bonus: Undo an Accidental Deletion Object stores support object versioning, which provides functionality beyond traditional filesystems. Versioning keeps multiple copies of an object if a key is overwritten and inserts a DeleteMarker instead of erasing data when deletes are issued. An associated lifecycle policy ensures that deleted or overwritten data is eventually deleted. First, enable versioning on the bucket if it isn’t already. In the FlashBlade GUI’s bucket view, the “Enable versioning…” can be accessed in the upper right corner. And then in order to undelete files that have been accidentally deleted, you can simply go find the delete marker and remove it. There is no “undelete” operation at the filesystem level, so this needs to be out-of-band through a different mechanism or script. An example Python script (gist here) to undelete an object by removing its DeleteMarker: #!/usr/bin/python3 import boto3 import sys FB_DATAVIP='10.62.64.200' if len(sys.argv) != 3: print("Usage: {} bucketname key".format(sys.argv[0])) sys.exit(1) bucketname = sys.argv[1] key = sys.argv[2] s3 = boto3.resource('s3', endpoint_url='https://' + FB_DATAVIP) kwargs = {'Bucket' : bucketname, 'Prefix' : key} pageresponse = s3.meta.client.get_paginator('list_object_versions').paginate(**kwargs) for pageobject in pageresponse: if ‘DeleteMarkers’ in pageobject.keys() and pageobject[‘DeleteMarkers’][0][‘Key’] == key: print("Undeleting s3://{}/{}".format(bucketname, key)) s3.ObjectVersion(bucketname, key, pageobject['DeleteMarkers'][0]['VersionId']).delete() And then the object can be undeleted as simply as this: ./s3-undelete.py phrex temp/pod.yaml Undeleting s3://phrex/temp/pod.yaml A safe and secure undelete would restrict the usage of this script to an administrator in order to limit the use of keys with broader delete permissions. Finally, create a lifecycle rule to automatically clean up old object versions, i.e., if an object is no longer the most recent, it can be eventually deleted so that space is reclaimed. Similarly, if an object is deleted, the original will be kept for this long allowing a user to undo that deletion within the lifecycle’s time window. Object Storage Performance Testing While a fuse client for S3 is never the highest-performing data access path, it is important to understand the performance differences between the two clients, s3fs and goofys, as well as traditional shared filesystems like NFS. The goal of this section is to understand when fuse clients are useful and the performance differences between s3fs and goofys. This section presents performance testing of basic scenarios to help understand when and where the S3 fuse clients are useful. In each test, I compare the fuse clients presenting an object bucket as a “filesystem” with a true NFS shared filesystem. Test scenario: All tests run against a small nine-blade FlashBlade Client is 16 core, 96GB DRAM, Ubuntu 20.04 Ramdisk used as the source or sink for write and read tests respectively A direct S3 performance test gets 1.1GB/s writes and 1.5GB/s reads. I also compare with a high-performance NFS filesystem, backed by the same FlashBlade, to illustrate the fuse-client overhead. Tested goofys version 0.24.0, s3fs version v1.86, and rclone version 1.50.2 I use filesystem tools like “cp,” “rm,” and “cat” for these tests, but it is important to note that in most cases the filesystem operations will be built into existing legacy applications, e.g., fwrite() and fread(). I chose these tools because they achieve good throughput on native filesystems, are simple to understand, and are easily reproducible. The summary of performance results is that across read/write and metadata-intensive tests, the performance ordering is goofys, s3fs, and then rclone as the slowest. Throughput Results The first test reads and writes large files to determine basic throughput of each fuse client. I either write via “cp” or read via “cat” 24 files, each 1GB in size. Each test is repeated with files accessed serially or in parallel. As an example, writing to the fuse filesystem serially: for i in {1..24}; do cp /mnt/ramdisk/file_1G /mnt/$d/temp/file_1G_$i done The parallel version uses ‘&’ to launch each copy in the background and then ‘wait’ blocks until all background processes complete: for i in {1..24}; do cp /mnt/ramdisk/file_1G /mnt/$d/temp/file_1G_$i & done wait Two observations from the write results. First, goofys is significantly faster than the other fuse clients on serial writes, though still slightly slower than direct NFS. Second, parallelizing the filesystem operations results in improved write speeds in all cases, but goofys is still the fastest. The second test uses ‘cat’ to read files through the fuse clients, using the same set of 24 1GB files. As with the writes, the reads are tested both serially and in parallel. Performance trends are similar with goofys fastest for serial reads, but s3fs handles parallel reads slightly better. The more surprising result is that both goofys and s3fs are faster than true NFS for serial reads. This is a consequence of how the Linux kernel NFS client performs readahead less aggressively than the fuse clients. Metadata Results The next set of tests focuses on metadata-intensive workloads: small