Unstructured Data Quick Tips

As with many things in life, deduplication is a compromise. In order to gain increased levels of storage efficiency, additional cluster resources (CPU, memory and disk IO) are utilized to find and execute the sharing of common data blocks.

Another important performance impact consideration with dedupe is the potential for data fragmentation. After deduplication, files that previously enjoyed contiguous on-disk layout will often have chunks spread across less optimal file system regions. This can lead to slightly increased latencies when accessing these files directly from disk, rather than from cache.

To help reduce this risk, SmartDedupe will not share blocks across node pools or data tiers, and will not attempt to deduplicate files smaller than 32KB in size. On the other end of the spectrum, the largest contiguous region that will be matched is 4MB.

Because deduplication is a data efficiency product rather than performance enhancing tool, in most cases the consideration will be around cluster impact management. This is from both the client data access performance front, since, by design, multiple files will be sharing common data blocks, and also from the dedupe job execution perspective, as additional cluster resources are consumed to detect and share commonality.

The first deduplication job run will often take a substantial amount of time to run, since it must scan all files under the specified directories to generate the initial index and then create the appropriate shadow stores. However, deduplication job performance will typically improve significantly on the second and subsequent job runs (incrementals), once the initial index and the bulk of the shadow stores have already been created.

If incremental deduplication jobs do take a long time to complete, this is most likely indicative of a data set with a high rate of change. If a deduplication job is paused or interrupted, it will automatically resume the scanning process from where it left off.

As mentioned previously, deduplication is a long running process that involves multiple job phases that are run iteratively. SmartDedupe typically processes around 1TB of data per day, per node.

Deduplication can significantly increase the storage efficiency of data. However, the actual space savings will vary depending on the specific attributes of the data itself. As mentioned above, the deduplication assessment job can be run to help predict the likely space savings that deduplication would provide on a given data set.

For example, virtual machines files often contain duplicate data, much of which is rarely modified. Deduplicating similar OS type virtual machine images (VMware VMDK files, etc, that have been block-aligned) can significantly decrease the amount of storage space consumed. However, the potential for performance degradation as a result of block sharing and fragmentation should be carefully considered first.

OneFS SmartDedupe does not deduplicate across files that have different protection settings. For example, if two files share blocks, but file1 is parity protected at +2:1, and file2 has its protection set at +3, SmartDedupe will not attempt to deduplicate them. This ensures that all files and their constituent blocks are protected as configured.  Additionally, SmartDedupe won’t deduplicate files that are stored on different node pools. For example, if file1 and file2 are stored on tier 1 and tier 2 respectively, and tier1 and tier2 are both protected at 2:1, OneFS won’t deduplicate them. This helps guard against performance asynchronicity, where some of a file’s blocks could live on a different tier, or class of storage, than others.

OneFS performance resource management provides statistics for the resources used by jobs – both cluster-wide and per-node. This information is provided via the ‘isi statistics workload’ CLI command. Available in a ‘top’ format, this command displays the top jobs and processes, and periodically updates the information.

For example, the following syntax shows, and indefinitely refreshes, the top five processes on a cluster:

# isi statistics workload --limit 5 –format=top

last update:  2020-09-23T16:45:25 (s)ort: default

CPU  Reads Writes    L2   L3   Node SystemName      JobType

1.4s 9.1k 0.0        3.5k 497.0 2    Job:  237       IntegrityScan[0]

1.2s 85.7 714.7      4.9k 0.0  1    Job:  238       Dedupe[0]

1.2s 9.5k 0.0        3.5k 48.5 1    Job:  237       IntegrityScan[0]

1.2s 7.4k 541.3      4.9k 0.0  3    Job: 238        Dedupe[0]

1.1s 7.9k 0.0        3.5k 41.6 2    Job:  237       IntegrityScan[0]

From the output, we can see that two job engine jobs are in progress: Dedupe (job ID 238), which runs at low impact and priority level 4 is contending with IntegrityScan (job ID 237), which runs by default at medium impact and priority level 1.

The resource statistics tracked per job, per job phase, and per node include CPU, reads, writes, and L2 & L3 cache hits. Unlike the output from the ‘top’ command, this makes it easier to diagnose individual job resource issues, etc.

Below are some examples of typical space reclamation levels that have been achieved run SmartDedupe on various data types. Be aware though that these space savings values are provided solely as rough guidance. Since no two data sets are alike (unless they’re replicated), actual results can and will vary considerably from these examples.

Workflow / Data Type	Typical Space Savings
Virtual Machine Data	35%
Home Directories / File Shares	25%
Email Archive	20%
Engineering Source Code	15%
Media Files	10%

SmartDedupe is included as a core component of OneFS but requires a valid product license key in order to activate. An unlicensed cluster will show a SmartDedupe warning until a valid product license has been applied to the cluster.

For optimal cluster performance, observing the following SmartDedupe best practices is recommended.

• Deduplication is most effective when applied to data sets with a low rate of change – for example, archived data.
• Enable SmartDedupe to run at subdirectory level(s) below /ifs.
• Avoid adding more than ten subdirectory paths to the SmartDedupe configuration policy,
• SmartDedupe is ideal for home directories, departmental file shares and warm and cold archive data sets.
• Run SmartDedupe against a smaller sample data set first to evaluate performance impact versus space efficiency.
• Schedule deduplication to run during the cluster’s low usage hours – i.e. overnight, weekends, etc.
• After the initial dedupe job has completed, schedule incremental dedupe jobs to run every two weeks or so, depending on the size and rate of change of the dataset.
• Always run SmartDedupe with the default ‘low’ impact Job Engine policy.
• Run the dedupe assessment job on a single root directory at a time. If multiple directory paths are assessed in the same job, you will not be able to determine which directory should be deduplicated.
• When replicating deduplicated data, to avoid running out of space on target, it is important to verify that the logical data size (i.e. the amount of storage space saved plus the actual storage space consumed) does not exceed the total available space on the target cluster.
• Run a deduplication job on an appropriate data set prior to enabling a snapshots schedule.
• Where possible, perform any snapshot restores (reverts) before running a deduplication job. And run a dedupe job directly after restoring a prior snapshot version.

With dedupe, there’s always trade-off between cluster resource consumption (CPU, memory, disk), the potential for data fragmentation and the benefit of increased space efficiency. Therefore, SmartDedupe is not ideally suited for high performance workloads.

• Depending on an application’s I/O profile and the effect of deduplication on the data layout, read and write performance and overall space savings can vary considerably.
• SmartDedupe will not permit block sharing across different hardware types or node pools to reduce the risk of performance asymmetry.
• SmartDedupe will not share blocks across files with different protection policies applied.
• OneFS metadata, including the deduplication index, is not deduplicated.
• Deduplication is a long running process that involves multiple job phases that are run iteratively.
• SmartDedupe will not attempt to deduplicate files smaller than 32KB in size.
• Dedupe job performance will typically improve significantly on the second and subsequent job runs, once the initial index and the bulk of the shadow stores have already been created.
• SmartDedupe will not deduplicate the data stored in a snapshot. However, snapshots can certainly be created of deduplicated data.
• If deduplication is enabled on a cluster that already has a significant amount of data stored in snapshots, it will take time before the snapshot data is affected by deduplication. Newly created snapshots will contain deduplicated data, but older snapshots will not.
• Any file on a cluster that is ‘un-deduped’ is automatically marked to ‘not re-dupe’. In order to reapply deduplicate to an un-deduped file, specific flags on the shadow store need to be cleared. For example:How to check the setting

  # isi get -D /ifs/data/test | grep -i dedupe

  *  Do not dedupe:      0

  Undedupe the file via isi_sstore :

  # isi_sstore undedupe /ifs/data/test

  Verify the setting:

  # isi get -D /ifs/data/test | grep -i dedupe

  *  Do not dedupe:      1

  ​​​​​​​If you want that file to participate in dedupe again then you need reset the “Do not dedupe” flag.

