AIS Loader (aisloader) is a tool to measure storage performance. It is a load generator that we constantly use to benchmark and stress-test AIStore or any S3-compatible backend.

In fact, aisloader can list, write, and read S3(**) buckets directly, which makes it quite useful, convenient, and easy to use benchmark to compare storage performance with AIStore in front of S3 and without.

(**) aisloader can be further easily extended to work directly with any Cloud storage provider including, but not limited to, AIStore-supported GCP, OCI, and Azure.

In addition, aisloader generates synthetic workloads that mimic training and inference workloads - the capability that allows to run benchmarks in isolation (which is often preferable) avoiding compute-side bottlenecks (if any) and associated complexity.

There’s a large set of command-line switches that allow to realize almost any conceivable workload, with basic permutations always including:

• number of workers
• read and write sizes
• read and write ratios

Detailed protocol-level tracing statistics are also available - see HTTP tracing section below for brief introduction.

October 2025 update: aisloader retains its StatsD integration for operational and benchmarking use cases. While AIStore itself now uses Prometheus exclusively, aisloader (for now) continues to emit its runtime metrics via StatsD — allowing DevOps to collect and visualize performance data from large aisloader fleets.

To integrate aisloader with Prometheus-based observability stacks, run the official Prometheus StatsD Exporter (which would translate aisloader’s StatsD metrics into Prometheus format).

Table of Contents

• Setup
• Command Line Options
  • Quick Reference (Alphabetical)
  • Command Line Options Grouped by Category
• Assorted Command Line
• Environment variables
• Archive Workload
• Multipart Download Stream
• Get-Batch Support
• Random Access Across Very Large Collections
• Examples
• Collecting stats
  • Grafana
• HTTP tracing
• AISLoader-Composer
• References

Setup

You can install aisloader using the following Bash script:

• https://github.com/NVIDIA/aistore/blob/main/scripts/install_from_binaries.sh

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./scripts/install_from_binaries.sh --help

Alternatively, you can also build the tool directly from the source:

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## uncomment if needed:
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## git clone https://github.com/NVIDIA/aistore.git
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## cd aistore
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##
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$ make aisloader

For usage, run: aisloader, aisloader usage, or aisloader --help.

For usage examples and extended commentary, see also:

• https://github.com/NVIDIA/aistore/blob/main/bench/tools/aisloader/test/ci-test.sh

Command Line Options

This section presents two alternative, intentionally redundant views for usability: a concise alphabetical quick reference for fast lookups, and a grouped-by-category presentation with explanations and examples for deeper understanding.

For the most recently updated command-line options and examples, please run aisloader or aisloader usage.

Quick Reference (Alphabetical)

Command Line Options Grouped by Category

Cluster Connection and API Configuration (clusterParams)

Target Bucket and Properties (bucketParams)

Timing, Intensity, and Name-Getter Configuration (workloadParams)

Object Size Constraints and Integrity (sizeCksumParams)

Archive/Shard Configuration (archParams)

Object Naming Strategy (namingParams)

Read Operation Configuration (readParams)

Multipart Upload Settings (multipartParams)

Multipart Download Stream Settings (mpdStreamParams)

ETL Configuration (etlParams)

Fleet Coordination (loaderParams)

Statistics and Monitoring (statsParams)

Cleanup, Dry-Run, HTTP Tracing, Termination Control (miscParams)

Assorted Command Line

Duration

The loads can run for a given period of time (option -duration <duration>) or until the specified amount of data is generated (option -totalputsize=<total size in KBs>).

If both options are provided the test finishes on the whatever-comes-first basis.

Example 100% write into the bucket “abc” for 2 hours:

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$ aisloader -bucket=abc -provider=ais -duration 2h -totalputsize=4000000 -pctput=100

The above will run for two hours or until it writes around 4GB data into the bucket, whatever comes first.

Write vs Read

You can choose a percentage of writing (versus reading) by setting the option -pctput=<put percentage>.

