OneFS S3 Protocol Support

OneFS 9.0 introduces support for the AWS S3 API as a protocol, extending the PowerScale data lake to natively include object, and enabling workloads which write data via file protocols such as NFS, HDFS or SMB, and then read that data via S3, or vice versa.

Because objects are files “under the hood”, the same OneFS data services, such as Snapshots, SyncIQ, WORM, etc, are all seamlessly integrated.

Applications now have multiple access options – across both file and object – to the same underlying dataset, semantics, and services, eliminating the need for replication or migration for different access requirements, and vastly simplifying management.

This makes it possible to run hybrid and cloud-native workloads, which use S3-compatible backend storage, for example cloud backup & archive software, modern apps, analytics flows, IoT workloads, etc. – and to run these on-prem, alongside and coexisting with traditional file-based workflows.

In addition to HTTP 1.1, OneFS S3 supports HTTPS 1.2, to meet organizations’ security and compliance needs. And since S3 is integrated as a top-tier protocol, performance is anticipated to be similar to SMB.

By default, the S3 service listens on port 9020 for HTTP and 9021 for HTTPS, although both these ports are easily configurable.

Every S3 object is linked to a file, and each S3 bucket maps to a specific directory called the bucket path.  If the bucket path is not specified, a default is used. When creating a bucket, OneFS adds a dot-s3 directory under the bucket path, which is used to store temporary files for PUT objects.

The AWS S3 data model is a flat structure, without a strict hierarchy of sub-buckets or sub-folders. However, it does provide a logical hierarchy, using object key-name prefixes and delimiters, which OneFS leverages to support a rudimentary concept of folders.

OneFS S3 also incorporates multi-part upload, using HTTP’s ‘100 continue’ header, allowing OneFS to ingest large objects, or copy existing objects, in parts, thereby improving upload performance.

OneFS allows both ‘virtual hosted-style requests’, where you specify a bucket in a request using the HTTP Host header, and also ‘path-style requests’, where a bucket is specified using the first slash-delimited component of the Request-URI path.

Every interaction with S3 is either authenticated or anonymous. While authentication verifies the identity of the requester, authorization controls access to the desired data. OneFS treats unauthenticated requests as anonymous, mapping it to the user ‘nobody’.

OneFS S3 uses either AWS Signature Version 2 or Version 4 to authenticate requests, which must include a signature value that authenticates the request sender. This requires the user to have both an access ID and a secret Key, which can be obtained from the OneFS key management portal.

The secret key is used to generate the signature value, along with several request header values. After receiving the signed request, OneFS uses the access ID to retrieve a copy of the secret key internally, recomputes the signature value of the request, and compares it against the received signature. If they match, the requester is authenticated, and any header value used in the signature is verified to be tamper-free.

Bucket ACLs control whether a user has permission on an S3 bucket. When receiving a request for a bucket operation, OneFS parses the user access ID from the request header and evaluates the request according to the target bucket ACL. To access OneFS objects, the S3 request must be authorized at both the bucket and object level, using permission enforcement based on the native OneFS ACLs.

Here’s the list of the principle S3 operations that OneFS 9.0 currently supports:

Operation S3 API name Description
DELETE object DeleteObject This operation enables you to delete a single object from a bucket. Delete multiple objects from a bucket using a single request is not supported.
GET object GetObject Retrieves an object content.
GET object ACL GetObjectAcl Get the access control list (ACL) of an object.
HEAD object HeadObject The HEAD operation retrieves metadata from an object without returning the object itself. This operation is useful if you’re only interested in an object’s metadata. The operation returns a 200 OK if the object exists and you have permission to access it. Otherwise, the operation might return responses such as 404 Not Found and 403 Forbidden.
PUT object PutObject Adds an object to a bucket.
PUT object – copy CopyObject Creates a copy of an object that is already stored in OneFS.
PUT object ACL PutObjectAcl Sets the access control lists (ACL) permissions for an object that already exists in a bucket.
Initiate multipart upload CreateMultipartUpload Initiate a multipart upload and returns an upload ID. This upload ID is used to associate with all the parts in the specific multipart upload. You specify this upload ID in each of your subsequent upload part requests. You also include this upload ID in the final request to either complete or abort the multipart upload request.
Upload part UploadPart Uploads a part in a multipart upload. Each part must be at least 5MB in size, except the last part. And max size of each part is 5GB.
Upload part – Copy UploadPartCopy Upload a part by copying data from an existing object as data source. Each part must be at least 5MB in size, except the last part. And max size of each part is 5GB.
Complete multipart upload CompleteMultipartUpload Completes a multipart upload by assembling previously uploaded parts.
List multipart uploads ListMultipartUploads Lists in-progress multipart uploads. An in-progress multipart upload is a multipart upload that has been initiated using the Initiate Multipart Upload request but has not yet been completed or aborted.  
List parts ListParts List the parts that have been uploaded for a specific multipart upload.  
Abort multipart upload AbortMultipartUpload Abort a multipart upload. After a multipart upload is aborted, no additional parts can be uploaded using that upload ID. The storage consumed by any previously uploaded parts will be freed. However, if any part uploads are currently in progress, those part uploads might or might not succeed. As a result, it might be necessary to abort a given multipart upload multiple times in order to completely free all storage consumed by all parts.  