  How to reset the path.

  isi_sstore attr –no_dedupe=false <path>

SmartDedupe is one of several components of OneFS that enable OneFS to deliver a very high level of raw disk utilization. Another major storage efficiency attribute is the way that OneFS natively manages data protection in the file system. Unlike most file systems that rely on hardware RAID, OneFS protects data at the file level and, using software-based erasure coding, allows most customers to enjoy raw disk space utilization levels in the 80% range or higher. This is in contrast to the industry mean of around 50-60% raw disk capacity utilization. SmartDedupe serves to further extend this storage efficiency headroom, bringing an even more compelling and demonstrable TCO advantage to primary file based storage.

SmartDedupe post process dedupe is compatible with OneFS in-line data reduction (which we’ll cover in another blog post series) and vice versa. In-line compression is able to compress OneFS shadow stores. However, for SmartDedupe to process compressed data, the SmartDedupe job will have to decompress it first in order to perform deduplication, which is an addition resource overhead.

As we saw in the previous article in this series, SmartDedupe operates at the directory level, targeting all files and directories underneath one or more root directories.

SmartDedupe not only deduplicates identical blocks in different files, it also matches and shares identical blocks within a single file. For two or more files to be deduplicated, the two following attributes must be the same:

• Disk pool policy ID
• Protection policy

If either of these attributes differs between two or more matching files, their common blocks will not be shared. SmartDedupe also does not deduplicate files that are less than 32 KB or smaller, because the resource consumption overhead outweighs the small storage efficiency benefit.

There are two principal elements to managing deduplication in OneFS. The first is the configuration of the SmartDedupe process itself. The second involves the scheduling and execution of the Dedupe job. These are both described below.

SmartDedupe works on data sets which are configured at the directory level, targeting all files and directories under each specified root directory. Multiple directory paths can be specified as part of the overall deduplication job configuration and scheduling.

Similarly, the dedupe directory paths can also be configured from the CLI via the isi dedupe settings modify command. For example, the following command targets /ifs/data and /ifs/home for deduplication:

# isi dedupe settings modify --paths /ifs/data, /ifs/home

Bear in mind that the permissions required to configure and modify deduplication settings are separate from those needed to run a deduplication job. For example, a user’s role must have job engine privileges to run a deduplication job. However, in order to configure and modify dedupe configuration settings, they must have the deduplication role privileges.

SmartDedupe can be run either on-demand (started manually) or via a predefined schedule. This is configured via the cluster management ‘Job Operations’ section of the WebUI.

The recommendation is to schedule and run deduplication during off-hours, when the rate of data change on the cluster is low. If clients are continually writing to files, the amount of space saved by deduplication will be minimal because the deduplicated blocks are constantly being removed from the shadow store.

To modify the parameters of the dedupe job itself, run the isi job types modify command. For example, the following command configures the deduplication job to be run every Saturday at 12:00 AM:

# isi job types modify Dedupe --schedule "Every Saturday at 12:00 AM"

For most clusters, after the initial deduplication job has completed, the recommendation is to run an incremental deduplication job once every two weeks.

The amount of disk space currently saved by SmartDedupe can be determined by viewing the cluster capacity usage chart and deduplication reports summary table in the WebUI. The cluster capacity chart and deduplication reports can be found by navigating to File System Management > Deduplication > Summary.

In addition to the bar chart and accompanying statistics (above), which graphically represents the data set and space efficiency in actual capacity terms, the dedupe job report overview field also displays the SmartDedupe savings as a percentage.

SmartDedupe space efficiency metrics are also provided via the ‘isi dedupe stats’ CLI command:

# isi dedupe stats

      Cluster Physical Size: 676.8841T

          Cluster Used Size: 236.3181T

  Logical Size Deduplicated: 29.2562T

             Logical Saving: 25.5125T

Estimated Size Deduplicated: 42.5774T

  Estimated Physical Saving: 37.1290T

In OneFS 8.2.1 and later, SmartQuotas has been enhanced to report the capacity saving from deduplication, and data reduction in general, as a storage efficiency ratio. SmartQuotas reports efficiency as a ratio across the desired data set as specified in the quota path field. The efficiency ratio is for the full quota directory and its contents, including any overhead, and reflects the net efficiency of compression and deduplication. On a cluster with licensed and configured SmartQuotas, this efficiency ratio can be easily viewed from the WebUI by navigating to ‘File System > SmartQuotas > Quotas and Usage’.

Similarly, the same data can be accessed from the OneFS command line via is ‘isi quota quotas list’ CLI command. For example:

# isi quota quotas list

Type      AppliesTo  Path           Snap  Hard  Soft  Adv  Used    Efficiency

-----------------------------------------------------------------------------

directory DEFAULT    /ifs           No    -     -     -    2.3247T 1.29 : 1

-----------------------------------------------------------------------------

Total: 1

More detail, including both the physical (raw) and logical (effective) data capacities, is also available via the ‘isi quota quotas view <path> <type>’ CLI command. For example:

# isi quota quotas view /ifs directory

                        Path: /ifs

                        Type: directory

                   Snapshots: No

 Thresholds Include Overhead: No

                       Usage

                           Files: 4245818

         Physical(With Overhead): 1.80T

           Logical(W/O Overhead): 2.33T

Efficiency(Logical/Physical): 1.29 : 1

…

To configure SmartQuotas for data efficiency reporting, create a directory quota at the top-level file system directory of interest, for example /ifs. Creating and configuring a directory quota is a simple procedure and can be performed from the WebUI, as follows:

Navigate to ‘File System > SmartQuotas > Quotas and Usage’ and select ‘Create a Quota’. In the create pane, field, set the Quota type to ‘Directory quota’, add the preferred top-level path to report on, select ‘File system logical size’ for Quota Accounting, and set the Quota Limits to ‘Track storage without specifying a storage limit’. Finally, select the ‘Create Quota’ button to confirm the configuration and activate the new directory quota.

The efficiency ratio is a single, current-in time efficiency metric that is calculated per quota directory and includes the sum of SmartDedupe plus in-line data reduction. This is in contrast to a history of stats over time, as reported in the ‘isi statistics data-reduction’ CLI command output, described above. As such, the efficiency ratio for the entire quota directory will reflect what is actually there. via the platform API as of OneFS 8.2.2.

The OneFS WebUI cluster dashboard also now displays a storage efficiency tile, which shows physical and logical space utilization histograms and reports the capacity saving from in-line data reduction as a storage efficiency ratio. This dashboard view is displayed by default when opening the OneFS WebUI in a browser and can be easily accessed by navigating to ‘File System > Dashboard > Cluster Overview’.

The Job Engine parallel execution framework provides comprehensive run time and completion reporting for the deduplication job.

Once the dedupe job has started working on a directory tree, the resulting space savings it achieves can be monitored in real time. While SmartDedupe is underway, job status is available at a glance via the progress column in the active jobs table. This information includes the number of files, directories and blocks that have been scanned, skipped and sampled, and any errors that may have been encountered.

Additional progress information is provided in an Active Job Details status update, which includes an estimated completion percentage based on the number of logical inodes (LINs) that have been counted and processed.

Once the SmartDedupe job has run to completion, or has been terminated, a full dedupe job report is available. This can be accessed from the WebUI by navigating to Cluster Management > Job Operations > Job Reports, and selecting ‘View Details’ action button on the desired Dedupe job line item.

The job report contains the following relevant dedupe metrics.

Report Field	Description of Metric
Start time	When the dedupe job started.
End time	When the dedupe job finished.
Scanned blocks	Total number of blocks scanned under configured path(s).
Sampled blocks	Number of blocks that OneFS created index entries for.
Created dedupe requests	Total number of dedupe requests created. A dedupe request gets created for each matching pair of data blocks. For example, three data blocks all match, two requests are created: One request to pair file1 and file2 together, the other request to pair file2 and file3 together.
Successful dedupe requests	Number of dedupe requests that completed successfully.
Failed dedupe requests	Number of dedupe requests that failed. If a dedupe request fails, it does not mean that the also job failed. A deduplication request can fail for any number of reasons. For example, the file might have been modified since it was sampled.