Example with a mixed PUT=30% and GET=70% load:

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$ aisloader -bucket=ais://abc -duration 5m -pctput=30 -cleanup=true

Example 100% PUT:

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$ aisloader -bucket=abc -duration 5m -pctput=100 -cleanup=true

The duration in both examples above is set to 5 minutes.

To test 100% read (-pctput=0), make sure to fill the bucket beforehand.

Read range

The loader can read the entire object (default) or a range of object bytes.

To set the offset and length to read, use option -readoff=<read offset (in bytes)> and readlen=<length to read (in bytes)>.

For convenience, both options support size suffixes: k - for KiB, m - for MiB, and g - for GiB.

Example that reads a 32MiB segment at 1KB offset from each object stored in the bucket “abc”:

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$ aisloader -bucket=ais://abc -duration 5m -cleanup=false -readoff=1024 -readlen=32m

The test (above) will run for 5 minutes and will not “cleanup” after itself (next section).

Cleanup

NOTE: -cleanup is a mandatory option defining whether to destroy bucket upon completion of the benchmark.

The option must be specified in the command line.

Example:

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$ aisloader -bucket=ais://abc -pctput=100 -totalputsize=16348 -cleanup=false
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$ aisloader -bucket=ais://abc -duration 1h -pctput=0 -cleanup=true

The first line in this example above fills the bucket “abc” with 16MiB of random data. The second - uses existing data to test read performance for 1 hour, and then removes all data.

If you just need to clean up old data prior to running a test, run the loader with 0 (zero) total put size and zero duration:

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$ aisloader -bucket=<bucket to cleanup> -duration 0s -totalputsize=0

Object size

For the PUT workload the loader generates randomly-filled objects. But what about object sizing?

By default, object sizes are randomly selected as well in the range between 1MiB and 1GiB. To set preferred (or fixed) object size(s), use the options -minsize=<minimal object size in KiB> and -maxsize=<maximum object size in KiB>

Setting bucket properties

Before starting a test, it is possible to set mirror or EC properties on a bucket (for background, please see storage services).

For background on local mirroring and erasure coding (EC), please see storage services.

To achieve that, use the option -bprops. For example:

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$ aisloader -bucket=ais://abc -pctput=0 -cleanup=false -duration 10s -bprops='{"mirror": {"copies": 2, "enabled": false}, "ec": {"enabled": false, "data_slices": 2, "parity_slices": 2}}'

The above example shows the values that are globally default. You can omit the defaults and specify only those values that you’d want to change. For instance, to enable erasure coding on the bucket “abc”:

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$ aisloader -bucket=ais://abc -duration 1h -bprops='{"ec": {"enabled": true}}' -cleanup=false

This example sets the number of data and parity slices to 2 which, in turn, requires the cluster to have at least 5 target nodes: 2 for data slices, 2 for parity slices and one for the original object.

Once erasure coding is enabled, its properties data_slices and parity_slices cannot be changed on the fly.

Note that (n data_slices, m parity_slices) erasure coding requires at least (n + m + 1) target nodes in a cluster.

Even though erasure coding and/or mirroring can be enabled/disabled and otherwise reconfigured at any point in time, specifically for the purposes of running benchmarks it is generally recommended to do it once prior to writing any data to the bucket in question.

The following sequence populates a bucket configured for both local mirroring and erasure coding, and then reads from it for 1h:

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# Fill bucket
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$ aisloader -bucket=ais://abc -cleanup=false -pctput=100 -duration 100m -bprops='{"mirror": {"enabled": true}, "ec": {"enabled": true}}'
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# Read
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$ aisloader -bucket=abc -cleanup=false -pctput=0 -duration 1h

Bytes Multiplicative Suffix

Parameters in aisLoader that represent the number of bytes can be specified with a multiplicative suffix. For example: 8M would specify 8 MiB. The following multiplicative suffixes are supported: ‘t’ or ‘T’ - TiB ‘g’ or ‘G’ - GiB, ‘m’ or ‘M’ - MiB, ‘k’ or ‘K’ - KiB. Note that this is entirely optional, and therefore an input such as 300 will be interpreted as 300 Bytes.

