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https://github.com/johnramsden/zbdbench


https://github.com/johnramsden/zbdbench

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README 

        
# ZBDBench: Benchmark Suite for Zoned Block Devices



ZBDBench is a collection of benchmarks for zoned storage devices (Zoned Namespace (ZNS) SSDs and Shingled-Magnetic Recording (SMR) HDDs) that tests both the raw performance of the device, and runs standard benchmarks for applications such as RocksDB (dbbench) and MySQL (sysbench).



Community

---------

For help or questions about zbdbench usage (e.g. "how do I do X?") see [ZonedStorage.io](https://zonedstorage.io), our [Matrix](https://app.element.io/#/room/#zonedstorage-general:matrix.org) chat, or on [Slack](https://join.slack.com/t/zonedstorage/shared_invite/zt-uyfut5xe-nKajp9YRnEWqiD4X6RkTFw).



To report a bug, file a documentation issue, or submit a feature request, please open a GitHub issue.



For release announcements and other discussions, please subscribe to this repository or join us on Matrix.



Dependencies

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



The benchmark tool requires Python 3.4+. In addition to a working python

environment, the script requires the following installed:



 - Linux kernel 5.9 or newer

   - Check your loaded kernel version using:

     `uname -a`



 - nvme-cli

   - Ubuntu: `sudo apt-get install nvme-cli`

   - Fedora: `sudo dnf -y install nvme-cli`



 - blkzone and blkdiscard (available through util-linux)

   - Ubuntu: `sudo apt-get install util-linux`

   - Fedora: `sudo dnf -y install util-linux-ng`

   - CentOS: `sudo yum -y install util-linux-ng`



 - a valid container (podman) environment

   - If you do not have a container environment installed, please see [this

     link](https://podman.io/getting-started/installation)



 - installed containers:

   - zfio - contains latest fio compiled with zone capacity support

   - zrocksdb - contains rocksdb with zenfs built-in

   - zzenfs - contains the zenfs tool to inspect the zenfs file-system



   The containers can be installed with:

     `cd recipes/docker; sudo ./build.sh`



   The container installation can be verified by listing the image:

     `sudo podman images`



  - matplotlib, pandas and openpyxl for graph plotting

    ```

    sudo pip install matplotlib

    sudo pip install pandas

    sudo pip install openpyxl

    ```



Getting Started

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



The run.py script runs a predefined benchmark on a block device.



The block device does not have to be zoned - the workloads will work

on both types of block devices.



The script performs a set of checks before running the benchmark, such as

validating that it is about to write to a block device, not mounted, and ready.



After the benchmark has run, the output is available in:



    zbdbench_results/YYYYMMDDHHMMSS (date format is replaced with the current time)



Each benchmark has a report function, which creates a csv file with the

specific output. See the section below for the csv format for each benchmark.



To execute the 'fio_zone_mixed' benchmark, run:



    sudo ./run.py -d /dev/nvmeXnY -b fio_zone_mixed



If you have the latest fio installed, you may skip the container installation and

run the benchmarks using the system commands.



    sudo ./run.py -d /dev/nvmeXnY -b fio_zone_mixed -c no



To list available benchmarks, run:



    ./run.py -l



## WARNING



You need to have read/write permissions to the device or file you are

targeting. Usually block devices are owned by `root` user or `disk` group. You

can either change ownership of the block device your are testing:



    sudo chown myusername /dev/nvmeXnY



or make it world writable:



    sudo chmod o+rw /dev/nvmeXnY



Or elevate the privileges when running `zbdbench`:



    sudo ./run.py 



Please be sure that you are familiar with the security implications of the

option you choose. If you start a test on a different block device than the one

you intended, you may loose data and your system may fail to boot.



Command Options

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



List available benchmarks:



    ./run.py -l



Run specific benchmark:



    ./run.py -b benchmark -d /dev/nvmeXnY



Run fio_zone_xxx benchmark with SPDK FIO plugin(io_uring zoned bdev) in a container env.:



    ./run.py -b fio_zone_xxx --mq-deadline-scheduler -d /dev/nvmeXnY -s yes -c yes



Run fio_zone_xxx benchmark with SPDK FIO plugin(io_uring zoned bdev) directly on Host System.

Zbdbench will checkout and build SPDK(also FIO) in dir provided using --spdk-path option:



    ./run.py -b fio_zone_xxx --mq-deadline-scheduler -d /dev/nvmeXnY -s yes -c no --spdk-path /dir/path



Regenerate a report (and its plots)



    ./run.py -b fio_zone_mixed -r zbdbench_results/YYYYMMDDHHMMSS



Regenerate plots from existing csv report



    ./run.py -b fio_zone_throughput_avg_lat -p zbdbench_results/YYYYMMDDHHMMSS/fio_zone_throughput_avg_lat.csv



Overwrite benchmark run with the none device scheduler:



    ./run.py -b benchmark -d /dev/nvmeXnY --none-scheduler



Overwrite benchmark run with the mq-deadline device scheduler:



    ./run.py -b benchmark -d /dev/nvmeXnY --mq-deadline-scheduler



Benchmarks

----------



- All fio benchmarks are setting the none scheduler by default if the iodepth is 1.

- When doing random fio workloads, the `norandommap` fio option is set.



SPDK FIO plugin support:

  - Following benchmarks have SPDK FIO plugin support

     - fio_zone_write

     - fio_zone_mixed

     - fio_zone_throughput_avg_lat

  - Adding SPDK FIO plugin support for a new benchmark

     - See benchs/template.py for guidance



## fio_steady_state_performance

  - Puts the (conventional) drive into its steady state by completely filling it

    and then overwriting it. This puts conventional block devices into the state

    where the on device garbage colletion is working to free up space.