 

Skipped files	Number of files that were not scanned by the deduplication job. The primary reason is that the file has already been scanned and hasn’t been modified since. Another reason for a file to be skipped is if it’s less than 32KB in size. Such files are considered too small and don’t provide enough space saving benefit to offset the fragmentation they will cause.
Index entries	Number of entries that currently exist in the index.
Index lookup attempts	Cumulative total number of lookups that have been done by prior and current deduplication jobs. A lookup is when the deduplication job attempts to match a block that has been indexed with a block that hasn’t been indexed.
Index lookup hits	Total number of lookup hits that have been done by earlier deduplication jobs plus the number of lookup hits done by this deduplication job. A hit is a match of a sampled block with a block in index.

Dedupe job reports are also available from the CLI via the ‘ isi job reports view <job_id> ’ command.

From an execution and reporting stance, the Job Engine considers the ‘dedupe’ job to comprise of a single process or phase. The Job Engine events list will report that Dedupe Phase1 has ended and succeeded. This indicates that an entire SmartDedupe job, including all four internal dedupe phases (sampling, duplicate detection, block sharing, & index update), has successfully completed. For example:

# isi job events list --job-type dedupe

Time                Message

------------------------------------------------------

2020-09-01T13:39:32 Dedupe[1955] Running

2020-09-01T13:39:32 Dedupe[1955] Phase 1: begin dedupe

2020-09-01T14:20:32 Dedupe[1955] Phase 1: end dedupe

2020-09-01T14:20:32 Dedupe[1955] Phase 1: end dedupe

2020-09-01T14:20:32 Dedupe[1955] Succeeded

For deduplication reporting across multiple OneFS clusters, SmartConnect is also integrated with Isilon’s InsightIQ cluster reporting and analysis product. A report detailing the space savings delivered by deduplication is available via InsightIQ’s File Systems Analytics module.

Received several questions from the field recently around OneFS SmartDedupe, so this seemed like a useful topic to delve into. For the first article, we’ll dig into SmartDedupe’s underlying architecture.

In essence, SmartDedupe helps to maximize the storage efficiency of a cluster by decreasing the amount of physical storage required to house any given dataset. Efficiency is achieved by scanning the on-disk data for identical blocks and then eliminating the duplicates. This approach is commonly referred to as post-process, or asynchronous, deduplication. This is in contrast to the real time, in-line dedupe that’s performed on certain nodes as part of OneFS in-line data reduction. In-line DR will be explored in a future series of blog article. That said…

On discovering duplicate blocks, SmartDedupe moves a single copy of those blocks to a special set of files known as shadow stores. During this process, duplicate blocks are removed from the actual files and replaced with pointers to the shadow stores.

With post-process deduplication, new data is first stored on the storage device and then a subsequent process analyzes the data looking for commonality. This means that initial file write or modify performance is not impacted, since no additional computation is required in the write path.

Under the covers, SmartDedupe is comprised of five principle components:

• Deduplication Control Path
• Deduplication Job
• Deduplication Engine
• Shadow Store
• Deduplication Infrastructure

The SmartDedupe job  is a highly distributed background process that orchestrates deduplication across all the nodes in the cluster. Job control encompasses file system scanning, detection and sharing of matching data blocks, in concert with the Deduplication Engine.

The SmartDedupe control path is the user interface portion, comprising the OneFS WebUI, command line interface and platform API, and is responsible for managing the configuration, scheduling and control of the deduplication job.

SmartDedupe works on data sets which are configured at the directory level, targeting all files and directories under each specified root directory. Multiple directory paths can be specified as part of the overall deduplication job configuration and scheduling. By design, the deduplication job will automatically ignore (not deduplicate) the reserved cluster configuration information located under the /ifs/.ifsvar/ directory, and also any file system snapshots.

It’s worth noting that the RBAC permissions required to configure and modify the deduplication settings are separate from those needed to actually run a deduplication job. For example, a user’s role must have job engine privileges to run a deduplication job. However, in order to configure and modify dedupe configuration settings, they must have the deduplication role privileges.

‘Fingerprinting’ is the part of the dedupe process where unique digital signatures, or fingerprints, are calculated using the SHA-1 hashing algorithm, one for each 8KB data block in the sampled set.

When SmartDedupe runs for the first time, it scans the data set and selectively samples blocks from it, creating the fingerprint index. This index contains a sorted list of the digital fingerprints, or hashes, and their associated blocks. After the index is created, the fingerprints are checked for duplicates. When a match is found, during the sharing phase, a byte-by-byte comparison of the blocks is performed to verify that they are absolutely identical and to ensure there are no hash collisions. Then, if they are determined to be identical, the block’s pointer is updated to the already existing data block and the new, duplicate data block is released.

Hash computation and comparison is only utilized during the sampling phase. For the actual block sharing phase, full data comparison is employed. SmartDedupe also operates on the premise of variable length deduplication, where the block matching window is increased to encompass larger runs of contiguous matching blocks.

As we saw in the previous  article, OneFS shadow stores are file system containers that allow data to be stored in a sharable manner. This allows files to contain both physical data and pointers, or references, to shared blocks in shadow stores.

For example, consider the shadow store information for a regular, undeduped file:

# isi get -DDD file.orig | grep –i shadow

*  Shadow refs:        0

         zero=36 shadow=0 ditto=0 prealloc=0 block=28

A second copy of this file is then created and then deduped:

# isi get -DDD file.* | grep -i shadow

*  Shadow refs:        28

         zero=36 shadow=28 ditto=0 prealloc=0 block=0

*  Shadow refs:        28

         zero=36 shadow=28 ditto=0 prealloc=0 block=0

As we can see, the block count of the original file has now become zero and the shadow block count for both the original file and it’s and copy has become ‘28′. Additionally, if another file copy is added and deduplicated, the same shadow store info and count is reported for all three files.

It’s worth noting that, even if duplicate file(s) are removed, the original file still retains the shadow store layout.

Dedupe is performed in parallel across the cluster by the OneFS Job Engine via a dedicated deduplication job, which distributes worker threads across all nodes. This distributed work allocation model allows SmartDedupe to scale linearly as an Isilon cluster grows and additional nodes are added.

The control, impact management, monitoring and reporting of the deduplication job is performed by the Job Engine in a similar manner to other storage management and maintenance jobs on the cluster.

While deduplication can run concurrently with other cluster jobs, only a single instance of the deduplication job, albeit with multiple workers, can run at any one time. Although the overall performance impact on a cluster is relatively small, the deduplication job does consume CPU and memory resources.

Architecturally, the duplication job, and supporting dedupe infrastructure, are made up of the following four phases:

Because the SmartDedupe job is typically long running, each of the phases are executed for a set time period, performing as much work as possible before yielding to the next phase. When all four phases have been run, the job returns to the first phase and continues from where it left off. Incremental dedupe job progress tracking is available via the OneFS Job Engine reporting infrastructure.

Phase 1 – Sampling

In the sampling phase, SmartDedupe performs a tree-walk of the configured data set in order to collect deduplication candidates for each file. The rational is that a large percentage of shared blocks can be detected with only a smaller sample of data blocks represented in the index table.

By default, the sampling phase selects one block from every sixteen blocks of a file as a deduplication candidate. For each candidate, a key/value pair consisting of the block’s fingerprint (SHA-1 hash) and file system location (logical inode number and byte offset) is inserted into the index. Once a file has been sampled, the file is flagged and won’t be re-scanned until it has been modified. This drastically improves the performance of subsequent deduplication jobs.

Phase 2 – Duplicate Detection

During the duplicate detection phase, the dedupe job scans the index table for fingerprints (or hashes) that match those of the candidate blocks.