 

Essentially, this includes the basic bucket and object create, read, update, delete, or CRUD, operations, plus multipart upload. More detail on the full range of supported S3 API semantics will be provided in the beta test plan.

It’s worth noting that OneFS can accommodate individual objects up to 16TB in size, unlike AWS S3, which caps this at a maximum of 5TB per object.

Please be aware that OneFS 9.0 does not natively support versioning or Cross-Origin Resource Sharing (CORS). However, SnapshotIQ and SyncIQ can be used as a substitute for this functionality.

The OneFS S3 implementation includes a new WebUI and CLI for ease of configuration and management.  This enables:

  • The creation of buckets and configuration of OneFS specific options, such as object ACL policy
  • The ability to generate access IDs and secret keys for users through the WebUI key management portal.
  • Global settings, including S3 service control and configuration of the HTTP listening ports.
  • Configuration of Access zones, for multi-tenant support.

All the WebUI functionality and more is also available through the CLI using the new ‘isi s3’ command set:

# isi s3

Description:

    Manage S3 buckets and protocol settings.

Required Privileges:

    ISI_PRIV_S3

Usage:

    isi s3 <subcommand>

        [--timeout <integer>]

        [{--help | -h}]

Subcommands:

    buckets      Manage S3 buckets.

    keys         Manage S3 keys.

    log-level    Manage log level for S3 service.

    mykeys       Manage user's own S3 keys.

    settings     Manage S3 default bucket and global protocol settings.

 

PowerScale Platforms

In this article, we’ll take a quick peek at the new PowerScale F200 and F600 hardware platforms that were released last week. For reference, here’s where these new nodes sit in the current hardware hierarchy:

The PowerScale F200 is an entry-level all flash node that utilizes affordable SAS SSDs and a single-CPU 1U PowerEdge platform. It’s performance and capacity profile makes it ideally suited for use cases such as remote office/back office environments, factory floors, IoT, retail, smaller organizations, etc. The key advantages to the F200 are its low entry capacities and price points and the flexibility to add nodes individually, as opposed to a chassis/2 node minimum for the legacy Gen6 platforms.

The F200 contains four 3.5” drive bays populated with a choice of 960GB, 1.92TB, or 3.84TB enterprise SAS SSDs.

Inline data reduction, which incorporates compression, dedupe, and single instancing, is included as standard and requires no additional licensing.

Under the hood, the F200 node is based on the PowerEdge R640 server platform. Each node contains a  single Socket Intel CPU, and 10/25 GbE Front-End and Back-End networking,

Configurable memory options of 48GB or 96GB per node are available.

In contrast, the PowerScale F600 is a mid-level all-flash platform that utilizes NVMe SSDs and a dual-CPU 1U PowerEdge platform.  The ideal use cases for the F600 include performant workflows, such as M&E, EDA, HPC, and others, with some cost sensitivity and less demand for capacity.

The F600 contains eight 2.5” drive bays populated with a choice of 1.92TB, 3.84TB, or 7,68TB enterprise NVMe SSDs. Inline data reduction, which incorporates compression, dedupe, and single instancing, is also included as standard.

The F600 is also based on the 1U R640 PowerEdge server platform, but, unlike the F200, with dual socket Intel CPUs. Front-End networking options include 10/25 GbE or 40/100 GbE and with 100 GbE for the Back-End network.

Configurable memory options include 128GB, 192GB, or 384GB per node.

For Ethernet networking, the 10/40GbE environment uses SFP+ and QSFP+ cables and modules, whereas the 25/100GbE environment uses SFP28 and QSFP28 cables and modules. These cables are mechanically identical and the 25/100GbE NICs and switches will automatically read cable types and adjust accordingly. However, be aware that the 10/40GbE NICS and switches will not recognize SFP28 cables.

The 40GbE and 100GbE connections are actually four lanes of 10GbE and 25GbE respectively, allowing switches to ‘breakout’ a QSFP port into 4 SFP ports. While this is automatic on the Dell back-end switches, some front-end switches may need configuring

The F200 has a single NIC configuration comprising both a 10/25GbE front-end and back-end. By comparison, the F600 nodes are available in two configurations, with a 100GbE back-end and either a 25GbE or 100GbE front-end and.

