Table of Contents

Independent Benchmarks

Sugon (中科曙光)

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

Setup Hadoop Benchmark

Here are my steps. First, checkout hadoop 2.10.0 binary, untar, and cd in to the hadoop directory.

wget http://apache.mirrors.hoobly.com/hadoop/common/hadoop-2.10.0/hadoop-2.10.0.tar.gz
tar xvf hadoop-2.10.0.tar.gz
cd hadoop-2.10.0

Modify the file ./etc/hadoop/core-site.xml

<configuration>
    <property>
        <name>fs.seaweedfs.impl</name>
        <value>seaweed.hdfs.SeaweedFileSystem</value>
    </property>
    <property>
        <name>fs.defaultFS</name>
        <value>seaweedfs://localhost:8888</value>
    </property>
</configuration>

Then get the seaweedfs hadoop client jar.

cd share/hadoop/common/lib/
wget https://repo1.maven.org/maven2/com/seaweedfs/seaweedfs-hadoop2-client/3.80/seaweedfs-hadoop2-client-3.80.jar

TestDFSIO Benchmark

The TestDFSIO benchmark is used for measuring I/O (read/write) performance.

However, the generated data to write is all zero. SeaweedFS automatically compress this kind of data. So this is not scientific for now.

TestDFSIO write tests

Start the TestDFSIO write tests:

bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-2.10.0-tests.jar TestDFSIO -write -nrFiles 8 -size 32GB -bufferSize 8388608 -resFile /tmp/TestDFSIOwrite.txt

...
20/07/25 16:48:21 INFO fs.TestDFSIO: ----- TestDFSIO ----- : read
20/07/25 16:48:21 INFO fs.TestDFSIO:             Date & time: Sat Jul 25 16:48:21 PDT 2020
20/07/25 16:48:21 INFO fs.TestDFSIO:         Number of files: 8
20/07/25 16:48:21 INFO fs.TestDFSIO:  Total MBytes processed: 262144
20/07/25 16:48:21 INFO fs.TestDFSIO:       Throughput mb/sec: 399.16
20/07/25 16:48:21 INFO fs.TestDFSIO:  Average IO rate mb/sec: 399.34
20/07/25 16:48:21 INFO fs.TestDFSIO:   IO rate std deviation: 8.56
20/07/25 16:48:21 INFO fs.TestDFSIO:      Test exec time sec: 659.45
20/07/25 16:48:21 INFO fs.TestDFSIO:

TestDFSIO read tests

Start the TestDFSIO read tests:

bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-2.10.0-tests.jar TestDFSIO -read -nrFiles 8 -size 32GB -bufferSize 8388608 -resFile /tmp/TestDFSIOwrite.txt

...

20/07/17 15:59:38 INFO fs.TestDFSIO: ----- TestDFSIO ----- : read
20/07/17 15:59:38 INFO fs.TestDFSIO:             Date & time: Fri Jul 17 15:59:38 PDT 2020
20/07/17 15:59:38 INFO fs.TestDFSIO:         Number of files: 8
20/07/17 15:59:38 INFO fs.TestDFSIO:  Total MBytes processed: 8192
20/07/17 15:59:38 INFO fs.TestDFSIO:       Throughput mb/sec: 393.26
20/07/17 15:59:38 INFO fs.TestDFSIO:  Average IO rate mb/sec: 393.72
20/07/17 15:59:38 INFO fs.TestDFSIO:   IO rate std deviation: 13.33
20/07/17 15:59:38 INFO fs.TestDFSIO:      Test exec time sec: 22.76
20/07/17 15:59:38 INFO fs.TestDFSIO:

Independent Benchmarks

Sugon (中科曙光)

To test the performance of HDFS and SeaweedFS, we did a comparison by running 4 common spark operators, such as count, group by, join and write, for group by and join , there is a count followed to act.

The basic configuration information of cluster is as follows:

HDFS：
- Node number: 25
- Total disks: 36disk * 25node = 900disk
- Disk capacity: 3.7T SATA
- Total disk capacity: 3.19PB
- Replication: 5
SeaweedFS:
- Node number: 6（3+3 rack）
- Disk capacity: 3.7T SATA
- Cluster max volume: 21500
- Total disk capacity: 799TB
- Replication policy: 010

Here are the details and results of our test. At the beginning of the test, we put our data to both HDFS and SeaweedFS. The amount of the data is 100 million records, and stored in 200 parquet files. The size of each parquet file is about 89 MB. We ran spark on yarn with 20 executors. In spark, we got two DataFrames by reading parquet from HDFS and HCFS separately, then executed count, group by and join by 100 times , and write by 10 times, on each DataFrame.

As for count, SeaweedFS's advantage is obvious. The average time of the DataFrame from HDFS is 4.05 seconds, while SeaweedFS is only 0.659. Following is the result:

Summary	HDFS	SeaweedFS
Count	100	100
Mean	4.050	0.659
Stddev	0.264	0.941
Min	3.678	0.392
Max	5.692	9.688

As for write, we wrote the DataFrame from HDFS to SeaweedFS, and wrote the DataFrame from HCFS to SeaweedFS. Following is the result:

Summary	HDFS	SeaweedFS
Count	10	10
Mean	234.279	232.078
Stddev	26.823	12.652
Min	216.931	214.349
Max	307.330	252.375

As for group by, following is the result:

Summary	HDFS	SeaweedFS
Count	100	100
Mean	14.121	12.515
Stddev	1.972	1.255
Min	12.879	11.322
Max	32.296	22.573

As for join, every DataFrame join with itself on one column. Following is the result:

Summary	HDFS	SeaweedFS
Count	100	100
Mean	25.684	23.897
Stddev	0.934	1.381
Min	24.006	22.275
Max	30.991	30.279

Introduction

API

Configuration

Filer

Filer Stores

Advanced Filer Configurations

FUSE Mount

WebDAV

Cloud Drive

AWS S3 API

AWS IAM

Machine Learning

TensorFlow with SeaweedFS

HDFS

Replication and Backup

Async Replication to another Filer [Deprecated]
Async Backup
Async Filer Metadata Backup
Async Replication to Cloud [Deprecated]
Kubernetes Backups and Recovery with K8up