If the index entries of two files match, a request entry is generated.  In order to improve deduplication efficiency, a request entry also contains pre and post limit information. This information contains the number of blocks in front of and behind the matching block which the block sharing phase should search for a larger matching data chunk, and typically aligns to a OneFS protection group’s boundaries.

Phase 3 – Block Sharing

For the block sharing phase the deduplication job calls into the shadow store library and dedupe infrastructure to perform the sharing of the blocks.

Multiple request entries are consolidated into a single sharing request, which is processed by the block sharing phase, and ultimately results in the deduplication of the common blocks. The file system searches for contiguous matching regions before and after the matching blocks in the sharing request; if any such regions are found, they will also be shared. Blocks are shared by writing the matching data to a common shadow store and creating references from the original files to this shadow store.

Phase 4 – Index Update

The index table is populated with the sampled and matching block information gathered during the previous three phases. After a file has been scanned by the dedupe job, OneFS may not find any matching blocks in other files on the cluster. Once a number of other files have been scanned, if a file continues to not share any blocks with other files on the cluster, OneFS will remove the index entries for that file. This helps prevent OneFS from wasting cluster resources searching for unlikely matches. SmartDedupe scans each file in the specified data set once, after which the file is marked, preventing subsequent dedupe jobs from rescanning the file until it has been modified.

In OneFS 9.0 by default Services are not enabled by default, this also includes NFS, SMB, S3 and HDFS.

When attempting to use HDFS against an 9.0 cluster, the Hadoop client may see the following error on all HDFS access.

[cdh6-1-user1@centos-10 ~]$ hadoop fs -ls /

ls: Call From centos-10.foo.com/10.246.156.21 to cdh6.foo.com:8020 failed on connection exception: java.net.ConnectException: Connection refused; For more details see:  http://wiki.apache.org/hadoop/ConnectionRefused

This is because the HDFS service is not enabled on HDFS and therefore connections are refused.

When looking at the cluster we see the Service is Disabled as by design.

# isi services -al | grep hdfs

Available Services:

  hdfs                 HDFS Server                              Disabled

 

But when looking at the WebUI or the CLI, this misleading as the Service appears enabled

cascade-1# isi hdfs settings view

                 Service: Yes

      Default Block Size: 128M

   Default Checksum Type: none

     Authentication Mode: simple_only

          Root Directory: /ifs/data

         WebHDFS Enabled: Yes

           Ambari Server:

         Ambari Namenode:

             ODP Version:

    Data Transfer Cipher: none

Ambari Metrics Collector:

To enable the Service and allow HDFS connectivity, enable the hdfs service directly from the CLI.

# isi services hdfs enable

# isi services -al | grep hdfs

Available Services:

  hdfs                 HDFS Server                              Enabled

Now the service is Enabled, HDFS operation can occur.

[cdh6-1-user1@centos-10 ~]$ hadoop fs -ls /

Found 11 items

-rwxrwxrwx   3 root         wheel               1 2020-02-20 11:38 /1.txt

-rwxrwxrwx   3 hbase        yarn               17 2020-02-20 11:31 /THIS_IS_ISILON_zone1-hadoop.txt

drwxr-xr-x   - hbase        hbase               0 2020-05-26 14:19 /_hbase

-rw-r--r--   3 root         wheel               0 2020-09-14 17:46 /cdh6_zone.txt

drwxr-xr-x   - hbase        hbase               0 2020-08-11 11:47 /hbase

-rw-r--r--   3 root         wheel               0 2020-09-14 17:46 /isilon_9.txt

drwxrwxrwx   - cdh6-1-user1 supergroup          0 2020-03-10 15:20 /nfs

drwxrwxr-x   - solr         solr                0 2019-12-12 13:29 /solr

drwxrwxrwt   - hdfs         supergroup          0 2019-12-12 13:29 /tmp

drwxr-xr-x   - hdfs         supergroup          0 2020-01-15 12:32 /user

-rw-r--r--   3 root         wheel               0 2020-09-14 17:47 /zone-3.txt

 

As an FYI: NFS, SMB & S3 are also Disabled by default in 9.0, but the Service checkbox/status can be managed via the WebUI Service enabled box on these services.

# isi services -al | grep smb

   smb                  SMB Service                              Disabled

Enable the Service via the WebUI:

# isi services -al | grep smb

   smb                  SMB Service                              Enabled

In the previous article, we looked at an overview of the shadow store and its three primary use cases within OneFS. Now, let’s look at shadow store mechanics, reporting, and job engine integration.

Under the hood, OneFS provides a SIN cache, which helps facilitate shadow store allocations. This provides a mechanism to create a shadow store on demand when required, and then cache that shadow store in memory on the local node so that it can be shared with subsequent allocators. The SIN cache separates stores by disk pool, protection policy and whether or not the store is a container.

When referencing data in a shadow store, blocks are identified with a SIN (shadow identification number) and LBN pair. A file with shadow store blocks will have protection group (PG) information that points to SINs. For example:

# isi get -DD /ifs/data/file.dup | head -100

POLICY  W  LEVEL PERFORMANCE COAL  ENCODING      FILE              IADDRS

default  4+2/2 concurrency on    UTF-8         file.dup     <1,6,35008000:512>, <2,3,236753920:512>, <3,5,302813184:512> 

...

PROTECTION GROUPS

       lbn 0: 4+2/2

               4000:0001:0067:0009@0#64

               0,0,0:8192#32

The ‘isi get’ CLI command will display information about a particular shadow store when using the –L flag and the SIN:

# isi get –DDL <SIN>

# isi get -DDL 4000:0001:003c:0005 | head -20

isi: Could not find a path to LIN:0x40000001003c0005/SNAP:18446744073709551615: Invalid argument

No valid path for LIN 0x40000001003c0005

POLICY  W  LEVEL PERFORMANCE COAL  ENCODING      FILE              IADDRS

+2:1  18   4+2/2 concurrency off   N/A           <unlinked>        <1,9,168098816:512>, <2,6,269270016:512>, <3,6,33850368:512> ct:  1337648672 rt: 0

*************************************************

* IFS inode: [ 1,9,168098816:512, 2,6,269270016:512, 3,6,33850368:512 ]

*************************************************

*

*  Inode Version:      6

*  Dir Version:        2

*  Inode Revision:     1

*  Inode Mirror Count: 3

*  Recovered Flag:     0

*  Recovered Groups:   0

*  Link Count:         2

*  Size:               133660672

*  Mode:               0100000

*  Flags:              0

*  Physical Blocks:    19251

*  LIN:                4000:0001:003c:0005

The protection group information for a SIN will also contain ‘reference count’ (refcount) information.

lbn 384: 4+2/2

               1,4,5054464:8192#16

               1,7,450527232:8192#16

               2,9,411435008:8192#16

               2,11,556056576:8192#16

               3,5,678928384:8192#16

               3,8,579436544:8192#16

               REF(    384): { 3, 3, 3, 3, 3, 3, 3, 3 }

               REF(    392): { 3, 3, 3, 3, 3, 3, 3, 3 }

               REF(    400): { 3, 3, 3, 3, 3, 3, 3, 3 }

               REF(    408): { 3, 3, 3, 3, 3, 3, 3, 3 }

               REF(    416): { 3, 3, 3, 3, 3, 3, 3, 3 }

               REF(    424): { 3, 3, 3, 3, 3, 3, 3, 3 }

               REF(    432): { 3, 3, 3, 3, 3, 3, 3, 3 }

               REF(    440): { 3, 3, 3, 3, 3, 3, 3, 3 }

The isi_sstore stats command can be used to display aggregate container statistics, alongside those of regular, or block, shadow stores. The output also includes storage efficiency stats. For example:

# isi_sstore stats

Block SIN stats:

33 GB user data takes 6 MB in shadow stores, using 11 MB physical space.

10792K physical average per shadow store.


