Optimise the negative xattr lookup with bloom filter.
The bit value for the bloom filter map has a reverse semantics for
compatibility. That is, the bit value of 0 indicates existence, while
the bit value of 1 indicates the absence of corresponding xattr.
The initial version is _only_ enabled when xattr_filter_reserved is
zero. The filter map internals may change in the future, in which case
the reserved flag will be set non-zero and we don't need bothering the
compatible bits again at that time. For now disable the optimization if
this reserved flag is non-zero.
Signed-off-by: Jingbo Xu <jefflexu@linux.alibaba.com>
Reviewed-by: Gao Xiang <hsiangkao@linux.alibaba.com>
Reviewed-by: Chao Yu <chao@kernel.org>
Link: https://lore.kernel.org/r/20230722094538.11754-3-jefflexu@linux.alibaba.com
Signed-off-by: Gao Xiang <hsiangkao@linux.alibaba.com>
Add DEFLATE compression as the 3rd supported algorithm.
DEFLATE is a popular generic-purpose compression algorithm for quite
long time (many advanced formats like gzip, zlib, zip, png are all
based on that) as Apple documentation written "If you require
interoperability with non-Apple devices, use COMPRESSION_ZLIB. [1]".
Due to its popularity, there are several hardware on-market DEFLATE
accelerators, such as (s390) DFLTCC, (Intel) IAA/QAT, (HiSilicon) ZIP
accelerator, etc. In addition, there are also several high-performence
IP cores and even open-source FPGA approches available for DEFLATE.
Therefore, it's useful to support DEFLATE compression in order to find
a way to utilize these accelerators for asynchronous I/Os and get
benefits from these later.
Besides, it's a good choice to trade off between compression ratios
and performance compared to LZ4 and LZMA. The DEFLATE core format is
simple as well as easy to understand, therefore the code size of its
decompressor is small even for the bootloader use cases. The runtime
memory consumption is quite limited too (e.g. 32K + ~7K for each zlib
stream). As usual, EROFS ourperforms similar approaches too.
Alternatively, DEFLATE could still be used for some specific files
since EROFS supports multiple compression algorithms in one image.
[1] https://developer.apple.com/documentation/compression/compression_algorithm
Reviewed-by: Chao Yu <chao@kernel.org>
Signed-off-by: Gao Xiang <hsiangkao@linux.alibaba.com>
Link: https://lore.kernel.org/r/20230810154859.118330-1-hsiangkao@linux.alibaba.com
As Sandeep shown [1], high priority RT per-cpu kthreads are
typically helpful for Android scenarios to minimize the scheduling
latencies.
Switch EROFS_FS_PCPU_KTHREAD_HIPRI on by default if
EROFS_FS_PCPU_KTHREAD is on since it's the typical use cases for
EROFS_FS_PCPU_KTHREAD.
Also clean up unneeded sched_set_normal().
[1] https://lore.kernel.org/r/CAB=BE-SBtO6vcoyLNA9F-9VaN5R0t3o_Zn+FW8GbO6wyUqFneQ@mail.gmail.com
Reviewed-by: Yue Hu <huyue2@coolpad.com>
Reviewed-by: Sandeep Dhavale <dhavale@google.com>
Reviewed-by: Chao Yu <chao@kernel.org>
Signed-off-by: Gao Xiang <hsiangkao@linux.alibaba.com>
Link: https://lore.kernel.org/r/20230522092141.124290-1-hsiangkao@linux.alibaba.com
Using per-cpu thread pool we can reduce the scheduling latency compared
to workqueue implementation. With this patch scheduling latency and
variation is reduced as per-cpu threads are high priority kthread_workers.
The results were evaluated on arm64 Android devices running 5.10 kernel.
The table below shows resulting improvements of total scheduling latency
for the same app launch benchmark runs with 50 iterations. Scheduling
latency is the latency between when the task (workqueue kworker vs
kthread_worker) became eligible to run to when it actually started
running.
