Multiple compression

Extracted Attributes

• Software Definition

  Support for multiple compression streams and algorithms within a Linux compressed RAM block device

• Financial Definition

  A reduction in valuation ratios (such as P/E ratio) where investors pay less per dollar of earnings

• Key Financial Drivers

  Rising interest rates, shifting growth expectations, increased risk perception, and macroeconomic changes

• Opposite Term (Finance)

  Multiple expansion

• Supported Compression Algorithms in zram

  DEFLATE, LZ4, LZO (LZO-RLE), Zstandard, 842

Timeline

• Linux kernel version 3.15 is released, adding support for multiple compression streams and multiple compression algorithms to zram. (Source: Wikipedia: Zram)

  2014-06-08

• Payment processor Adyen suffers a 40% stock plunge, highlighting sector-wide multiple compression in the tech market. (Source: Document 6919e1f7-cce4-4da9-87e0-5773c8bd9e48)

  2023-08-18

Zram

zram, formerly called compcache, is a Linux kernel module for creating a compressed block device in RAM, i.e. a RAM disk with on-the-fly disk compression. The block device created with zram can then be used for swap or as a general-purpose RAM disk. The two most common uses for zram are for the storage of temporary files (/tmp) and as a swap device. Initially, zram had only the latter function, hence the original name "compcache" ("compressed cache"). When empty, zram block device allocates about 0.1% of its size. After four years in the Linux kernel's driver staging area, zram was introduced into the mainline Linux kernel in version 3.14, released on March 30, 2014. From Linux kernel version 3.15 onwards (released on June 8, 2014), zram supports multiple compression streams and multiple compression algorithms. Compression algorithms include DEFLATE (DEFLATE), LZ4 (LZ4, and LZ4HC "high compression"), LZO (LZO-RLE "run-length encoding"), Zstandard (ZSTD), 842 (842). From kernel 5.1, the default is LZO-RLE, which has a balance of speed and compression ratio. Like most other system parameters, the compression algorithm can be selected via sysfs. When used as a compressed swap space, zram is similar to zswap, which is not a general-purpose RAM disk, but rather an in-kernel compressed cache for swap pages. However, zswap always requires a backing store, which is not the case for zram. Since the introduction of CONFIG_ZRAM_WRITEBACK in kernel version 4.14, zram is also able to use a storage device as a backing store, so it is able to move less-frequently used pages to disk, albeit the device can be only a partition at the moment (and the process cannot be managed automatically by kernel when the memory needs to be freed, but requires pre-emptive user-space programming). Since kernel version 7.0, zram can send the data to the backing store without decompressing them first. When used for swap, zram (like zswap) allows Linux to make more efficient use of RAM, since the operating system can then hold more pages of memory in the compressed swap than if the same amount of RAM had been used as application memory or disk cache. This is particularly effective on machines that do not have much memory. In 2012, Ubuntu briefly considered enabling zram by default on computers with small amounts of installed RAM. For this same reason, Fedora enabled zram by default starting with release 33. Using compressed swap space with zram or zswap also offers advantages for low-end hardware devices such as embedded devices and netbooks. Such devices usually use flash-based storage, which has limited lifespan due to write amplification, and may also use it to provide swap space. Using zram or zswap reduces the swap usage, which effectively reduces the amount of wear placed on flash-based storage and makes it last longer. Using zram also results in significantly reduced I/O for Linux systems that require swapping. Additionally, zram in specific workloads can act as a method of nearly doubling memory (albeit at great risk without backup swap) while running faster than if zram was disabled. This is usually seen when using zram on low-end hardware alongside garbage-collected and I/O intensive workloads with slow (i.e. USB) storage, where writes to swap may cause long freezes. This can make persistent USB Linux installations more usable on low-memory systems.

Web Search Results

• Multiple Compression vs Expansion Explained | P/E Changes | StockTitan

  ## Frequently Asked Questions ### What is multiple compression in simple terms? Multiple compression occurs when investors pay less for each dollar of company earnings, causing valuation ratios like the P/E ratio to decrease. For example, if a stock's P/E ratio falls from 20 to 15, that's multiple compression – the market is now willing to pay only $15 for each $1 of earnings instead of $20. ### What causes P/E expansion and compression? The primary causes include interest rate changes (rising rates often cause compression, falling rates often cause expansion), shifts in growth expectations, changes in risk perception, sector rotation, and overall market sentiment. Company-specific factors like management changes or competitive advantages also drive multiple changes. [...] ## What Is Multiple Compression and Expansion? Think of valuation multiples like a mood ring for the stock market. Multiple compression occurs when investors suddenly decide they're willing to pay less for each dollar a company earns – imagine a popular restaurant where diners used to happily pay $30 for a burger suddenly deciding it's only worth $20. The burger hasn't changed, but the perception of its value has. Conversely, multiple expansion is when that same burger becomes worth $40 in diners' minds. The restaurant hasn't changed its recipe or service, but something shifted in how people value the experience. [...] ### What's the difference between multiple compression and a bear market? Multiple compression can occur in individual stocks or sectors without a bear market. However, bear markets usually involve widespread multiple compression across the entire market. Not all multiple compression leads to a bear market, but most bear markets feature significant P/E compression. ### How do investors analyze potential multiple expansion?