5708.92 refs per block.

Reference efficiency 99.9825%.

Storage efficiency 57.0892%


Container SIN stats:

0 B user data takes 0 B in shadow stores, using 0 B physical space.


Raw counts={ type 0 num_ss=1 lsize=6209536 pblk=1349 refs=4328123 }{ type 1 num_ss=0 lsize=0 pblk=0 refs=0 }

Running the ‘isi_sstore list’ command in its verbose (-v) form also displays the type of SIN, the ‘fragmentation score’ (frag score) metric and whether a container is ‘underfull’, amongst other things:

# isi_sstore list –v | head –n 2

SIN                 lsize   psize    refs    filesize date         sin type underfull frag score

4000:0001:0002:0000 6209536 11003392 4328123 2121080K Jan 29 21:09 block    no 0.01

When it comes to the job engine, there are several jobs that interact with and cater to shadow stores – in addition to the dedupe job and SmartPools for small file packing. These include:

The Flexprotect job has two phases which are of particular relevance to shadow stores.

1. The ‘LIN reverify’ phase: Metatree transfers are allowed, even if a file is under repair. Since metatree transfer goes in opposite direction from linscan, the LIN table needs to be re-verified to ensure a file is not missed during the first LIN verify. Note that both LIN verify and reverify will scan only the LIN potion of the LIN table.
2. The ‘SIN verify’ phase: Once it’s determined that all the LINs are good, the SINs are inspected to ensure they are all correct. This is necessary since a cloning operation during Flexprotect, for example, might have moved an un-repaired block to a shadow store. This phase scans only the SIN portion of the table.

In general, the collect job isn’t required for (logical) blocks stored in shadow stores isn’t, since the freeing system is resilient to failure. The one exception is that references from files intentionally leaked by removing a LIN table entry to a file will not be freed, so collect will deal with these.

The ShadowStoreDelete job examines each shadow store for allocated blocks that have no external references (other than the shadow store’s reference) and frees the blocks. If all blocks in a shadow store have been freed then the shadow store is removed. A good practice is to run the ShadowStoreDelete job prior to running IntegrityScan on clusters with file clones and/or running SmartDedupe or small file storage efficiency jobs.

The ShadowStoreProtect job updates the protection level of shadow stores which are referenced by a LIN with a higher requested protection. Shadow stores that require a protection level change are added to a persistent queue (PQ) and consumed by this job.

There is also a SinReport job engine job which can be run to find LINs with SINs within the file system.

All the jobs which can change the protection contain an additional phase for SINs. For every LIN pointing to a particular SIN, if the LINs new protection policy is higher than that of the shadow store, it will update the SIN’s protection policy. In the SIN phase, the highest recorded policy will be used to protect the shadow store. In the case of disk pools, shadow stores may inherit the effective protection from the disk pool but not the disk pool itself.

As we have seen, to a large degree shadow stores store data like regular files. However, blocks from regular files are moved or copied to shadow stores and the original blocks in the source file are replaced with references to the blocks in the shadow store. If any of the logical blocks in the source file are written to, a copy on write (COW) event is triggered, which causes a local allocation of a block for the source file to replace the shadow reference. There may be multiple files with references to the same logical block in a shadow store. When all external references to a block in a shadow store have been released the block in the shadow store is now unused and will never be referenced again. The background garbage collection job, ShadowStoreDelete, periodically scans all the shadow stores and frees these unreferenced blocks. Once all the blocks in a shadow store are released, the shadow store itself can then be removed.

Be aware that files which reference shadow stores may also behave differently from regular files in that reading shadow-store references can be slower than reading data directly. Specifically, reading non-cached shadow-store references is slower than reading non-cached data. Reading cached shadow-store references takes no more time than reading cached data.

When files that reference shadow stores are replicated to another Isilon cluster or backed up via NDMP, the shadow stores are not transferred to the target Isilon cluster or backup device. The files are transferred as if they contained the data that they reference from shadow stores. On the target Isilon cluster or backup device, the files consume the same amount of space as if they had not referenced shadow stores.

When OneFS creates a shadow store, OneFS assigns the shadow store to a storage pool of a file that references the shadow store. If you delete the storage pool that a shadow store resides on, the shadow store is moved to a pool occupied by another file that references the shadow store.

OneFS does not delete a shadow-store block immediately after the last reference to the block is deleted. Instead, OneFS waits until the ShadowStoreDelete job is run to delete the unreferenced block. If a large number of unreferenced blocks exist on the cluster, OneFS might report a negative deduplication savings until the ShadowStoreDelete job is run.

Shadow stores are protected at least as much as the most protected file that references it. For example, if one file that references a shadow store resides in a storage pool with +2 protection and another file that references the shadow store resides in a storage pool with +3 protection, the shadow store is protected at +3.

Quotas account for files that reference shadow stores as if the files contained the data referenced from shadow stores; from the perspective of a quota, shadow-store references do not exist. However, if a quota includes data protection overhead, the quota does not account for the data protection overhead of shadow stores.

Within OneFS, the shadow store is a class of system file that contains blocks which can be referenced by different file – thereby providing a mechanism that allows multiple files to share common data. Shadow stores were first introduced in OneFS 7.0, initially supporting Isilon file clones, and indeed there are many overlaps between cloning and deduplicating files. As we will see, a variant of shadow store is also used as a container for file packing in OneFS SFSE (Small File Storage Efficiency), often used in archive workflows such as healthcare’s PACS.

Architecturally, each shadow store can contain up to 256 blocks, with each block able to be referenced by 32,000 files. If this 32KB reference limit is exceeded, a new shadow store is created. Additionally, shadow stores do not reference other shadow stores. All blocks within a shadow store must be either sparse or point at an actual data block. And snapshots of shadow stores are not allowed, since shadow stores have no hard links.

Shadow stores contain the physical addresses and protection for data blocks, just like normal file data. However, a fundamental difference between a shadow stores and a regular file is that the former doesn’t contain all the metadata typically associated with traditional file inodes. In particular, time-based attributes (creation time, modification time, etc) are explicitly not maintained.

Consider the shadow store information for a regular, undeduped file (file.orig):

# isi get -DDD file.orig | grep –i shadow

*  Shadow refs:        0

         zero=36 shadow=0 ditto=0 prealloc=0 block=28

A second copy of this file (file.dup) is then created and then deduplicated:

# isi get -DDD file.* | grep -i shadow

*  Shadow refs:        28

         zero=36 shadow=28 ditto=0 prealloc=0 block=0

*  Shadow refs:        28

         zero=36 shadow=28 ditto=0 prealloc=0 block=0

As we can see, the block count of the original file has now become zero and the shadow count for both the original file and its copy is incremented to ‘28′. Additionally, if another file copy is added and deduplicated, the same shadow store info and count is reported for all three files. It’s worth noting that even if the duplicate file(s) are removed, the original file will still retain the shadow store layout.

Each shadow store has a unique identifier called a shadow inode number (SIN). But, before we get into more detail, here’s a table of useful terms and their descriptions:

Element	Description
Inode	Data structure that keeps track of all data and metadata (attributes, metatree blocks, etc.) for files and directories in OneFS
LIN	Logical Inode Number uniquely identifies each regular file in the filesystem.
LBN	Logical Block Number  identifies the block offset for each block in a file
IFM Tree or Metatree	Encapsulates the on-disk and in-memory format of the inode. File data blocks are indexed by LBN in the IFM B-tree, or file metatree. This B-tree stores protection group (PG) records keyed by the first LBN. To retrieve the record for a particular LBN, the first key before the requested LBN is read. The retried record may or may not contain actual data block pointers.
IDI	Isi Data Integrity checksum. IDI checkcodes help avoid data integrity issues which can occur when hardware provides the wrong data, for example. Hence IDI is focused on the path to and from the drive and checkcodes are implemented per OneFS block.
Protection Group (PG)	A protection group encompasses the data and redundancy associated with a particular region of file data. The file data space is broken up into sections of 16 x 8KB blocks called stripe units. These correspond to the N in N+M notation; there are N+M stripe units in a protection group.
Protection Group Record	Record containing block addresses for a data stripe .There are five types of PG records: sparse, ditto, classic, shadow, and mixed. The IFM B-tree uses the B-tree flag bits, the record size, and an inline field to identify the five types of records.
BSIN	Base Shadow Store, containing cloned or deduped data
CSIN	Container Shadow Store, containing packed data (container or files).
SIN	Shadow Inode Number is a LIN for a Shadow Store, containing blocks that are referenced by different files; refers to a Shadow Store
Shadow Extent	Shadow extents contain a Shadow Inode Number (SIN), an offset, and a count. Shadow extents are not included in the FEC calculation since protection is provided by the shadow store.

