I think the ~/.mozilla/firefox/XXX.default-YYY/storage/default/https+++ZZZ.com/cache/https+++domain.com/ style dirs are the storage for what's called "service workers" which is persistent code related to each website that sends notifiications even if no related tab is open.
Suppose you have a favorite website that sells something, you might register with them that you're interested in a particular kind of product. A serviceworker for that site would be in the "ZZZ" folder named after that site, the code in there would run even if you don't have a tab open for that site. It's done so you can get a notification. In other cases it's some other code that the web designers don't want to have to reload each time you visit, caching it in your storage folder saves time and network.
You can see all your service workers in the Firefox menu: Help -> More troubleshooting information -> about:serviceworkers ( or load about:serviceworkers )
v1.6.0 has been released with this feature; prefix your source image configuration with docker:// to use a base image stored in the Docker daemon.
Gradle: jib.from.image = 'docker://docker-image'
Maven: <from><image>docker://docker-image</image></from>
The purgeServerSideCache method is deprecated and calling it has no effect - you'll get a console warning about that. This method is now replaced with refreshServerSideStore
Hard disk space can easily get filled up with cached package files, old kernels, and other obsolete files that occupy unwanted hard disk space. Here are top five best and safer ways to clean and free up hard disk space in Ubuntu. We show you Terminal and GUI way of cleaning up system.
In this tutorial you will learn everything you need to know about cache memory in an easy to follow language. The cache memory is the hig-speed memory insite the CPU.
slabtop command (part of the package procps) shows top memory objects used by the kernel.
dstat can help you figure out what is happening. dstat -cdnpmgs --top-bio --top-cpu --top-mem
Also have a look at smem ("smem -kt"), it can show you nicely what is in your swap.
ZRAM if you have no HDD/SSD swap partition.
ZSWAP if you do have a HDD/SSD swap partition.
ZCACHE: It does what ZSWAP does and ALSO compresses and speeds the filesystem page cache. (It is internally much more complicated and is not in the mainline kernel as it is still under development).
A CachedRowSet object is special in that it can operate without being connected to its data source, that is, it is a disconnected RowSet object. It gets its name from the fact that it stores (caches) its data in memory so that it can operate on its own data rather than on the data stored in a database.
MapProxy is an open source proxy for geospatial data. It caches, accelerates and transforms data from existing map services and serves any desktop or web GIS client.
Ben Nadel takes his previous HTML5 Cache Manifest experiment and converts it into an "App Mode" iPhone application that can fun in full-screen, offline mode.
Is Ehcache a NoSQL store? No, I would not characterise it as that, but I have seen it used for some NoSQL use cases. In these situations it compared very well — with higher performance and more flexible consistency than the well-known NoSQL stores. Let me explain.
"To add CSS or JS that should be present on all pages, modules should not implement this hook, but declare these files in their .info file."
CSS files can be added to a .info file using the following format:
Redis is a key-value database. It is similar to memcached but the dataset is not volatile, and keys can be strings, exactly like in memcached, but also lists and sets with atomic operations to push/pop elements. In order to be very fast but at the same time persistent the whole dataset is taken in memory and from time to time and/or when a number of changes to the dataset are performed it is written asynchronously on disk. You may lost the last few queries that is acceptable in many applications but it is as fast as an in memory DB (btw the SVN version of Redis includes support for replication in order to solve this problem by redundancy). Replication and other interesting features are a work in progress (Basic master <-> slave replication implemented in Redis SVN). Redis is written in ANSI C Redis is pretty fast!, 110000 SETs/second, 81000 GETs/second in an entry level Linux box.
Varnish is a state-of-the-art, high-performance HTTP accelerator. It uses the advanced features in Linux 2.6, FreeBSD 6/7 and Solaris 10 to achieve its high performance.