+-------------------------+-----------+----------------+---------+
| | workqueue | kthread_worker | diff |
+-------------------------+-----------+----------------+---------+
| Average (us) | 15253 | 2914 | -80.89% |
| Median (us) | 14001 | 2912 | -79.20% |
| Minimum (us) | 3117 | 1027 | -67.05% |
| Maximum (us) | 30170 | 3805 | -87.39% |
| Standard deviation (us) | 7166 | 359 | |
+-------------------------+-----------+----------------+---------+
Background: Boot times and cold app launch benchmarks are very
important to the Android ecosystem as they directly translate to
responsiveness from user point of view. While EROFS provides
a lot of important features like space savings, we saw some
performance penalty in cold app launch benchmarks in few scenarios.
Analysis showed that the significant variance was coming from the
scheduling cost while decompression cost was more or less the same.
Having per-cpu thread pool we can see from the above table that this
variation is reduced by ~80% on average. This problem was discussed
at LPC 2022. Link to LPC 2022 slides and talk at [1]
[1] https://lpc.events/event/16/contributions/1338/
[ Gao Xiang: At least, we have to add this until WQ_UNBOUND workqueue
issue [2] on many arm64 devices is resolved. ]
[2] https://lore.kernel.org/r/CAJkfWY490-m6wNubkxiTPsW59sfsQs37Wey279LmiRxKt7aQYg@mail.gmail.com
Signed-off-by: Sandeep Dhavale <dhavale@google.com>
Signed-off-by: Gao Xiang <hsiangkao@linux.alibaba.com>
Link: https://lore.kernel.org/r/20230208093322.75816-1-hsiangkao@linux.alibaba.com
A new fscache based mode is going to be introduced for erofs, in which
case on-demand read semantics is implemented through fscache.
As the first step, register fscache volume for each erofs filesystem.
That means, data blobs can not be shared among erofs filesystems. In the
following iteration, we are going to introduce the domain semantics, in
which case several erofs filesystems can belong to one domain, and data
blobs can be shared among these erofs filesystems of one domain.
Signed-off-by: Jeffle Xu <jefflexu@linux.alibaba.com>
Reviewed-by: Gao Xiang <hsiangkao@linux.alibaba.com>
Link: https://lore.kernel.org/r/20220425122143.56815-12-jefflexu@linux.alibaba.com
Acked-by: Chao Yu <chao@kernel.org>
Signed-off-by: Gao Xiang <hsiangkao@linux.alibaba.com>
Add MicroLZMA support in order to maximize compression ratios for
specific scenarios. For example, it's useful for low-end embedded
boards and as a secondary algorithm in a file for specific access
patterns.
MicroLZMA is a new container format for raw LZMA1, which was created
by Lasse Collin aiming to minimize old LZMA headers and get rid of
unnecessary EOPM (end of payload marker) as well as to enable
fixed-sized output compression, especially for 4KiB pclusters.
Similar to LZ4, inplace I/O approach is used to minimize runtime
memory footprint when dealing with I/O. Overlapped decompression is
handled with 1) bounced buffer for data under processing or 2) extra
short-lived pages from the on-stack pagepool which will be shared in
the same read request (128KiB for example).
Link: https://lore.kernel.org/r/20211010213145.17462-8-xiang@kernel.org
Acked-by: Chao Yu <chao@kernel.org>
Signed-off-by: Gao Xiang <hsiangkao@linux.alibaba.com>
In order to support multi-layer container images, add multiple
device feature to EROFS. Two ways are available to use for now:
- Devices can be mapped into 32-bit global block address space;
- Device ID can be specified with the chunk indexes format.
Note that it assumes no extent would cross device boundary and mkfs
should take care of it seriously.
In the future, a dedicated device manager could be introduced then
thus extra devices can be automatically scanned by UUID as well.
Link: https://lore.kernel.org/r/20211014081010.43485-1-hsiangkao@linux.alibaba.com
Reviewed-by: Chao Yu <chao@kernel.org>
Reviewed-by: Liu Bo <bo.liu@linux.alibaba.com>
Signed-off-by: Gao Xiang <hsiangkao@linux.alibaba.com>
Add iomap support for non-tailpacking uncompressed data in order to
support DIO and DAX.
Direct I/O is useful in certain scenarios for uncompressed files.
