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, our Matrix chat, or on Slack.

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
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 <args>

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'.

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 <IP>
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;

westerndigitalcorporation / zbdbench

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