So far in this series (click here for an index of the complete series, as well as supporting screencasts), I have illustrated how to develop both a LO-REST, AJAX-Friendly service, as well as HI-REST services adhering to the unified API of HTTP. In the very first post, I touched on some aspects of REST, but I haven’t spent much time on the benefits of following a RESTful architectural style. I made mention of the fact that RESTful services follow the "way of the web". As it turns out, this proves to be quite powerful.
The growing disparity between processor and memory performance has made cache misses increasingly expensive. Additionally, data and instruction caches are not always used efficiently, resulting in large numbers of cache misses. Therefore, the importance of cache performance improvements at each level of the memory hierarchy will continue to grow. In numeric programs, there are several known compiler techniques for optimizing data cache performance. However, integer (nonnumeric) programs often have irregular access patterns that are more difficult for the compiler to optimize. In the past, cache management techniques such as cache bypassing were implemented manually at the machine-language-programming level. As the available chip area grows, it makes sense to spend more resources to allow intelligent control over the cache management. In this paper, we present an approach to improving cache effectiveness, taking advantage of the growing chip area, utilizing run-time adaptive cache management techniques, optimizing both performance and cost of implementation. Specifically, we are aiming to increase data cache effectiveness for integer programs. We propose a microarchitecture scheme where the hardware determines data placement within the cache hierarchy based on dynamic referencing behavior. This scheme is fully compatible with existing instruction set architectures. This paper examines the theoretical upper bounds on the cache hit ratio that cache bypassing can provide for integer applications, including several Windows applications with OS activity. Then, detailed trace-driven simulations of the integer applications are used to show that the implementation described in this paper can achieve performance close to that of the upper bound
Scalaris is a scalable, transactional, distributed key-value store. It can be used for building scalable Web 2.0 services.
Scalaris uses a structured overlay with a non-blocking Paxos commit protocol for transaction processing with strong consistency over replicas. Scalaris is implemented in Erlang.
memcached is a high-performance, distributed memory object caching system, generic in nature, but intended for use in speeding up dynamic web applications by alleviating database load.
A library for using Memcached as a second level distributed cache in Hibernate.
* Based on the excellent spymemcached client
* Includes support for the Whalin (danga) memcached client
* Supports entity and query caching.
* See the Configuration page
* See how easy it is to configure in Grails
Welcome to the newly open sourced SHOP.COM Cache System: sccache
What is it?
The SHOP.COM Cache System is an object cache system that...
* is an in-process cache and external, shared Cache
* is horizontally scalable
* stores cached objects to disk
* supports associative keys
* is non-transactional
* can have any size key and any size data
* does auto-GC based on TTL
* is container and platform neutral
M. Martin, J. Unbehauen, и S. Auer. Proceedings of 7th Extended Semantic Web Conference (ESWC 2010), 30 May -- 3 June 2010, Heraklion, Crete, Greece, том 6089 из Lecture Notes in Computer Science, стр. 304-318. Berlin / Heidelberg, Springer, (2010)
M. Martin, J. Unbehauen, и S. Auer. Proceedings of 7th Extended Semantic Web Conference (ESWC 2010), 30 May -- 3 June 2010, Heraklion, Crete, Greece, том 6089 из Lecture Notes in Computer Science, стр. 304--318. Berlin / Heidelberg, Springer, (2010)
M. Bilal, и S. Kang. IEEE Access, 5 (1):
1962–1701(2017)cite arxiv:1702.04078Comment: This print includes minor enhancement and corrections to the published journal version of this article in IEEE Access.
M. Bilal, и S. Kang. (2017)cite arxiv:1702.04078Comment: This article is published in IEEE Access. This print includes minor enhancement and corrections to the published journal version of this article.
M. Martin, J. Unbehauen, и S. Auer. Proceedings of 7th Extended Semantic Web Conference (ESWC 2010), 30 May -- 3 June 2010, Heraklion, Crete, Greece, том 6089 из Lecture Notes in Computer Science, стр. 304--318. Berlin / Heidelberg, Springer, (2010)
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