For example, double pagecache can be avoid by direct I/O when
loop device is used for uncompressed files containing upper layer
compressed filesystem.
This adds iomap DIO support for non-tailpacking cases first and
tail-packing inline files are handled in the follow-up patch.
Link: https://lore.kernel.org/r/20210805003601.183063-2-hsiangkao@linux.alibaba.com
Cc: linux-fsdevel@vger.kernel.org
Reviewed-by: Chao Yu <chao@kernel.org>
Signed-off-by: Huang Jianan <huangjianan@oppo.com>
Signed-off-by: Gao Xiang <hsiangkao@linux.alibaba.com>
Since multiple pcluster sizes could be used at once, the number of
compressed pages will become a variable factor. It's necessary to
introduce slab pools rather than a single slab cache now.
This limits the pclustersize to 1M (Z_EROFS_PCLUSTER_MAX_SIZE), and
get rid of the obsolete EROFS_FS_CLUSTER_PAGE_LIMIT, which has no
use now.
Link: https://lore.kernel.org/r/20210407043927.10623-4-xiang@kernel.org
Acked-by: Chao Yu <yuchao0@huawei.com>
Signed-off-by: Gao Xiang <hsiangkao@redhat.com>
Introduce superblock checksum feature in order to
check at mounting time.
Note that the first 1024 bytes are ignore for x86
boot sectors and other oddities.
Link: https://lore.kernel.org/r/20191104024937.113939-1-gaoxiang25@huawei.com
Signed-off-by: Pratik Shinde <pratikshinde320@gmail.com>
Reviewed-by: Chao Yu <yuchao0@huawei.com>
Cc: Dan Carpenter <dan.carpenter@oracle.com>
Signed-off-by: Gao Xiang <gaoxiang25@huawei.com>
EROFS filesystem has been merged into linux-staging for a year.
EROFS is designed to be a better solution of saving extra storage
space with guaranteed end-to-end performance for read-only files
with the help of reduced metadata, fixed-sized output compression
and decompression inplace technologies.
In the past year, EROFS was greatly improved by many people as
a staging driver, self-tested, betaed by a large number of our
internal users, successfully applied to almost all in-service
HUAWEI smartphones as the part of EMUI 9.1 and proven to be stable
enough to be moved out of staging.
EROFS is a self-contained filesystem driver. Although there are
still some TODOs to be more generic, we have a dedicated team
actively keeping on working on EROFS in order to make it better
with the evolution of Linux kernel as the other in-kernel filesystems.
As Pavel suggested, it's better to do as one commit since git
can do moves and all histories will be saved in this way.
Let's promote it from staging and enhance it more actively as
a "real" part of kernel for more wider scenarios!
Cc: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
Cc: Alexander Viro <viro@zeniv.linux.org.uk>
Cc: Andrew Morton <akpm@linux-foundation.org>
Cc: Stephen Rothwell <sfr@canb.auug.org.au>
Cc: Theodore Ts'o <tytso@mit.edu>
Cc: Pavel Machek <pavel@denx.de>
Cc: David Sterba <dsterba@suse.cz>
Cc: Amir Goldstein <amir73il@gmail.com>
Cc: Christoph Hellwig <hch@infradead.org>
Cc: Darrick J . Wong <darrick.wong@oracle.com>
Cc: Dave Chinner <david@fromorbit.com>
Cc: Jaegeuk Kim <jaegeuk@kernel.org>
Cc: Jan Kara <jack@suse.cz>
Cc: Richard Weinberger <richard@nod.at>
Cc: Linus Torvalds <torvalds@linux-foundation.org>
Cc: Chao Yu <yuchao0@huawei.com>
Cc: Miao Xie <miaoxie@huawei.com>
Cc: Li Guifu <bluce.liguifu@huawei.com>
Cc: Fang Wei <fangwei1@huawei.com>
Signed-off-by: Gao Xiang <gaoxiang25@huawei.com>
Link: https://lore.kernel.org/r/20190822213659.5501-1-hsiangkao@aol.com
Signed-off-by: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
Jingbo Xu
Gao Xiang