  - (Random) Read and (Random) Write performance of the drive is subseqently messured.



### fio_zone_write

  - executes a fio workload that writes sequential to 14 zones in parallel and

    while writing 6 times the capacity of the device.



  - generated csv output (fio_zone_write.csv)

    1. written_gb: gigabytes written (GB)

    2. write_avg_mbs: average throughput (MB/s)



### fio_zone_mixed

  - executes a fio workload that first preconditions the block device to steady

    state. Then rate limited writes are issued, in which 4KB random reads

    are issued in parallel. The average latency for the 4KB random read is

    reported.



  - generated csv output (fio_zone_mixed.csv)

    1. write_avg_mbs_target: target write throughput (MB/s)

    2. read_lat_avg_us: avg 4KB random read latency (us)

    3. write_avg_mbs: write throughput (MB/s)

    4. read_lat_us_avg_measured: avg 4KB random read latency (us)

    5. clat_*_us: Latency percentiles



    ** Note that (2) is only reported if write_avg_mbs_target and write_avg_mbs

       are equal. When they are not equal, the reported average latency is

       misleading, as the write throughput requested has not been possible to

       achieve.



### fio_zone_throughput_avg_lat

  - Executes all combinations of the following workloads report the throughput

    and latency in the csv report (Note: 14 is a possible value for max_open_zones):

      - Sequential read, random read, sequential write

      - BS: 4K, 8K, 16K, 32K, 64K, 128K

      - Sequential write and sequential read specific:

        - Number of parallel jobs: 1, 2, 4, 8, 14, 16, 32, 64, 128 (skipping entries > max_open_zones)

        - QD: 1

        - ioengine: psync

      - Random read specific:

        - QD: 1, 2, 4, 8, 14, 16, 32, 64, 128

        - ioengine: io_uring



    For reads the drive is prepared with a write. The ZBD is reset before each

    run.



  - Generated csv output file is fio_zone_throughput_avg_lat.csv

    1. avg_lat_us: Average latency in µs for the specific run.

    2. throughput_MiBs: Throughput in MiBs for the specific run.

    3. clat_p1_us - clat_p100us: completion latency percentiles in µs.



  - Generates multiple graphs that plot the behavior of throughput and latency.



### usenix_atc_2021_zns_eval

  Executes RocksDB's db_bench according to the RocksDB evaluation section

  (5.2 RocksDB) of the paper '[ZNS: Avoiding the Block Interface Tax for

  Flash-based SSDs](https://www.pdl.cmu.edu/PDL-FTP/Storage/USENIX_ATC_2021_ZNS.pdf)'.



  Depending on if the specified drive to benchmark is a ZNS or Conventional

  device different benchmarks are run.

  - For conventional devices the db_bench workload is run on the following

    filesystems:

        - xfs

        - f2fs

  - For ZNS devices the db_bench workload is run on the f2fs filesystem and

    with the ZenFS RocksDB plugin without an additional filesystem.



  Note: the tests are designed to run on 2TB devices.



### sysbench

  Executes a sysbench workload within a percona-server MyRocks installation.

  For conventional devices, the default filesystem will be xfs whereas for

  ZBD devices by default the benchmark will be issued through ZenFS, the

  RocksDB plugin which enables direct access to zoned storage.

  If the `-x btrfs` is supplied the benchmark will run on zoned or

  conventional devices with btrfs as the filesystem.



  The benchmark will first bulk-load the drive with a database of about 800GB.

  10 million `db-entries` correspond to ~2GB of capacity.

  With `200.000.000 table-size * 20 tables = 4000M db-entries` the database

  size will result in 800GB.

  After that the following oltp workloads are run each for 30 minutes in the

  given order:

  - oltp_update_index.lua

  - oltp_update_non_index.lua

  - oltp_delete.lua

  - oltp_write_only.lua

  - oltp_insert.lua

  - oltp_read_write.lua

  - oltp_read_only.lua



Advance Data Analysis using SQLite

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

Benchmarks can implement to collect their CSV report into a SQLite database.

See `data_collector/sqlite_data_collector.py`



The database file `data-collection.sqlite3` will be created/modified in the

given output directory (by default `zbdbench_results`)



The database design is keeped in an easy format. Each ZBDBench benchmarking run

causes an entry in the `zbdbench_run` table which collects general system

information.

Each ZBDBench run can generate multiple results that are collected in a

benchmark specific table (e.g. `fio_zone_throughput_avg_lat`)



TODO: Add graph for the database layout



In case you want to connect your SQLite DB with Excel you need to install the

MySQL ODBC https://dev.mysql.com/downloads/connector/odbc/ .



On MacOS also install iOBDC http://www.iodbc.org/dataspace/doc/iodbc/wiki/iodbcWiki/Downloads .

Copy /usr/local/mysql-connector-odbc-8.0.12-macos10.13-x86-64bit to

/Library/ODBC and adjust /Library/ODBC/odbcinst.init

https://stackoverflow.com/questions/52896893/macos-connector-mysql-odbc-driver-could-not-be-loaded-in-excel-for-mac-2016 .



In the 'ODBC Data Source Administrator' a 'User DSN' needs to be created with the

following keywords and values:

```

SERVER 

NO_SCHEMA 1

```



Within Excel in the 'Data' tab you can 'Get Data' 'From Database (Microsoft Query)' with

the specified 'User DSN' and the following query:



```

SELECT * FROM fio_zone_throughput_avg_lat INNER JOIN zbdbench_run ON fio_zone_throughput_avg_lat.zbdbench_run_id = zbdbench_run.id;

```