Here’s what the back-end NIC/Switch Support Matrix looks like for the PowerScale F200 and F600:

Drive subsystem-wise, the PowerEdge R640 platform’s bay numbering scheme starts with 0 instead of 1. On the F200, there are four SAS SSDs, numbered from 0 to 3.

The F600 has ten total bays, of which numbers 0 and 1 on the far left are unused. The eight NVMe SSDs therefore reside in bays 2 to 9.

Support has been added to OneFS 9.0 for NVMe. alongside the legacy SCSI and ATA interfaces. Note that NVMe drives are only currently supported on the F600 nodes, and these drives use the NVMe and NVD drivers. The NVD is a block device driver that exposes an NVMe namespace like a drive and is what most OneFS operations act upon, and each NVMe drive has a /dev/nvmeX, /dev/nvmeXnsX and /dev/nvdX device entry. From a drive management standpoint, the CLI and WebUI are pretty much unchanged. While NVMe has been added as new drive type, the ’isi devices’ CLI syntax stays the same and the locations remain as ‘bays’. Similarly, the CLI drive utilities such as ‘isi_radish’ and ‘isi_drivenum’ also operate the same, where applicable

The F600 and F200 nodes’ front panel has limited functionality compared to older platform generations and will simply allow the user to join a node to a cluster and display the node name after the node has successfully joined the cluster.

Similar to legacy Gen6 platforms, a PowerScale node’s serial number can be found either by viewing /etc/isilon_serial_number or running the ‘isi_hw_status | grep SerNo’ CLI command syntax. The serial number reported by OneFS will match that of the service tag attached to the physical hardware and the /etc/isilon_system_config file will report the appropriate node type. For example:

# cat /etc/isilon_system_config

PowerScale F600

Introducing Dell EMC PowerScale…

Today we’re thrilled to launch Dell EMC PowerScale – a new unstructured data storage family centered  around OneFS 9.0.

This release represents a series of firsts for us:

  • Hardware-wise, we’ve delivered our first NVMe offering, the first nodes delivered in a compact 1RU form factor, the first of our platforms designed and built entirely on Dell Power-series hardware, and the first PowerScale branded products.

  • Software-wise, OneFS 9.0 introduces support for the AWS S3 API as a top-tier protocol – extending our data lake to natively include object, and enabling hybrid and cloud-native workloads that use S3-compatible backend storage, such cloud backup & archive software, modern apps, analytics flows, IoT workloads, etc. And to run these on-prem, alongside and coexisting with traditional file-based workflows.

  • DataIQ’s tight integration with OneFS 9.0 enables seamless data discovery, understanding, and movement, delivering intelligent insights and holistic management.

  • CloudIQ harnesses the power of machine learning and AI to proactively mitigate issues before they become problems.

Over the course of the next few blog articles, we’ll explore the new platforms, features and functionality of the new PowerScale family in more depth…

OneFS Caching Hierarchy

Caching occurs in OneFS at multiple levels, and for a variety of types of data. For this discussion we’ll concentrate on the caching of file system structures in main memory and on SSD.

OneFS’ caching infrastructure design is based on aggregating each individual node’s cache into one cluster wide, globally accessible pool of memory. This is done by using an efficient messaging system, which allows all the nodes’ memory caches to be available to each and every node in the cluster.

For remote memory access, OneFS utilizes the Sockets Direct Protocol (SDP) over an Ethernet or Infiniband (IB) backend interconnect on the cluster. SDP provides an efficient, socket-like interface between nodes which, by using a switched star topology, ensures that remote memory addresses are only ever one hop away. While not as fast as local memory, remote memory access is still very fast due to the low latency of the backend network.

OneFS uses up to three levels of read cache, plus an NVRAM-backed write cache, or write coalescer. The first two types of read cache, level 1 (L1) and level 2 (L2), are memory (RAM) based, and analogous to the cache used in CPUs. These two cache layers are present in all Isilon storage nodes.  An optional third tier of read cache, called SmartFlash, or Level 3 cache (L3), is also configurable on nodes that contain solid state drives (SSDs). L3 cache is an eviction cache that is populated by L2 cache blocks as they are aged out from memory.

The OneFS caching subsystem is coherent across the cluster. This means that if the same content exists in the private caches of multiple nodes, this cached data is consistent across all instances. For example, consider the following scenario:

  1. Node 2 and Node 4 each have a copy of data located at an address in shared cache.
  2. Node 4, in response to a write request, invalidates node 2’s copy.
  3. Node 4 then updates the value.
  4. Node 2 must re-read the data from shared cache to get the updated value.

OneFS utilizes the MESI Protocol to maintain cache coherency, implementing an “invalidate-on-write” policy to ensure that all data is consistent across the entire shared cache. The various states that in-cache data can take are:

M – Modified: The data exists only in local cache, and has been changed from the value in shared cache. Modified data is referred to as ‘dirty’.