Blocks in a shadow store are identified with a SIN and LBN (logical block number).

# isi get -DD /ifs/data/file.dup | fgrep –A 4 –i “protection group”

PROTECTION GROUPS

       lbn 0: 4+2/2

               4000:0001:0067:0009@0#64

               0,0,0:8192#32

A SIN is essentially a LIN that is dedicated to a shadow store file, and SINs are allocated from a subset of the LIN range. Just as every standard file is uniquely identified by a LIN, every shadow store is uniquely identified by a SIN. It is easy to tell if you are dealing with a shadow store because the SIN will begin with 4000. For example, in the output above:

4000:0001:0067:0009

Correspondingly, in the protection group (PG) they are represented as:

• SIN
• Block size
• LBN
• Run

The referencing protection group will not contain valid IDI data (this is with the file itself). FEC parity, if required, will be computed assuming a zero block.

When a file references data in a shadow store, it contains meta-tree records that point to the shadow store. This meta-tree record contains a shadow reference, which comprises a SIN and LBN pair that uniquely identifies a block in a shadow store.

A set of extension blocks within the shadow store holds the reference count for each shadow store data block. The reference count for a block is adjusted each time a reference is created or deleted from any other file to that block. If a shadow store block’s reference count drop to zero, it is marked as deleted, and the ShadowStoreDelete job, which runs periodically, deallocates the block.

Be aware that shadow stores are not directly exposed in the filesystem namespace. However, shadow stores and relevant statistics can be viewed using the ‘isi dedupe stats’, ‘isi_sstore list’ and ‘isi_sstore stats’ command line utilities.

Cloning

In OneFS, files can easily be cloned using the ‘cp –c’ command line utility. Shadow store(s) are created during the file cloning process, where the ownership of the data blocks is transferred from the source to the shadow store.

In some instances, data may be copied directly from the source to the newly created shadow stores. Cloning uses logical references to shadow stores, and the source and the destination data blocks refer to an offset in a shadow store. The source file’s protection group(s) are moved to a shadow store, and the PG is now referenced by both the source file and destination clone file. After cloning a file, both the source and the destination data blocks refer to an offset in a shadow store.

Dedupe

Shadow Stores are also used for both OneFS in-line deduplication and post-process SmartDedupe. The principle difference with dedupe, as compared to cloning, is the process by which duplicate blocks are detected.

Since in-line dedupe and SmartDedupe use different hashing algorithms, the indexes for each are not shared directly. However, the work performed by each dedupe solution can be leveraged by each other.  For instance, if SmartDedupe writes data to a shadow store, when those blocks are read, the read hashing component of inline dedupe will see those blocks and index them.

SmartDedupe post process dedupe is compatible with in-line data reduction and vice versa. In-line compression is able to compress OneFS shadow stores. However, for SmartDedupe to process compressed data, the SmartDedupe job will have to decompress it first in order to perform deduplication, which is an addition resource overhead.

Currently neither SmartDedupe nor in-line dedupe are immediately aware of the duplicate matches that each other finds.  Both in-line dedupe and SmartDedupe could dedupe blocks containing the same data to different shadow store locations, but OneFS is unable to consolidate the shadow blocks together.  When blocks are read from a shadow store into L1 cache, they are hashed and added into the in-memory index where they can be used by in-line dedupe.

Unlike SmartDedupe, in-line dedupe can deduplicate a run of consecutive blocks to a single block in a shadow store. In contrast, the SmartDedupe job also has to spend more effort to ensure that contiguous file blocks are generally stored in adjacent blocks in the shadow store. If not, both read and degraded read performance may be impacted.

Small File Storage Efficiency

A class of specialized shadow stores are also used as containers for storage efficiency, allowing packing of small file into larger structures that can be FEC protected.

These shadow stores differ from regular shadow stores in that they are deployed as single-reference stores. Additionally, container shadow stores are also optimized to isolate fragmentation, support tiering, and live in a separate subset of ID space from regular shadow stores. (4080:xxxx:xxxx:xxxx).

There are a several notable problems with many common drive retirement practices. Although not all of them are related to information security, many still result in excess cost. For example, companies that decide to re-purpose their hardware may choose to overwrite the data rather than erase it completely. The process itself is both time consuming, and a potential data security risk. For example, since re-allocated sectors on the drives are not covered by the overwrite process, this means that some old information will remain on disk.

Another option is to degauss and physically shred drives when the storage hardware is retired. Degaussing can yield mixed results, since different drives require unique optimal degauss strengths. This also often leads to readable data being left on the drive.

Thirdly, there is the option to hire professional disposal services to destroy the drive. However, the more people handling the data, the higher the data vulnerability. Total costs can also increase dramatically because of the need to publish internal reports and any auditing fees.

To address these issues, OneFS includes Instant Secure Erase (ISE) functionality. ISE enables the cryptographic erasure of non-SEDs drives in an Isilon cluster, providing customers with the ability to erase the contents of a drive after smartfail.

But first, some key terminology:

Term	Definition
Cryptographic Erase	‘SANITIZE’ command sets for SCSI/ATA drive is defined by the T10/T13 technical committees, respectively.
Instant Secure Erase	The industry term referring to the drive’s ‘cryptographic erase’ capability.
isi_drive_d	The OneFS drive daemon that manages the various drive states/activities, mapping devices to physical drive slots, and supporting firmware updates.

 

So OneFS ISE uses the ‘cryptographic erase’ command to erase proprietary user data on supported drives. ISE is enabled by default and automatically performed upon OneFS Smart-failing a supported drive.

ISE can also be run manually against a specific drive. To do this, it sends standard commands to the drive, depending on its interface type. For example:

• SCSI: “SANITIZE (cryptographic)”
• ATA: “CRYPTO SCRAMBLE EXT”

If the drive firmware supports the appropriate above command, it swaps out the Data Encryption key to render data on the storage media unreadable.

In order to use ISE, the following requirements must be met:

• The cluster is running OneFS 8.2.1 or later.
• The node is not a SED-configuration (for automatic ISE action upon smartfail).
• User has privileges to run related CLI commands (for manually performed ISE).
  • For example, the privilege to run ‘isi_radish’.
• Cluster contains currently supported drives:
  • SCSI / ATA interface.
  • Supports “cryptographic erase” command.
• The target drive is present.

ISE can be invoked by the following methods:

1. Via the isi_drive_d daemon during a drive Smartfail.
   • If the node is non-SED configuration.
   • Configurable through ‘drive config’.
2. Manually, by running the ‘isi_radish’ command.
3. Programmatically, by executing the python ‘isi.hw.bay’ module.

As mentioned previously, ISE is enabled by default. If this is not desired, it can be easily disabled from the OneFS CLI with the following syntax:

# isi devices drive config modify --instant-secure-erase no

The following CLI command can also be used to manually run ISE:

# isi_radish -S <bay/dev>

ISE provides fairly comprehensive logging, and the results differ slightly depending on whether it is run manually or automatically during a smartfail. Additionally, the ‘isi device drive list’ CLI command output will display the drive state. For example:

State	Context
SMARTFAIL	During ISE action
REPLACE	After ISE finish

Note that an ISE failure or error will not block the normal smartfail process.