Environment variables

To state the same slightly differently, cluster endpoint can be defined in two ways:

• as (plain) http://ip:port address, whereby ‘—ip’ and ‘—port’ are command-line options.
• via AIS_ENDPOINT environment universally supported across all AIS clients, e.g.:

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$ export AIS_ENDPOINT=https://10.07.56.68:51080

In addition, environment can be used to specify client-side TLS (aka, HTTPS) configuration:

See also:

• HTTPS: loading, reloading, and generating certificates; switching cluster between HTTP and HTTPS

Archive Workload

AIStore supports packing many small files into shards (TAR, ZIP, TGZ, LZ4-TAR) to improve performance and reduce metadata overhead.

AISLoader can benchmark both archive creation (PUT) and reading individual files from existing shards (GET).

Archive Parameters

When the bucket contains shards, aisloader automatically:

1. Lists objects with archive expansion enabled
2. Detects archived files (e.g., shard-987.tar/file-042.bin)
3. Reads from them using the ?archpath= API parameter

The displayed statistics will show whether objects are plain or archived, e.g.:

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Found 108,959 plain objects and 1,089,590 archived files (91% archived)

Usage Examples

Create 100% shards (each containing 10 files)

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$ aisloader -bucket=ais://abc -pctput=100 -arch.pct=100 \
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            -arch.num-files=10 -arch.minsize=1K -arch.maxsize=100K \
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            -duration=5m -cleanup=false

All PUTs create shards; each shard contains 10 files between 1KB and 100KB.

Mixed workload: 30% shards, 70% plain objects

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$ aisloader -bucket=ais://abc -pctput=100 -arch.pct=30 \
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            -arch.num-files=10 -arch.minsize=1K -arch.maxsize=100K \
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            -duration=5m -cleanup=false

30% of PUT operations create shards; the rest create plain objects.

Read from existing shards

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$ aisloader -bucket=ais://abc -pctput=0 -duration=1h -cleanup=false

When the target bucket contains shards, aisloader automatically:

1. Lists objects with archive expansion enabled
2. Identifies archived files (e.g., photos.tar/00042.jpg)
3. Issues GET requests using ?archpath= to retrieve individual files inside shards

Example startup message:

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Found 108,959 plain objects and 1,089,590 archived files (91% archived)

Create shards using a specific archive format

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$ aisloader -bucket=ais://abc -pctput=100 -arch.pct=100 -arch.format=.tgz -arch.num-files=20 -duration=5m -cleanup=false

Performance Considerations

• Small-file workloads benefit greatly from sharding: fewer large objects → fewer metadata lookups → higher throughput.

• Archived GETs add CPU overhead, especially for compressed formats (.tgz, .tar.lz4).

• Throughput vs. operation rate tradeoff:

  • Large plain objects → high MB/s
  • Many tiny files inside shards → lower MB/s but similar (or higher) operations/sec

Example typical comparison:

• Reading 16KB plain objects: ~250 MiB/s
• Reading 1KB archived files: ~13 MiB/s, but with comparable GET operations/sec

Limitations

• Multipart uploads of shards are not yet supported (requires streaming chunk writer).
• Direct S3 access (-s3endpoint) does not support archive operations (sharding requires AIStore).

For more information about AIStore’s archive/shard support, see

• AIStore Archive Documentation.

Multipart Download Stream

aisloader can benchmark the MultipartDownloadStream API, which downloads a single object using multiple concurrent HTTP range requests. This improves single-object GET throughput for chunked objects by engaging multiple disks on the server side.

Configuration

When -pctmpdstream is non-zero, the specified fraction of GET operations will use MultipartDownloadStream instead of regular single-stream GET. The remaining GETs use the standard path. Statistics for MPD stream operations are tracked separately (labeled GET-MPDSTREAM in the output).