• algorithms - Arguments for, and the applicability of, using multiple forms of compression on one or more files? - Unix & Linux Stack Exchange

  `.tar.gz` Multiple compression doesn't do any good, as any general compression algorithm will try to squeeze the actual information to as small a file as possible, essentially making it look as random (high-entropy) as possible. Random data doesn't compress very well, so the later compression layers don't do much. Of course, if the initial compression is trivial (e.g. only run-length encoding), then a more sophisticated algorithm might still be able to compress the data further. But in that case, you'd probably be better off using the better algorithm on the original data. [...] Multiple rounds of compression will easily increase the file size, exactly because there are usually some headers involved. For file compression, it's useful to know what compression algorithm was used, and e.g. `gzip` also stores the original file name. `gzip` ilkkachu's user avatar ## You must log in to answer this question. Start asking to get answers Find the answer to your question by asking. Explore related questions See similar questions with these tags. ### Related #### Hot Network Questions # Subscribe to RSS To subscribe to this RSS feed, copy and paste this URL into your RSS reader. ##### Unix & Linux ##### Company ##### Stack Exchange Network Site design / logo © 2026 Stack Exchange Inc; user contributions licensed under CC BY-SA . rev 2026.7.30.44770 [...] TommyFlowers's user avatar ## 1 Answer 1 No, usually it doesn't make sense to compress multiple times unless you happen to have different layers trying to do compression (e.g. you have a PNG image, stored in a compressed tar archive (`.tar.gz`), stored on a filesystem or transferred over a link that does compression). `.tar.gz`

• Compressing Time Series Data | TDengine Database

  Simple8b Simple8b is a 64-bit algorithm that implements compressing multiple integers (between 0 and 1<<60-1) into a 64-bit storage structure. The first 4 bits represent the selector, and the last 60 bits are used to store data. The advantage is that it is simple and efficient, and the fixed-length encoding ensures the decompression efficiency, but the compression rate is low for large integers or large floating values. This algorithm is suitable for unsigned integers with a small range. LZO [...] There is no single compression method or algorithm that is optimal for all data; instead, an appropriate method is chosen based on the characteristics of the data being compressed. Data compression is a summary of the trends and rules found in data. These rules can be based on content – the similarities between adjacent frames in a video, for example; on representation, as in entropy coding and transform coding; or on bitrate, as in differential compression and deep compression. If the original data and decompressed data are exactly the same, the compression method can be considered lossless. A compression method that alters data is considered lossy. [...] ## TDengine implementation TDengine time series database (TSDB) provides three compression options: no compression, one-stage compression, and two-stage compression. The one-stage compression is correspondingly compressed according to the type of data. The compression algorithms include delta-delta encoding, simple 8B method, zig-zag encoding, LZ4 and other algorithms, which we have briefly introduced above. Two-stage compression is based on one-stage compression, and then compresses with a general compression algorithm to ensure a higher compression rate. To sum up, we can see that there are still many choices of compression algorithms, and the specific program can be comprehensively selected according to the compression effect and cost. Hongze Cheng

• A Survey of Data Compression Algorithms and their ...

  These packets are then compressed either within a single time or as a group and then sent to the decompressor where the process is reversed. Packet-based compression has been available for many years and can be found in routers, VPN clients. Packet-based compression systems have additional problems. When compressing packets, these systems must choose between writing small packets to the net-work and performing additional work to aggregate and encapsulate multiple packets. Neither option produces optimal results. Writing small packets to the network increases TCP/IP header overhead, while aggregating and encapsulating packets adds encapsulation headers to the stream. [...] The Huffman algorithm is better suited to smaller files, although compression is not significant for very small files. On the whole, the LZ algorithm is preferred over the Huffman algorithm, and a number of popular file compression utilities use variations of this algorithm. Please note that it is rather obvious that the more the words in a test data are similar, the more the compression ratio is. This fact is also observable from our simulation results. VI. MULTIMEDIA COMPRESSION: JPEG AND MPEG Multimedia images have become a vital and ubiqui-tous component of everyday life. The amount of infor-mation encoded in an image is quite large. Even with the advances in bandwidth and storage capabilities, if images were not compressed many applications would be too costly.

• Data compression - Wikipedia

  In information theory, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy "Redundancy (information theory)"). No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information. Typically, a device that performs data compression is referred to as an encoder, and one that performs the reversal of the process (decompression) as a decoder. [...] Most video compression formats and codecs exploit both spatial and temporal redundancy (e.g., through difference coding with motion compensation). Similarities can be encoded by only storing differences between e.g., temporally adjacent frames (inter-frame coding) or spatially adjacent pixels (intra-frame coding). Inter-frame compression (a temporal delta encoding) (re)uses data from one or more earlier or later frames in a sequence to describe the current frame. Intra-frame coding, on the other hand, uses only data from within the current frame, effectively being still-image compression. [...] [edit] Comparison of two revisions of a file Data compression can be viewed as a special case of data differencing. Data differencing consists of producing a difference given a source and a target, with patching reproducing the target given a source and a difference. Since there is no separate source and target in data compression, one can consider data compression as data differencing with empty source data, the compressed file corresponding to a difference from nothing. This is the same as considering absolute entropy "Entropy (information theory)") (corresponding to data compression) as a special case of relative entropy (corresponding to data differencing) with no initial data. The term differential compression is used to emphasize the data differencing connection. ## Uses