For a manual ISE run against a specific drive, the results are both displayed on the OneFS CLI console and written to /var/log/messages.

The ISE logfile warning messages include:

Action	Log Entry
Running ISE	“Attempting to erase smartfailed drive in bay N …”, “Drive in bay N is securely erased” (isi_drive_history.log) “is securely erased: bay:N unit:N dev:daN Lnum:N seq:N model:X …”
ISE not supported	“Drive in bay N is not securely erased, because it doesn’t support crypto sanitize.”
ISE disabled in drive config	“Smartfailed drive in bay N is not securely erased. instant-secure-erase disabled in drive_d config.”
ISE error	“Drive in bay N is not securely erased, attempt failed.” “Drive in bay N is not securely erased, can’t determine if it supports crypto sanitize.” (isi_drive_history.log) “failed to be securely erased: bay:N unit:N dev:daN Lnum:N seq:N model:X …”

When troubleshooting ISE, a good first move is using the CLI ‘grep’ utility to search for the keyword ‘erase’ in log files.

Symptom	Detail
ISE was successful but took too long to run	•       It depends on drive model, but usually < 1minute •       It may block other process from accessing the drive.
ISE reports error	•       Usually it’s due to CAM got error sending sanitize commands •       Looking at console & /var/log/messages & dmesg for errors during ISE activity timeframe –         Did CAM report error? –         Did the device driver / expander report error? –         Did the drive/device drop during sanitize activity?

 

As clusters increase in scale and the number of competing workloads place demands on system resources, more visibility is required in order to share cluster resources equitably. OneFS partitioned performance monitoring helps define, monitor and react to performance-related issues on the cluster. This allows storage admins to pinpoint resource consumers, helping to identify rogue workloads, noisy neighbor processes, or users that consume excessive system resources.

Partitioned performance monitoring can be used to define workloads and view the associated performance statistics – protocols, disk ops, read/write bandwidth, CPU, IOPs, etc. Workload definitions can be quickly and simply configured to include any combination of directories, exports, shares, paths, users, clients and access zones. Customized settings and filters can be crafted to match specific workloads for a dataset that meets the required criteria, and reported statistics are refreshed every 30 seconds. Workload monitoring is also key for show-back and charge-back resource accounting.

Category	Description	Example
Workload	A set of identification metrics and resource consumption metrics.	{username:nick, zone_name:System} consumed {cpu:1.2s, bytes_in:10K, bytes_out:20M, …}
Dataset	A specification of identification metrics to aggregate workloads by, and the workloads collected that match that specification.	{username, zone_name}
Filter	A method for including only workloads that match specific identification metrics.	{zone_name:System}

Each resource listed below is tracked by certain stages of partitioned performance monitoring to provide statistics within a performance dataset, and for limiting specific workloads.

Resource Name	Definition	First Introduced
CPU Time	Measures CPU utilization. There are two different measures of this at the moment; raw measurements are taken in CPU cycles, but they are normalized to microseconds before aggregation.	OneFS 8.0.1
Reads	A count of blocks read from disk (including SSD). It generally counts 8 KB file blocks, though 512-byte inodes also count as a full block. These are physical blocks, not logical blocks, which doesn’t matter much for reads, but is important when analyzing writes.	OneFS 8.0.1
Writes	A count of blocks written to disk; or more precisely, to the journal. As with reads, 512-byte inode writes are counted as full blocks; for files, 8 KB blocks. Since these are physical blocks, writing to a protected file will count both the logical file data and the protection data.	OneFS 8.0.1
L2 Hits	A count of blocks found in a node’s L2 (Backend RAM) cache on a read attempt, avoiding a read from disk.	OneFS 8.0.1
L3 Hits	A count of blocks found in a node’s L3 (Backend SSD) cache on a read attempt, replacing a read from disk with a read from SSD.	OneFS 8.0.1
Protocol Operations	·         Protocol (smb1,smb2,nfs3, nfs4, s3) ·         NFS in OneFS 8.2.2 and later ·         SMB in OneFS 8.2 and later ·         S3 in OneFS 9.0 ·         For SMB 1, this is the number of ops (commands) on the wire with the exception of the NEGOTIATE op. ·         For SMB 2/3 this is the number of chained ops (commands) on the wire, with the exception of the NEGOTIATE op. ·         The counted op for chained ops will always be the first op. ·         SMB NEGOTIATE ops will not be associated with a specific user.	OneFS 8.2.2
Bytes In	A count of the amount of data received by the server from a client, including the application layer headers but not including TCP/IP headers.	OneFS 8.2
Bytes Out	A count of the amount of data sent by the server to a client, including the application layer headers but not including TCP/IP headers.	OneFS 8.2
Read/Write/Other Latency Total	Sum of times taken from start to finish of ops as they run through the system identical to that provided by isi statistics protocol. Specifically, this is the time in between LwSchedWorkCreate and the final LwSchedWorkExecuteStop for the work item. Latencies are split between the three operations types, read/write/other, with a separate resource for each. Use Read/Write/Other Latency Count to calculate averages	OneFS 8.2
Read/Write/Other Latency Count	Count of times taken from start to finish of ops as they run through the system identical to that provided by isi statistics protocol. Latencies are split between the three operations types, read/write/other, with a separate resource for each. Used to calculate the average of Read/Write/Other Latency Total	OneFS 8.2
Workload Type	·         Dynamic (or blank) – Top-N tracked workload ·         Pinned – Pinned workload ·         Overaccounted – The sum of all stats that have been counted twice within the same dataset, used so that a workload usage % can be calculated. ·         Excluded – The sum of all stats that do not match the current dataset configuration. This is for workloads that do not have an element specified that is defined in the category, or for workloads in filtered datasets that do not match the filter conditions. ·         Additional – The amount of resources consumed by identifiable workloads not matching any of the above. Principally any workload that has dropped off of the top-n. ·         System – The amount of resources consumed by the kernel. ·         Unknown – The amount of resources that we could not attribute to any workload, principally due to falling off of kernel hashes of limited size.	OneFS 8.2

Identification Metrics are the client attributes of a workload interacting with OneFS through Protocol Operations, or System Jobs or Services. They are used to separate each workload into administrator-defined datasets.

Metric Name	Definition	First Introduced
System Name	The system name of a given workload. For services started by isi_mcp/lwsm/isi_daemon this is the service name itself. For protocols this is inherited from the service name. For jobs this is the job id in the form “Job: 123”.	OneFS 8.0.1
Job Type + Phase	A short containing the job type as the first n bytes, and the phase as the rest of the bytes. There are translations for job type to name, but not job phase to name.	OneFS 8.0.1
Username	The user as reported by the native token. Translated back to username if possible by IIQ / stat summary view.	OneFS 8.2
Local IP	IP Address, CIDR Subnet or IP Address range of the node serving that workload. CIDR subnet or range will only be output if a pinned workload is configured with that range. There is no overlap between addresses/subnets/ranges for workloads with all other metrics matching.	OneFS 8.2
Remote IP	IP Address, CIDR Subnet or IP Address range of the client causing this workload. CIDR subnet or range will only be output if a pinned workload is configured with that range. There is no overlap between addresses/subnets/ranges for workloads with all other metrics matching.	OneFS 8.2
Protocol	Protocol enumeration index. Translated to string by stat. ·         smb1, smb2 ·         nfs3, nfs4 ·         s3	OneFS 8.2, OneFS 8.2.2, & OneFS 9.0
Zone	The zone id of the current workload. If zone id is present all username lookups etc should use that zone, otherwise it should use the default “System” zone. Translation to string performed by InsightIQ / summary view.	OneFS 8.0.1
Group	The group that the current workload belongs to. Translated to string name by InsightIQ / summary view. For any dataset with group defined as an element the primary group will be tracked as a dynamic workload (unless there is a matching pinned workload in which case that will be used instead). If there is a pinned workload/filter with a group specified, the additional groups will also be scanned and tracked. If multiple groups match then stats will be double accounted, and any double accounting will be summed in the “Overaccounted” workload within the category.	OneFS 8.2
IFS Domain	The partitioned performance IFS domain and respective path LIN that a particular file belongs to, determined using the inode. Domains are not tracked using dynamic workloads unless a filter is created with the specified domain. Domains are created/deleted automatically by configuring a pinned workload or specifying a domain in a filter. A file can belong to multiple domains in which case there will be double accounting within the category. As with groups any double accounting will be summed in the “Overaccounted” workload within the category. The path must be resolved from the LIN by InsightIQ or the Summary View.	OneFS 8.2
SMB Share Name	The name of the SMB share that the workload is accessing through, provided by the smb protocol. Also provided at the time of actor loading are the Session ID and Tree ID to improve hashing/dtoken lookup performance within the kernel.	OneFS 8.2
NFS Export ID	The ID of the NFS export that the workload is accessing through, provided by the smb protocol.	OneFS 8.2.2
Path	Track and report SMB traffic on a specified /ifs directory path. Note that NFS traffic under a monitored path is excluded	OneFS 8.2.2