Usage Example

100% read using multipart download stream with 32 workers:

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$ aisloader -bucket=ais://my_bucket -pctput=0 -duration=10m -numworkers=4 \
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            -pctmpdstream=100 -mpdstream-workers=32 -mpdstream-chunk-size=64MiB \
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            -cleanup=false

Mixed workload — 50% regular GET, 50% MPD stream:

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$ aisloader -bucket=ais://my_bucket -pctput=0 -duration=10m -numworkers=8 \
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            -pctmpdstream=50 -mpdstream-workers=16 -cleanup=false

Restrictions

• Cannot be combined with -s3endpoint (direct S3 access)
• Cannot be combined with -get-batchsize
• Cannot be combined with -readoff / -readlen (range reads)

Get-Batch Support

With version 2.1, aisloader can now benchmark Get-Batch operations using the --get-batchsize flag (range: 1-1000). The tool consumes TAR streams (see note below), validates archived file counts, and tracks Get-Batch-specific statistics. The --continue-on-err flag enables testing of soft-error handling behavior.

Supported serialization formats include: .tar (default), .tar.gz, .tar.lz4, and .zip.

Random Access Across Very Large Collections

The tool uses the [name-getter] abstraction (see https://github.com/NVIDIA/aistore/blob/main/bench/tools/aisloader/namegetter/ng.go) to enable efficient random reads across very large collections: objects and archived files.

The --epochs N flag enables full-dataset read passes, with different algorithms selected automatically based on dataset size:

PermAffinePrime: For datasets larger than 100k (by default) objects, an affine transformation with prime modulus provides memory-efficient pseudo-random access without storing full permutations. The algorithm fills batch requests completely and may span epoch boundaries.

PermShuffle: For datasets up to (default) 100k objects, Fisher-Yates shuffle with uint32 indices (50% memory reduction compared to previous implementation).

Selection Logic:

Command-line override to set the size threshold (instead of default 100k): --perm-shuffle-max flag.

Examples

For the most recently updated command-line options and examples, please run aisloader or aisloader usage.

1. Create a 10-seconds load of 50% PUT and 50% GET requests:

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$ aisloader -bucket=my_ais_bucket -duration=10s -pctput=50 -provider=ais
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Found 0 existing objects
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Run configuration:
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{
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    "proxy": "http://172.50.0.2:8080",
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    "provider": "ais",
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    "bucket": "my_ais_bucket",
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    "duration": "10s",
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    "put upper bound": 0,
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    "put %": 50,
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    "minimal object size in Bytes": 1024,
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    "maximum object size in Bytes": 1048576,
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    "worker count": 1,
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    "stats interval": "10s",
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    "backed by": "sg",
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    "cleanup": true
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}
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Actual run duration: 10.313689487s
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Time      OP    Count                 	Total Bytes           	Latency(min, avg, max)              	Throughput            	Error
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01:52:52  Put   26                    	11.19GB               	296.39ms   5.70s      14.91s        	639.73MB              	0
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01:52:52  Get   16                    	3.86GB                	58.89ms    220.20ms   616.72ms      	220.56MB              	0
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01:52:52  CFG   0                     	0B                    	0.00ms     0.00ms     0.00ms        	0B                    	0
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01:52:52 Clean up ...
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01:52:54 Clean up done

2. Time-based 100% PUT into ais bucket. Upon exit the bucket is destroyed:

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$ aisloader -bucket=nvais -duration 10s -cleanup=true -numworkers=3 -minsize=1K -maxsize=1K -pctput=100 -provider=ais

3. Timed (for 1h) 100% GET from a Cloud bucket, no cleanup:

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$ aisloader -bucket=aws://nvaws -duration 1h -numworkers=30 -pctput=0 -cleanup=false

4. Mixed 30%/70% PUT and GET of variable-size objects to/from a Cloud bucket. PUT will generate random object names and is limited by the 10GB total size. Cleanup enabled - upon completion all generated objects and the bucket itself will be deleted:

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$ aisloader -bucket=s3://nvaws -duration 0s -cleanup=true -numworkers=3 -minsize=1024 -maxsize=1MB -pctput=30 -totalputsize=10G

5. PUT 1GB total into an ais bucket with cleanup disabled, object size = 1MB, duration unlimited:

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$ aisloader -bucket=nvais -cleanup=false -totalputsize=1G -duration=0 -minsize=1MB -maxsize=1MB -numworkers=8 -pctput=100 -provider=ais

6. 100% GET from an ais bucket:

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$ aisloader -bucket=nvais -duration 5s -numworkers=3 -pctput=0 -provider=ais -cleanup=false

7. PUT 2000 objects named as aisloader/hex({0..2000}{loaderid}):

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$ aisloader -bucket=nvais -duration 10s -numworkers=3 -loaderid=11 -loadernum=20 -maxputs=2000 -objNamePrefix="aisloader" -cleanup=false

8. Use random object names and loaderID to report statistics:

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$ aisloader -loaderid=10

9. PUT objects with random name generation being based on the specified loaderID and the total number of concurrent aisloaders:

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$ aisloader -loaderid=10 -loadernum=20

10. Same as above except that loaderID is computed by the aisloader as hash(loaderstring) & 0xff:

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$ aisloader -loaderid=loaderstring -loaderidhashlen=8

11. Print loaderID and exit (all 3 examples below) with the resulting loaderID shown on the right:

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$ aisloader -getloaderid (0x0)
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$ aisloader -loaderid=10 -getloaderid (0xa)
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$ aisloader -loaderid=loaderstring -loaderidhashlen=8 -getloaderid (0xdb)

12. Destroy existing ais bucket. If the bucket is Cloud-based, delete all objects:

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$ aisloader -bucket=nvais -duration 0s -totalputsize=0 -cleanup=true

13. Generate load on a cluster listening on custom IP address and port:

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$ aisloader -ip="example.com" -port=8080

14. Generate load on a cluster listening on custom IP address and port from environment variable:

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$ AIS_ENDPOINT="examples.com:8080" aisloader

15. Use HTTPS when connecting to a cluster:

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$ aisloader -ip="https://localhost" -port=8080

16. PUT TAR files with random files inside into a cluster:

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$ aisloader -bucket=ais://my_bucket -duration=10s -pctput=100 -arch.pct=100 -arch.num-files=10 -arch.minsize=1K -arch.maxsize=10K -cleanup=false

17. Generate load on tar2tf ETL. New ETL is started and then stopped at the end. TAR files are PUT to the cluster. Only available when cluster is deployed on Kubernetes.

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$ aisloader -bucket=my_ais_bucket -duration=10s -pctput=100 -provider=ais -readertype=tar -etl=tar2tf -cleanup=false

18. Timed 100% GET directly from S3 bucket (notice ‘-s3endpoint’ command line):

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$ aisloader -bucket=s3://xyz -cleanup=false -numworkers=8 -pctput=0 -duration=10m -s3endpoint=https://s3.amazonaws.com

19. PUT approx. 8000 files into s3 bucket directly, skip printing usage and defaults. Similar to the previous example, aisloader goes directly to a given S3 endpoint (‘-s3endpoint’), and AIStore is not being used:

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 $ aisloader -bucket=s3://xyz -cleanup=false -minsize=16B -maxsize=16B -numworkers=8 -pctput=100 -totalputsize=128k -s3endpoint=https://s3.amazonaws.com -quiet

20. Generate a list of object names (once), and then run aisloader without executing list-objects:

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$ ais ls ais://nnn --props name -H > /tmp/a.txt
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$ aisloader -bucket=ais://nnn -duration 1h -numworkers=30 -pctput=0 -filelist /tmp/a.txt -cleanup=false