E – Exclusive: The data exists only in local cache, but matches what is in shared cache. This data referred to as ‘clean’.

S – Shared: The data in local cache may also be in other local caches in the cluster.

I – Invalid: A lock (exclusive or shared) has been lost on the data.

L1 cache, or front-end cache, is memory that is nearest to the protocol layers (e.g. NFS, SMB, etc) used by clients, or initiators, connected to that node. The main task of L1 is to prefetch data from remote nodes. Data is pre-fetched per file, and this is optimized in order to reduce the latency associated with the nodes’ IB back-end network. Since the IB interconnect latency is relatively small, the size of L1 cache, and the typical amount of data stored per request, is less than L2 cache.

L1 is also known as remote cache because it contains data retrieved from other nodes in the cluster. It is coherent across the cluster, but is used only by the node on which it resides, and is not accessible by other nodes. Data in L1 cache on storage nodes is aggressively discarded after it is used. L1 cache uses file-based addressing, in which data is accessed via an offset into a file object. The L1 cache refers to memory on the same node as the initiator. It is only accessible to the local node, and typically the cache is not the primary copy of the data. This is analogous to the L1 cache on a CPU core, which may be invalidated as other cores write to main memory. L1 cache coherency is managed via a MESI-like protocol using distributed locks, as described above.

L2, or back-end cache, refers to local memory on the node on which a particular block of data is stored. L2 reduces the latency of a read operation by not requiring a seek directly from the disk drives. As such, the amount of data prefetched into L2 cache for use by remote nodes is much greater than that in L1 cache.

L2 is also known as local cache because it contains data retrieved from disk drives located on that node and then made available for requests from remote nodes. Data in L2 cache is evicted according to a Least Recently Used (LRU) algorithm. Data in L2 cache is addressed by the local node using an offset into a disk drive which is local to that node. Since the node knows where the data requested by the remote nodes is located on disk, this is a very fast way of retrieving data destined for remote nodes. A remote node accesses L2 cache by doing a lookup of the block address for a particular file object. As described above, there is no MESI invalidation necessary here and the cache is updated automatically during writes and kept coherent by the transaction system and NVRAM.

L3 cache is a subsystem which caches evicted L2 blocks on a node. Unlike L1 and L2, not all nodes or clusters have an L3 cache, since it requires solid state drives (SSDs) to be present and exclusively reserved and configured for caching use. L3 serves as a large, cost-effective way of extending a node’s read cache from gigabytes to terabytes. This allows clients to retain a larger working set of data in cache, before being forced to retrieve data from higher latency spinning disk. The L3 cache is populated with “interesting” L2 blocks dropped from memory by L2’s least recently used cache eviction algorithm. Unlike RAM based caches, since L3 is based on persistent flash storage, once the cache is populated, or warmed, it’s highly durable and persists across node reboots, etc. L3 uses a custom log-based filesystem with an index of cached blocks. The SSDs provide very good random read access characteristics, such that a hit in L3 cache is not that much slower than a hit in L2.

To utilize multiple SSDs for cache effectively and automatically, L3 uses a consistent hashing approach to associate an L2 block address with one L3 SSD. In the event of an L3 drive failure, a portion of the cache will obviously disappear, but the remaining cache entries on other drives will still be valid. Before a new L3 drive may be added to the hash, some cache entries must be invalidated.

OneFS also uses a dedicated inode cache in which recently requested inodes are kept. The inode cache frequently has a large impact on performance, because clients often cache data, and many network I/O activities are primarily requests for file attributes and metadata, which can be quickly returned from the cached inode.

OneFS provides tools to accurately assess the performance of the various levels of cache at a point in time. These cache statistics can be viewed from the OneFS CLI using the isi_cache_stats command. Statistics for L1, L2 and L3 cache are displayed for both data and metadata. For example:

# isi_cache_stats
Totals 

l1_data: a 224G 100% r 226G 100% p 318M 77%, l1_encoded: a 0.0B 0% r 0.0B 0% p 0.0B 0%, l1_meta: r 4.5T 99% p 152K 48%, 

l2_data: r 1.2G 95% p 115M 79%, l2_meta: r 27G 72% p 28M 3%, 

l3_data: r 0.0B 0% p 0.0B 0%, l3_meta: r 8G 99% p 0.0B 0%

For more detailed and formatted output, a verbose option of the command is available using the ‘isi_cache_stats -v’ option.

It’s worth noting that for L3 cache, the prefetch statistics will always read zero, since it’s a pure eviction cache and does not utilize data or metadata prefetch.

Due to balanced data distribution, automatic rebalancing, and distributed processing, OneFS is able to leverage additional CPUs, network ports, and memory as the system grows. This also allows the caching subsystem (and, by virtue, throughput and IOPS) to scale linearly with the cluster size.

