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).
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.
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
- Check your loaded kernel version using:
-
nvme-cli
- Ubuntu:
sudo apt-get install nvme-cli - Fedora:
sudo dnf -y install nvme-cli
- Ubuntu:
-
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
- Ubuntu:
-
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.shThe 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
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
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.
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
- All fio benchmarks are setting the none scheduler by default if the iodepth is 1.
- When doing random fio workloads, the
norandommapfio 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
-
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.
-
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)
- written_gb: gigabytes written (GB)
- write_avg_mbs: average throughput (MB/s)
-
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)
- write_avg_mbs_target: target write throughput (MB/s)
- read_lat_avg_us: avg 4KB random read latency (us)
- write_avg_mbs: write throughput (MB/s)
- read_lat_us_avg_measured: avg 4KB random read latency (us)
- 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.
-
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
- avg_lat_us: Average latency in µs for the specific run.
- throughput_MiBs: Throughput in MiBs for the specific run.
- clat_p1_us - clat_p100us: completion latency percentiles in µs.
-
Generates multiple graphs that plot the behavior of throughput and latency.
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.
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
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;
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