21. GetBatch example: read random batches each consisting of 64 archived files

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    $ ais ls ais://nnn --summary
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    NAME             PRESENT         OBJECTS         SIZE (apparent, objects, remote)        USAGE(%)
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    ais://nnn        yes             108959 0        1.67GiB 1.66GiB 0B                      0%
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    $ aisloader -bucket=ais://nnn -pctput=0 -duration=90m -numworkers=4 -cleanup=false -get-batchsize=64 --quiet -epochs 7 -cont-on-err
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    Found 1,089,590 archived files
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    Runtime configuration:
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    {
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       "proxy": "http://ais-endpoint:51080",
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       "bucket": "ais://nnn",
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       "duration": "1h30m0s",
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       "# workers": 4,
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       "stats interval": "10s",
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       "GET(batch): batch size": 64,
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       "archive (shards)": {
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          "% workload": 100,
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          "format": ".tar",
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          "minimum file size": 1024,
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          "maximum file size": 1048576
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       },
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       "name-getter": "unique epoch-based",
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       "cleanup": false
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    }
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    Time      OP    Count                   Size (Total)            Latency (min, avg, max)                 Throughput (Avg)        Errors (Total)
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    14:16:45  GBT   6,602 (6,602)           412.6MiB (412.6MiB)     4.399ms    6.030ms    22.278ms          41.26MiB/s (41.26MiB/s) -
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    14:16:55  GBT   6,397 (12,999)          399.8MiB (812.4MiB)     4.566ms    6.225ms    17.136ms          39.98MiB/s (40.62MiB/s) -
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    14:17:05  GBT   6,201 (19,200)          387.6MiB (1.2GiB)       4.609ms    6.424ms    22.505ms          38.75MiB/s (40.00MiB/s) -
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    14:17:15  GBT   6,127 (25,327)          382.9MiB (1.5GiB)       4.821ms    6.500ms    21.599ms          38.30MiB/s (39.57MiB/s) -
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    14:17:25  GBT   6,153 (31,480)          384.6MiB (1.9GiB)       4.765ms    6.473ms    23.135ms          38.46MiB/s (39.35MiB/s) -
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    ...

Collecting stats

Collecting is easy - aisloader supports at-runtime monitoring via with Graphite using StatsD. When starting up, aisloader will try to connect to provided StatsD server (see: statsdip and statsdport options). Once the connection is established the statistics from aisloader are send in the following format:

<metric_type>.aisloader.<hostname>-<loaderid>.<metric>

• metric_type - can be: gauge, timer, counter
• hostname - is the hostname of the machine on which the loader is ran
• loaderid - see: -loaderid option
• metric - can be: latency.*, get.*, put.*

Grafana

Grafana helps visualize the collected statistics. It is convenient to use and provides numerous tools to measure and calculate different metrics.

We provide simple script which allows you to set up the Graphite and Grafana servers which run inside separate dockers. To add new dashboards and panels, please follow: grafana tutorial.

When selecting a series in panel view, it should be in the format: stats.aisloader.<loader>.*. Remember that metrics will not be visible (and you will not be able to select them) until you start the loader.

HTTP tracing

Following is a brief illustrated sequence to enable detailed tracing, capture statistics, and toggle tracing on/off at runtime.

IMPORTANT NOTE:

The amount of generated (and extremely detailed) metrics can put a strain on your StatsD server. That’s exactly the reason for runtime switch to toggle HTTP tracing on/off. The example below shows how to do it (in particular, see kill -HUP).