FAQ: Ansible Module for Dell EMC Isilon

To which Ansible module for Dell EMC Isilon version does this FAQ apply?

This FAQ applies to version 1.1 of the module

 

Where can I get this Ansible module for Dell EMC Isilon?

We have a community in GitHub: https://github.com/dell/ansible-isilon

 

What is the software prerequisites?

  • Isilon OneFS 8 or higher
  • Ansible 2.7 or higher
  • Python 2.7.12 or higher
  • Red Hat Enterprise Linux 7.6

 

What are the supported features for this Ansible module for Dell EMC Isilon?

The Ansible Modules for Dell EMC Isilon includes:

  • File System Module
  • Access Zone Module
  • Users Module
  • Groups Module
  • Snapshot Module
  • Snapshot Schedule Module
  • NFS Module
  • SMB Module
  • Gather Facts Module

Each module includes View, Create, Delete and Modify operations. For the details, refer to the table below:

user

group

filesystem

Access zone

NFS export

SMB share

snapshot

Snapshot schedule

Create

y

y

y

n

y

y

y

y

Modify

y

y

y

y

y

y

y

y

Delete

y

y

y

n

y

y

y

y

View

y

y

y

y

y

y

y

y

What is the filesystem as we don’t see this concept in Isilon?

Filesystem in this Ansible module represents a directory in a given access zone with owner, ACL and even quotas specified.

 

How to install it?

I’ve listed high-level steps below. For the details, refer to the product guide at

https://github.com/dell/ansible-isilon/blob/dellemc_ansible/docs/Ansible%20for%20Dell%20EMC%20Isilon%20v1.1%20Product%20Guide.pdf

The following example is using CenoOS 8 + python 3.6 + Ansible 2.9.5 + Isilon sdk 8.1.1 + OneFS 8.2.2. The overall steps are as the followings:

  1. Install Ansible 2.9.5

# dnf -y install https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm

# dnf install ansible

  1. Check the python version for ansible by using the following command

# ansible –version

In my case it’s python 3.6.8

[root@c8 ~]# ansible –version

ansible 2.9.5

  config file = /etc/ansible/ansible.cfg

  configured module search path = [‘/root/.ansible/plugins/modules’, ‘/usr/share/ansible/plugins/modules’]

  ansible python module location = /usr/lib/python3.6/site-packages/ansible

  executable location = /usr/bin/ansible

  python version = 3.6.8 (default, Nov 21 2019, 19:31:34) [GCC 8.3.1 20190507 (Red Hat 8.3.1-4)]

  1. Install Isilon sdk 8.1.1

# pip3 install isi_sdk_8_1_1

  1. Install Isilon Ansible module: (make sure the path is aligned with the python version)

Copy utils/dellemc_ansible_utils.py to  /usr/lib/python3.6/site-packages/ansible/module_utils/

Copy all module Python files from ‘isilon/library’ folder to  /usr/lib/python3.6/site-packages/ansible/modules/storage/emc

  1. Install the playbook

Coyp dellemc_ansible/isilon/playbooks to any place you want

  1. Test the installation

Update the playbooks/ flo_test.yml. mine is as below:

– name: Collect set of facts in Isilon

  hosts: localhost

  connection: local

  vars:

    onefs_host: ‘192.168.116.88’

    verify_ssl: False

    api_user: ‘root’

    api_password: ‘a’

    access_zone: ‘System’

  tasks:

  – name: Get nodes of the Isilon cluster

    dellemc_isilon_gatherfacts:

      onefs_host: “{{onefs_host}}”

      verify_ssl: “{{verify_ssl}}”

      api_user: “{{api_user}}”

      api_password: “{{api_password}}”

      gather_subset:

        – nodes

    register: subset_result

  – debug:

      var: subset_result

run the playbook:

ansible-playbook  <path to playbooks/flo_test.yml>

If everything is good, you should see the Info for your Isilon is returned:

…………

                        “release”: “v9.0.0.BETA.0”,

                        “uptime”: 24533,

                        “version”: “Isilon OneFS v8.2.2(RELEASE): 0x900003000000001:Tue Feb 25 09:19:10 PST 2020    root@se********-build11-114:/b/mnt/obj/b/mnt/src/********md64.********md64/sys/IQ.********md64.rele********se   FreeBSD cl********ng version 5.0.0 (t********gs/RELEASE_500/fin********l 312559) (b********sed on LLVM 5.0.0svn)”

                    }

                }

            ],

            “total”: 1

        },

        “Providers”: [],

        “Users”: [],

        “changed”: false,

        “failed”: false

    }

}

 

PLAY RECAP *********************************************************************

localhost                  : ok=3    changed=0    unreachable=0    failed=0    skipped=0    rescued=0    ignored=0  

 

Does this module support quota?