files, nested directories, listings, and recursive deletes. The test data set is the linux-5.12.13 source code, which contains roughly 1GB of data in 4,700 directories and 71k files. The average file size is 14KB. Goofys is fastest for both the untar and the removal operations, but the gap is larger when compared to a native NFS. This indicates that these workloads suffer a larger performance penalty relative to native NFS. The test to populate the source repo untars files directly into object storage using the fuse layer as intermediary. But this pushes at the edge of where a fuse client makes sense from a performance perspective. Directly untarring to an NFS mount is 6x faster. In this case, an alternative approach of untarring to local storage and then using s5cmd to upload directly to the object store is 5x faster (257 seconds) than goofys! Using local storage as a staging area is faster because the local storage has lower latencies for the serial untar operation and then s5cmd can upload files concurrently. Of course, this technique only works if the local storage has capacity for the temporary storage. The last test uses the “find” command to find files with a certain extension (“.h” in this case) and exercises metadata responsiveness exclusively. As with the other tests, goofys performs best. Comparing to AWS Next, I focus on the fastest client, goofys, and compare performance when using either the FlashBlade as backing object store or AWS S3. I compare relative performance on the four major test scenarios previously presented: writing and reading large files, and then copying and removing a source code repository with directories and mixed file sizes. To match the VM used to test against the FlashBlade, I used a single m5.4xlarge instance with Ubuntu 20.04. The test scenarios here consist of serial access patterns because this is the default in most workflows. Parallelization often involves modifications of source programs in which case it is better to simply switch to native S3 accesses. Note that due to the fuse client, none of these tests actually stress the FlashBlade or AWS throughput bounds. The achieved lower latency of S3 operations on the FlashBlade results in better performance. For simple large, i.e., 1GB, file operations, the FlashBlade’s lower latency results in 28% faster runtimes relative to AWS S3. In contrast, when writing or removing nested directories with small-to-medium file sizes, the performance advantage increases to 3x-6x faster in favor of FlashBlade. This indicates that the metadata overheads of LIST operations and small objects are much higher with AWS S3. Summary Goofys, s3fs, and rclone-mount are fuse clients that enable the use of an object store with applications that expect files. These fuse clients enable the migration of workflows to object storage even when you have legacy file-based applications. Those applications expecting files can still work with objects through the fuse client layer. Summarizing best practices for when and how to use s3 fuse clients: Best to use for only one part of your data workflow, either simple writing or reading of files. Do not rely on POSIX filesystem features like permissions, file renames, random overwrites, etc. Prefer goofys as a fuse client choice because of superior performance💡Technology Tip Thursday💡: Napster & Your All-Time Playlist 🎶
Hello everyone, the Thanksgiving holiday passed and another Thursday is here: a 💡Technology Tip Thursday💡 to be exact! This is a thread where we sprinkle tidbits of knowledge to the community on technology milestones that happened throughout the years during this month! This week we're bringing you some crucial musical tech history. Did you know that is was this month, December 7, 1999 The Recording Industry Association of America Sues Napster, the music file sharing service? The Recording Industry Association of America (RIAA) sued Napster in December 1999 for copyright infringement, alleging the peer-to-peer file-sharing service enabled the illegal distribution of copyrighted music. The lawsuit, which involved multiple record companies, argued Napster was facilitating widespread music piracy, leading to a landmark legal battle that eventually contributed to Napster's shutdown in 2001. Napster didn't exactly invent file sharing, but its peer-to-peer (P2P) created a massive, decentralized data ecosystem seemingly overnight. This legal battle eventually pushed the music industry fully into the digital age. It forever changed how we access and listen to music, leading us all to create massive digital libraries! So for our community question this week: What is your go-to song or songs and albums that you could listen to over and over? Share your memories below! Be sure to comment on your peers' replies!Join us for Nutanix IT Unplugged with Isaac Slade
Our friends at Nutanix are sponsering a fun virtual event on Dec. 5 for IT Unplugged featuring a live performance by Isaac Slade, formerly of The Fray. You'll hear from both Nutanix and Pure about our new joint solution, along with some musical fun. Go here for more details and to register: https://event.nutanix.com/itunplugged-december2025?utm_source=PureStorage
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