1. Run aisloader for 90s (32 workers, 100% write, sizes between 1KB and 1MB) with detailed tracing enabled:

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$ aisloader -bucket=ais://abc -duration 90s -numworkers=32 -minsize=1K -maxsize=1M -pctput=50 --cleanup=false --trace-http=true

2. Have netcat listening on the default StatsD port 8125:

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$ nc 8125 -l -u -k
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# The result will look as follows - notice "*latency*" metrics (in milliseconds):
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5
...
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aisloader.u18044-0.put.latency.posthttp:0.0005412320409368235|ms|@0.000049
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aisloader.u18044-0.put.latency.proxyheader:0.06676835268647904|ms|@0.000049
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aisloader.u18044-0.put.latency.targetresponse:0.7371088368431411|ms|@0.000049aisproxy.DLEp8080.put.count:587262|caistarget.vuIt8081.kalive.ms:1|ms
9
aistarget.vuIt8081.disk.sda.avg.rsize:58982|g
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aistarget.vuIt8081.disk.sda.avg.wsize:506227|g
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aistarget.vuIt8081.put.count:587893|c
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aistarget.vuIt8081.put.redir.ms:2|ms
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aistarget.vuIt8081.disk.sda.read.mbps:5.32|g
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aistarget.vuIt8081.disk.sda.util:3|gaisloader.u18044-0.get.count:0|caisloader.u18044-0.put.count:19339|caisloader.u18044-0.put.pending:7|g|@0.000052aisloader.u18044-0.put.latency:4.068928072806246|ms|@0.000052
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aisloader.u18044-0.put.minlatency:0|ms|@0.000052
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aisloader.u18044-0.put.maxlatency:60|ms|@0.000052aisloader.u18044-0.put.throughput:1980758|g|@0.000052aisloader.u18044-0.put.latency.posthttp:0.0005170898185014737|ms|@0.000052
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aisloader.u18044-0.put.latency.proxyheader:0.06742851233259217|ms|@0.000052
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aisloader.u18044-0.put.latency.proxyrequest:0.1034696726821449|ms|@0.000052
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aisloader.u18044-0.put.latency.targetheader:0.0699622524432494|ms|@0.000052
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aisloader.u18044-0.put.latency.targetrequest:0.09168002482031129|ms|@0.000052
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aisloader.u18044-0.put.latency.targetresponse:0.806660116862299|ms|@0.000052
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aisloader.u18044-0.put.latency.proxy:0.6616681317544858|ms|@0.000052
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aisloader.u18044-0.put.latency.targetconn:1.0948859816950205|ms|@0.000052
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aisloader.u18044-0.put.latency.proxyresponse:0.425616629608563|ms|@0.000052
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aisloader.u18044-0.put.latency.proxyconn:1.2669734732923108|ms|@0.000052
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aisloader.u18044-0.put.latency.target:1.0063602047675682|ms|@0.000052aisproxy.DLEp8080.put.count:605044|caistarget.vuIt8081.put.redir.ms:2|ms
27
...

3. Finally, toggle detailed tracing on and off by sending aisoader SIGHUP:

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$ pgrep -a aisloader
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3800 aisloader -bucket=ais://abc -duration 90s -numworkers=32 -minsize=1K -maxsize=1M -pctput=100 --cleanup=false --trace-http=true
3
4
# kill -1 3800
5
# or, same: kill -HUP 3800

The result:

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Time      OP    Count                   Size (Total)            Latency (min, avg, max)                 Throughput (Avg)        Errors (Total)
2
10:11:27  PUT   20,136 (20,136 8 0)     19.7MiB (19.7MiB)       755.308µs  3.929ms    42.493ms         1.97MiB/s (1.97MiB/s)   -
3
...
4
Detailed latency info is disabled
5
...
6
7
# As stated, `SIGHUP` is a binary toggle - next time used it'll enable detailed trace with `aisloader printing:
8
9
Detailed latency info is enabled

Note that other than --trace-http, all command-line options in this section are used for purely illustrative purposes.

AISLoader Composer

For benchmarking production-level clusters, a single AISLoader instance may not be able to fully saturate the load the cluster can handle. In this case, multiple aisloader instances can be coordinated via the AISLoader Composer. See the README for instructions on setting up.

References

For AIS observability (including CLI, Prometheus, and Kubernetes integration), please see:

• AIS Observability Overview

For StatsD compliant backends, see:

• StatsD backends

Finally, for another supported - and alternative to StatsD - monitoring via Prometheus integration, see:

• Prometheus