The current version only support directory(file system) quotas, but not user or group quotas.

 

Where can I find the examples?

Check examples from each module’s file in /ansible-isilon/dellemc_ansible/isilon/library/

I’ve also create a short video on how to use this module to create and mount NFS export from Isilon.

 

What is the limitation of this module?

Gatherfacts

Getting the list of users and groups with very long names may fail.

Users and Groups

Only local users and groups can be created.

Operations on users and groups with very long names may fail.

Access Zone

Creation and deletion of access zones is not supported.

Filesystems

ACLs can only be modified from POSIX to POSIX mode.

Only directory quotas are supported but not user or group quotas.

Modification of include_snap_data flag is not supported.

NFS Export

If there multiple exports present with the same path in an access zone, operations on such exports fail.

Advanced Isilon features

No support for advanced Isilon features like SyncIQ, tiering, WORM and so on.

How to uninstall the module?

  1. pip3 uninstall isi_sdk_8_1_1
  2. Remove dellemc_ansible_utils.py from  /usr/lib/python3.6/site-packages/ansible/module_utils/
  3. Remove the following files from  /usr/lib/python3.6/site-packages/ansible/modules/storage/emc

dellemc_isilon_accesszone.py

dellemc_isilon_filesystem.py

dellemc_isilon_gatherfacts.py

dellemc_isilon_group.py

dellemc_isilon_nfs.py

dellemc_isilon_smb.py

dellemc_isilon_snapshot.py

dellemc_isilon_snapshotschedule.py

dellemc_isilon_user.py

  1. Remove all the play book

 

Where do submit an issue against the driver?

The Ansible module for Dell EMC Isilon is officially by Dell EMC. Therefore you can open a ticket directly to the support website : https://www.dell.com/support/ or open a discussion in the forum : https://www.dell.com/community/Containers/bd-p/Containers

 

Can I run this module in a production environment?

Yes, the module is production-grade. Please make sure your environment follows the pre-requisites and Ansible best practices.

Quick overview of Ansible module for Dell EMC Isilon

Ansible module for Dell EMC Isilon has been released 2 months before and I’ve seen many people are interested in it. Here is a quick demo of how to use it to  create and mount an NFS export. For further details, please go to our github community: https://github.com/dell/ansible-isilon

OneFS Endurant Cache

The earlier blog post on multi-threaded I/O generated several questions on synchronous writes in OneFS. So this seemed like a useful topic to explore in a bit more detail.

OneFS natively provides a caching mechanism for synchronous writes – or writes that require a stable write acknowledgement to be returned to a client. This functionality is known as the Endurant Cache, or EC.

The EC operates in conjunction with the OneFS write cache, or coalescer, to ingest, protect and aggregate small, synchronous NFS writes. The incoming write blocks are staged to NVRAM, ensuring the integrity of the write, even during the unlikely event of a node’s power loss.  Furthermore, EC also creates multiple mirrored copies of the data, further guaranteeing protection from single node and, if desired, multiple node failures.

EC improves the latency associated with synchronous writes by reducing the time to acknowledgement back to the client. This process removes the Read-Modify-Write (R-M-W) operations from the acknowledgement latency path, while also leveraging the coalescer to optimize writes to disk. EC is also tightly coupled with OneFS’ multi-threaded I/O (Multi-writer) process, to support concurrent writes from multiple client writer threads to the same file. And the design of EC ensures that the cached writes do not impact snapshot performance.

The endurant cache uses write logging to combine and protect small writes at random offsets into 8KB linear writes. To achieve this, the writes go to special mirrored files, or ‘Logstores’. The response to a stable write request can be sent once the data is committed to the logstore. Logstores can be written to by several threads from the same node, and are highly optimized to enable low-latency concurrent writes.

Note that if a write uses the EC, the coalescer must also be used. If the coalescer is disabled on a file, but EC is enabled, the coalescer will still be active with all data backed by the EC.

So what exactly does an endurant cache write sequence look like?

Say an NFS client wishes to write a file to an Isilon cluster over NFS with the O_SYNC flag set, requiring a confirmed or synchronous write acknowledgement. Here is the sequence of events that occur to facilitate a stable write.

1)  A client, connected to node 3, begins the write process sending protocol level blocks. 4K is the optimal block size for the endurant cache.

2)  The NFS client’s writes are temporarily stored in the write coalescer portion of node 3’s RAM. The Write Coalescer aggregates uncommitted blocks so that the OneFS can, ideally, write out full protection groups where possible, reducing latency over protocols that allow “unstable” writes. Writing to RAM has far less latency that writing directly to disk.

3)  Once in the write coalescer, the endurant cache log-writer process writes mirrored copies of the data blocks in parallel to the EC Log Files.

