Performance improvements to DFSClient and DataNode for faster DFS write at
replication factor of 1
--------------------------------------------------------------------------------------------------
Key: HDFS-959
URL: https://issues.apache.org/jira/browse/HDFS-959
Project: Hadoop HDFS
Issue Type: Improvement
Components: data-node, hdfs client
Affects Versions: 0.20.2, 0.22.0
Environment: RHEL5 on Dual CPU quad-core Intel servers, 16 GB RAM, 4
SATA disks.
Reporter: Naredula Janardhana Reddy
Fix For: 0.20.2, 0.22.0
The following improvements are suggested to DFSClient and DataNode to improve
DFS write throughput, based on experimental verification with replication
factor of 1.
The changes are useful in principle for replication factors of 2 and 3 as well,
but they do not currently demonstrate noticeable performance improvement in our
test-bed because of a network throughput bottleneck that hides the benefit of
these changes.
All changes are applicable to 0.20.2. Some of them are applicable to trunk, as
noted below. I have not verified applicability to 0.21.
List of Improvements
-----------------------------
Item 1: DFSCilent. Finer grain locks in WriteChunk(). Currently the lock is
held at the data block level (512 bytes). It can be moved to the packet level
(64kbytes), to lower the frequency of locking.
This optimization applies to 20.2. It already appears in trunk.
Item 2: Misc. improvements to DataNode
2.1: Concurrency of Disk Writes: Check sum verification and writing data to
disk can be moved to a separate thread ("Disk Write Thread"). This will allow
the existing "network thread" to trigger faster acks to the DFSClient. This
will also allow the packet to be transmitted to the replication node faster. In
effect, this will allow DataNode to consume packets at higher speeds.
This optimization applies to 20.2 and trunk.
2.2: Bulk Receive and Bulk Send: This optimization is enabled by doing 2.1.
We can now have DataNode receive more than one packet at a time since we have
added a buffer between the (existing) network thread and the (newly added) Disk
Write thread.
This optimization applies to 20.2 and trunk.
2.3: Early Ack: The proposed optimization is to send out acks to the client
as soon as possible instead of waiting for the disk write. Note that, the last
ack is an exception: It will be sent only after data has been flushed to the OS.
This optimization applies to 20.2. It already appears in trunk.
2.4: lseek optimization: Currently lseek (the system call) is called before
every disk write, which is not necessary when the write is sequential. The
propsed optimization calls lseek only when necessary.
This optimization applies to 20.2. I was unable to tell if it is already in
trunk.
2.5 Checksum buffered writes: Currently checksum is written in a buffered
stream of size 512 bytes. This can be increased to a higher numbers - such as
4kbytes - to lower the number of write() system calls. This will save context
switch overhead.
This optimization applies to 20.2. I was unable to tell if it is already in
trunk.
Item 3: Applying HADOOP-6166 - PureJavaCrc32() - from trunk to 20.2
This is applicable to 20.2. It already appears in trunk.
Performance Experiments Results
-----------------------------------------------
Performance experiments showed the following numbers:
Hadoop Version: 0.20.2
Server Configs: RHEL5, Quad-core dual-CPU, 16GB RAM, 4 SATA disks
$ uname -a
Linux gsbl90324.blue.ygrid.yahoo.com 2.6.18-53.1.13.el5 #1 SMP Mon Feb 11
13:27:27 EST 2008 x86_64 x86_64 x86_64 GNU/Linux
$ cat /proc/cpuinfo
model name : Intel(R) Xeon(R) CPU L5420 @ 2.50GHz
$ cat /etc/issue
Red Hat Enterprise Linux Server release 5.1 (Tikanga)
Kernel \r on an \m
Benchmark Details
--------------------------
Benchmark Name: DFSIO
Benchmark Configuration:
a) # maps (writers to DFS per node). Tried the following values: 1,2,3
b) # of nodes: Single-node test and 15-node cluster test
Results Summary
--------------------------
a) With all the above optimizations turned on
All these tests were done with replication factor of 1. Tests with replication
factors of 2 and 3 showed no noticeably improvement, because these improvements
are shielded by network bandwidth as noted above.
What was measured: Write throughput per client (in MB/s)
| Test Description |
Baseline (MB/s) | With improvements (MB/s) | % improvement |
| 15-node cluster with 1 map (writer) per node | 103
| 147 | ~43 % |
| Single node test with 1 maps (writer) per node | 102
| 148 | ~45 % |
| Single node test with 2 maps (writers) per node | 86
| 101 | ~16 % |
| Single node test with 3 maps (writers) per node | 67
| 76 | ~13 %
|
a) With above optimizations turned on individually
I ran some experiments by adding and removing items individually to understand
the approximate range of performance contribution from each item. These are the
numbers I got (They are approximate).
| ITEM | Title
| Improvement in 0.20 | Improvement in trunk |
| Item 1 | DFSCilent. Finer grain locks in WriteChunk() | 30%
| Already in trunk |
| Item 2.1 | Concurrency of Disk Writes |
25% | 15-20% |
| Item 2.2 | Bulk Receive and Bulk Send |
2% | (Have not yet tried) |
| Item 2.3 | Early Ack
| 2% | Already in trunk |
| Item 2.4 | lseek optimization
| 2% | (Have not yet tried) |
| Item 2.5 | Checksum buffered writes |
2% | (Have not yet tried) |
| Item 3 | Applying HADOOP-6166 - PureJavaCrc32() | 15%
| Already in trunk |
Patches
-----------
I will submit a patch for 0.20.2 shortly (in a day).
I expect to submit a patch for trunk after review comments for above patch.
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