So how does this work in practice? From the CLI, the following command syntax can be used to create a standard performance dataset monitor:

# isi performance dataset create –-name <name> <metrics>

For example:

# isi performance dataset create --name my_dataset username zone_name

To create a dataset that requires filters, use:

# isi performance dataset create –-name <name> <metrics> –-filters <filter-metrics>

# isi performance dataset create --name my_filtered_dataset username zone_name --filters zone_name

For example, to monitor the NFS exports in access zones:

# isi performance datasets create --name=dataset01 export_id zone_name

# isi statistics workload list --dataset=dataset01

Or to monitor by username for NFSv3 traffic only

# isi performance datasets create --name=ds02 username protocol --filters=protocol

# isi performance filters apply ds02 protocol:nfs3

# isi statistics workload list --dataset=ds02

Other performance dataset operation commands include:

# isi performance dataset list

# isi performance dataset view <name|id>

# isi performance dataset modify <name|id> --name <new_name>

# isi performance dataset delete <name|id>

A dataset will display the top 1024 workloads by default. Any remainder will be aggregated into a single additional workload.

If you want a workload to always be visible, it can be pinned using the following syntax:

# isi performance workload pin <dataset_name|id> <metric>:<value>

For example:

# isi performance workload pin my_dataset username:nick zone_name:System

Other workload operation commands include:

# isi performance workload list <dataset_name|id>

# isi performance workload view <dataset_name|id> <workload_name|id>

# isi performance workload modify <dataset_name|id> <workload_name|id> --name <new_name>

# isi performance workload unpin <dataset_name|id> <workload_name|id>

Multiple filters can also be applied to the same dataset. A workload will be included if it matches any of the filters. Any workload that doesn’t match a filter be aggregated into an excluded workload.

The following CLI command syntax can be sued to apply a filter:

# isi performance filter apply <dataset_name|id> <metric>:<value>

For example:

# isi performance filter apply my_filtered_dataset zone_name:System

Other filter options include:

# isi performance filter list <dataset_name|id>

# isi performance filter view <dataset_name|id> <filter_name|id>

# isi performance filter modify <dataset_name|id> <filter_name|id> --name <new_name>

# isi performance filter remove <dataset_name|id> <filter_name|id>

The following syntax can be used to enable path tracking. For example, to monitor traffic under /ifs/data:

# isi performance datasets create –name=dataset1 path

# isi performance workloads pin dataset1 path:/ifs/data/

Be aware that NFS traffic under a monitored path is currently not reported. For example:

The following CLI command can be used to define and view statistics for a dataset:

# isi statistics workload –-dataset <dataset_name|id>

For example:

# isi statistics workload --dataset my_dataset

    CPU  BytesIn  BytesOut   Ops  Reads  Writes   L2   L3  ReadLatency  WriteLatency  OtherLatency  UserName   ZoneName  WorkloadType

-------------------------------------------------------------------------------------------------------------------------------------

 11.0ms     2.8M     887.4   5.5    0.0   393.7  0.3  0.0      503.0us       638.8us         7.4ms       nick     System             -

  1.2ms    10.0K     20.0M  56.0   40.0     0.0  0.0  0.0        0.0us         0.0us         0.0us      mary     System        Pinned

 31.4us     15.1      11.7   0.1    0.0     0.0  0.0  0.0      349.3us         0.0us         0.0us       nick Quarantine             -

166.3ms      0.0       0.0   0.0    0.0     0.1  0.0  0.0        0.0us         0.0us         0.0us         -          -      Excluded

 31.6ms      0.0       0.0   0.0    0.0     0.0  0.0  0.0        0.0us         0.0us         0.0us         -          -        System

 70.2us      0.0       0.0   0.0    0.0     3.3  0.1  0.0        0.0us         0.0us         0.0us         -          -       Unknown

  0.0us      0.0       0.0   0.0    0.0     0.0  0.0  0.0        0.0us         0.0us         0.0us         -          -    Additional

  0.0us      0.0       0.0   0.0    0.0     0.0  0.0  0.0        0.0us         0.0us         0.0us         -          - Overaccounted

-------------------------------------------------------------------------------------------------------------------------------------

Total: 8

Includes standard statistics flags, i.e. --numeric, --sort, --totalby etc..

Other useful commands include the following:

To list all available identification metrics:

# isi performance metrics list

# isi performance metrics view <metric>

To view/modify the quantity of top workloads collected per dataset:

# isi performance settings view

# isi performance settings modify <n_top_workloads>

To assist with troubleshooting, the validation of the configuration is thorough, and errors are output directly to the CLI. Name lookup failures, for example UID to username mappings, are reported in an additional column in the statistics output. Errors in the kernel are output to /var/log/messages and protocol errors are written to the respective protocol log.

Note that statistics are updated every 30 seconds and, as such, a newly created dataset will not show up in the statistics output until the update has occurred. Similarly, an old dataset may be displayed until the next update occurs.

A dataset with a filtered metric specified but with no filters applied will not output any workloads. Paths and Non-Primary groups are only reported if they are pinned or have a filter applied. Paths and Non-Primary groups may result in work being accounted twice within the same dataset, as they can match multiple workloads. The total amount over-accounted within a dataset is aggregated into the Overaccounted workload.

As mentioned previously, the NFS, SMB, and S3 protocols are now supported in OneFS 9.0. Other primary protocol monitoring support, such as HDFS, will be added in a future release.

In addition to protocol stats, OneFS also includes job performance resource monitoring, which provides statistics for the resources used by jobs – both cluster-wide and per-node. Available in a ‘top’ format, this command displays the top jobs and processes, and periodically updates the information.

For example, the following syntax shows, and indefinitely refreshes, the top five processes on a cluster:

# isi statistics workload --limit 5 –-format=top

last update:  2020-07-11T06:45:25 (s)ort: default

CPU   Reads Writes      L2    L3    Node  SystemName        JobType

1.4s  9.1k  0.0         3.5k  497.0 2     Job:  237         IntegrityScan[0]

1.2s  85.7  714.7       4.9k  0.0   1     Job:  238         Dedupe[0]

1.2s  9.5k  0.0         3.5k  48.5  1     Job:  237         IntegrityScan[0]

1.2s  7.4k  541.3       4.9k  0.0   3     Job:  238         Dedupe[0]

1.1s  7.9k  0.0         3.5k  41.6  2     Job:  237         IntegrityScan[0]

The resource statistics tracked per job, per job phase, and per node include CPU, reads, writes, and L2 & L3 cache hits. Unlike the output from the ‘top’ command, this makes it easier to diagnose individual job resource issues, etc.