The protection level of the mirrored EC log files is the same as that of the data being written by the NFS client.

4)  When the data copies are received into the EC Log Files, a stable write exists and a write acknowledgement (ACK) is returned to the NFS client confirming the stable write has occurred. The client assumes the write is completed and can close the write session.

5)  The write coalescer then processes the file just like a non-EC write at this point. The write coalescer fills and is routinely flushed as required as an asynchronous write via to the block allocation manager (BAM) and the BAM safe write (BSW) path processes.

6)  The file is split into 128K data stripe units (DSUs), parity protection (FEC) is calculated and FEC stripe units (FSUs) are created.

7)  The layout and write plan is then determined, and the stripe units are written to their corresponding nodes’ L2 Cache and NVRAM. The EC logfiles are cleared from NVRAM at this point. OneFS uses a Fast Invalid Path process to de-allocate the EC Log Files from NVRAM.

8)  Stripe Units are then flushed to physical disk.

9)  Once written to physical disk, the data stripe Unit (DSU) and FEC stripe unit (FSU) copies created during the write are cleared from NVRAM but remain in L2 cache until flushed to make room for more recently accessed data.

As far as protection goes, the number of logfile mirrors created by EC is always one more than the on-disk protection level of the file. For example:

File Protection Level Number of EC Mirrored Copies
+1n 2
2x 3
+2n 3
+2d:1n 3
+3n 4
+3d:1n 4
+4n 5

The EC mirrors are only used if the initiator node is lost. In the unlikely event that this occurs, the participant nodes replay their EC journals and complete the writes.

If the write is an EC candidate, the data remains in the coalescer, an EC write is constructed, and the appropriate coalescer region is marked as EC. The EC write is a write into a logstore (hidden mirrored file) and the data is placed into the journal.

Assuming the journal is sufficiently empty, the write is held there (cached) and only flushed to disk when the journal is full, thereby saving additional disk activity.

An optimal workload for EC involves small-block synchronous, sequential writes – something like an audit or redo log, for example. In that case, the coalescer will accumulate a full protection group’s worth of data and be able to perform an efficient FEC write.

The happy medium is a synchronous small block type load, particularly where the I/O rate is low and the client is latency-sensitive. In this case, the latency will be reduced and, if the I/O rate is low enough, it won’t create serious pressure.

The undesirable scenario is when the cluster is already spindle-bound and the workload is such that it generates a lot of journal pressure. In this case, EC is just going to aggravate things.

So how exactly do you configure the endurant cache?

Although on by default, setting the efs.bam.ec.mode sysctl to value ‘1’ will enable the Endurant Cache:

# isi_sysctl_cluster efs.bam.ec.mode=1

EC can also be enabled & disabled per directory:

# isi set -c [on|off|endurant_all|coal_only] <directory_name>

To enable the coalescer but switch of EC, run:

# isi set -c coal_only

And to disable the endurant cache completely:

# isi_for_array –s isi_sysctl_cluster efs.bam.ec.mode=0

A return value of zero on each node from the following command will verify that EC is disabled across the cluster:

# isi_for_array –s sysctl efs.bam.ec.stats.write_blocks efs.bam.ec.stats.write_blocks: 0

If the output to this command is incrementing, EC is delivering stable writes.

Be aware that if the Endurant Cache is disabled on a cluster the sysctl efs.bam.ec.stats.write_blocks output on each node will not return to zero, since this sysctl is a counter, not a rate. These counters won’t reset until the node is rebooted.

As mentioned previously, EC applies to stable writes. Namely:

  • Writes with O_SYNC and/or O_DIRECT flags set
  • Files on synchronous NFS mounts

When it comes to analyzing any performance issues involving EC workloads, consider the following:

  • What changed with the workload?
  • If upgrading OneFS, did the prior version also have EC enable?
  • If the workload has moved to new cluster hardware:
  • Does the performance issue occur during periods of high CPU utilization?
  • Which part of the workload is creating a deluge of stable writes?
  • Was there a large change in spindle or node count?
  • Has the OneFS protection level changed?
  • Is the SSD strategy the same?

Disabling EC is typically done cluster-wide and this can adversely impact certain workflow elements. If the EC load is localized to a subset of the files being written, an alternative way to reduce the EC heat might be to disable the coalescer buffers for some particular target directories, which would be a more targeted adjustment. This can be configured via the isi set –c off command.

One of the more likely causes of performance degradation is from applications aggressively flushing over-writes and, as a result, generating a flurry of ‘commit’ operations. This can generate heavy read/modify/write (r-m-w) cycles, inflating the average disk queue depth, and resulting in significantly slower random reads. The isi statistics protocol CLI command output will indicate whether the ‘commit’ rate is high.

It’s worth noting that synchronous writes do not require using the NFS ‘sync’ mount option. Any programmer who is concerned with write persistence can simply specify an O_FSYNC or O_DIRECT flag on the open() operation to force synchronous write semantics for that fie handle. With Linux, writes using O_DIRECT will be separately accounted-for in the Linux ‘mountstats’ output. Although it’s almost exclusively associated with NFS, the EC code is actually protocol-agnostic. If writes are synchronous (write-through) and are either misaligned or smaller than 8k, they have the potential to trigger EC, regardless of the protocol.

The endurant cache can provide a significant latency benefit for small (eg. 4K), random synchronous writes – albeit at a cost of some additional work for the system.

However, it’s worth bearing the following caveats in mind:

  • EC is not intended for more general purpose I/O.
  • There is a finite amount of EC available. As load increases, EC can potentially ‘fall behind’ and end up being a bottleneck.
  • Endurant Cache does not improve read performance, since it’s strictly part of the write process.
  • EC will not increase performance of asynchronous writes – only synchronous writes.

OneFS Writes

OneFS run equally across all the nodes in a cluster such that no one node controls the cluster and all nodes are true peers. Looking from a high-level at the components within each node, the I/O stack is split into a top layer, or initiator, and a bottom layer, or participant. This division is used as a logical model for the analysis of OneFS’ read and write paths.

At a physical-level, CPUs and memory cache in the nodes are simultaneously handling initiator and participant tasks for I/O taking place throughout the cluster. There are caches and a distributed lock manager that are excluded from the diagram below for simplicity’s sake.

When a client connects to a node to write a file, it is connecting to the top half or initiator of that node. Files are broken into smaller logical chunks called stripes before being written to the bottom half or participant of a node (disk). Failure-safe buffering using a write coalescer is used to ensure that writes are efficient and read-modify-write operations are avoided. The size of each file chunk is referred to as the stripe unit size. OneFS stripes data across all nodes and protects the files, directories and associated metadata via software erasure-code or mirroring.

OneFS determines the appropriate data layout to optimize for storage efficiency and performance. When a client connects to a node, that node’s initiator acts as the ‘captain’ for the write data layout of that file. Data, erasure code (FEC) protection, metadata and inodes are all distributed on multiple nodes, and spread across multiple drives within nodes. The following illustration shows a file write occurring across all nodes in a three node cluster.

OneFS uses a cluster’s Ethernet or Infiniband back-end network to allocate and automatically stripe data across all nodes . As data is written, it’s also protected at the specified level.

When writes take place, OneFS divides data out into atomic units called protection groups. Redundancy is built into protection groups, such that if every protection group is safe, then the entire file is safe. For files protected by FEC, a protection group consists of a series of data blocks as well as a set of parity blocks for reconstruction of the data blocks in the event of drive or node failure. For mirrored files, a protection group consists of all of the mirrors of a set of blocks.

OneFS is capable of switching the type of protection group used in a file dynamically, as it is writing. This allows the cluster to continue without blocking in situations when temporary node failure prevents the desired level of parity protection from being applied. In this case, mirroring can be used temporarily to allow writes to continue. When nodes are restored to the cluster, these mirrored protection groups are automatically converted back to FEC protected.

During a write, data is broken into stripe units and these are spread across multiple nodes as a protection group. As data is being laid out across the cluster, erasure codes or mirrors, as required, are distributed within each protection group to ensure that files are protected at all times.

One of the key functions of the OneFS AutoBalance job is to reallocate and balance data and, where possible, make storage space more usable and efficient. In most cases, the stripe width of larger files can be increased to take advantage of new free space, as nodes are added, and to make the on-disk layout more efficient.

The initiator top half of the ‘captain’ node uses a modified two-phase commit (2PC) transaction to safely distribute writes across the cluster, as follows:

Every node that owns blocks in a particular write operation is involved in a two-phase commit mechanism, which relies on NVRAM for journaling all the transactions that are occurring across every node in the storage cluster. Using multiple nodes’ NVRAM in parallel allows for high-throughput writes, while maintaining data safety against all manner of failure conditions, including power failures. In the event that a node should fail mid-transaction, the transaction is restarted instantly without that node involved. When the node returns, it simply replays its journal from NVRAM.

In a write operation, the initiator also orchestrates the layout of data and metadata, the creation of erasure codes, and lock management and permissions control. OneFS can also optimize layout decisions made by to better suit the workflow. These access patterns, which can be configured at a per-file or directory level, include:

  • Concurrency: Optimizes for current load on the cluster, featuring many simultaneous clients.
  • Streaming:  Optimizes for high-speed streaming of a single file, for example to enable very fast reading with a single client.
  • Random:  Optimizes for unpredictable access to the file, by adjusting striping and disabling the use of prefetch.