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  • I am trying to figure out the best way to understand how to cache domain objects

    - by Brett Ryan
    I've always done this wrong, I'm sure a lot of others have too, hold a reference via a map and write through to DB etc.. I need to do this right, and I just don't know how to go about it. I know how I want my objects to be cached but not sure on how to achieve it. What complicates things is that I need to do this for a legacy system where the DB can change without notice to my application. So in the context of a web application, let's say I have a WidgetService which has several methods: Widget getWidget(); Collection<Widget> getAllWidgets(); Collection<Widget> getWidgetsByCategory(String categoryCode); Collection<Widget> getWidgetsByContainer(Integer parentContainer); Collection<Widget> getWidgetsByStatus(String status); Given this, I could decide to cache by method signature, i.e. getWidgetsByCategory("AA") would have a single cache entry, or I could cache widgets individually, which would be difficult I believe; OR, a call to any method would then first cache ALL widgets with a call to getAllWidgets() but getAllWidgets() would produce caches that match all the keys for the other method invocations. For example, take the following untested theoretical code. Collection<Widget> getAllWidgets() { Entity entity = cache.get("ALL_WIDGETS"); Collection<Widget> res; if (entity == null) { res = loadCache(); } else { res = (Collection<Widget>) entity.getValue(); } return res } Collection<Widget> loadCache() { // Get widgets from underlying DB Collection<Widget> res = db.getAllWidgets(); cache.put("ALL_WIDGETS", res); Map<String, List<Widget>> byCat = new HashMap<>(); for (Widget w : res) { // cache by different types of method calls, i.e. by category if (!byCat.containsKey(widget.getCategory()) { byCat.put(widget.getCategory(), new ArrayList<Widget>); } byCat.get(widget.getCatgory(), widget); } cacheCategories(byCat); return res; } Collection<Widget> getWidgetsByCategory(String categoryCode) { CategoryCacheKey key = new CategoryCacheKey(categoryCode); Entity ent = cache.get(key); if (entity == null) { loadCache(); } ent = cache.get(key); return ent == null ? Collections.emptyList() : (Collection<Widget>)ent.getValue(); } NOTE: I have not worked with a cache manager, the above code illustrates cache as some object that may hold caches by key/value pairs, though it's not modelled on any specific implementation. Using this I have the benefit of being able to cache all objects in the different ways they will be called with only single objects on the heap, whereas if I were to cache the method call invocation via say Spring It would (I believe) cache multiple copies of the objects. I really wish to try and understand the best ways to cache domain objects before I go down the wrong path and make it harder for myself later. I have read the documentation on the Ehcache website and found various articles of interest, but nothing to give a good solid technique. Since I'm working with an ERP system, some DB calls are very complicated, not that the DB is slow, but the business representation of the domain objects makes it very clumsy, coupled with the fact that there are actually 11 different DB's where information can be contained that this application is consolidating in a single view, this makes caching quite important.

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  • disk not accessible

    - by user107044
    i formatted my hard drive yesterday and it was working well even after the formatting. But when I restarted my system again , is is showing that the space is alloted to my files but they are inaccessible. I have even tried to unhide the files and folders, if they got hidden somehow. But nothing works. the hard drive is being shown empty but the properties are saying that it still conatins the data : http://imgur.com/ObjTE in the image, it is showing that the directory has only 1 file of size:4.8 kbps but the space being used by the drive is 11.6 GB. do suggest some solution.

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  • Disk drive won't let go of password prompt at bootup?

    - by user54003
    I had a hacker intrude into my system, at the time it was obvious, so I reinstalled. However, I am left with what appears to be a fatal problem as far as one of my disk drives goes. When I install that drive in my system, a prompt comes up for the disk password, and what it is asking for is a root password. The disk works otherwise normally but despite all my efforts, I have not been able to fix this disk. I have gotten the operating system parted magic and done the most extreme clean up available, the internal one which sends a signal to the disk electronics which runs a built in clean up program. Darik's boot and nuke, I've tried them all but I can't seem to remove this with anything in the Linux line. Does anyone have any suggestions? I've run gparted, created a Sun, an Apple and various other schemes to partition the disk, all to no avail. Can anyone help?

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  • Cache efficient code

    - by goldenmean
    This could sound a subjective question, but what i am looking for is specific instances which you would have encountered related to this. 1) How to make a code, cache effective-cache friendly? (More cache hits, as less cahce misses as possible). from both perspectives, data cache & program cache(instruction cache). i.e. What all things in one's code, related to data structures, code constructs one should take care of to make it cache effective. 2) Are there any particular data structures one must use, must avoid,or particular way of accessing the memers of that structure etc.. to make code cache effective. 3) Are there any program constructs(if, for, switch, break, goto,...), code-flow(for inside a if, if inside a for, etc...) one should follow/avoid in this matter? I am looking forward to hear individual experiences related to making a cache efficient code in general. It can be any programming language(C,C++,ASsembly,...), any hardware target(ARM,Intel,PowerPC,...), any OS(Windows,Linux,Symbian,...) etc.. More the variety, it will help better to understand it deeply.

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  • Win 8: Adding a boot volume to an MBR dynamic disk [NOT about changing to basic disks]

    - by Stilez
    (This is NOT aiming to convert to basic disk. In this question, the disk stays dynamic but becomes bootable) There doesn't seem to be a clear, well stated answer I can find, for the question "What are the criteria for Windows 8 to successfully boot from an MBR dynamic disk", or "how do I fix a dynamic MBR partition that's failing boot"? I've tried to educate myself but can't find crucial information to clear it all up. My existing HDD/SSD setup: DISK 0 ~ 60GB SSD/MBR/basic: (350MB recovery)(60GB windows 8 bootable) DISK 1 ~ 512GB SSD/MBR/dynamic: (350MB recovery)(60GB unallocated)(410GB mirrored data) DISK 2 ~ 512GB SSD/MBR/dynamic: (350MB recovery)(60GB unallocated)(410GB mirrored data) DISKS 3, 4, 5: (ignored for simplicity: 2xHDD RAID1 + caching SSD) I'm heavy duty on data crunching and virtualisation, just maxxed out 32GB RAM @ 2133 and moved to 4960X + 64GB. Disk 0 is a pure system disk of little value, and virtualisations runs off mirrored SSDs (Samsung 840 Pro 512 x 2) for double speed reading and so they snapshot in reasonable time. I'm using 4 SATA3 ports and the board only has two decent Intel ports (onboard Marvell are poorer quality). I'm wary of choosing between LSI, HighPoint and other 3rd party controllers as I'm unfamiliar with the maze of decent RAID cards (that's a whole other issue!). I want to cut down my SSD needs by moving the boot volume and caching volume to the 840 pros, giving a setup with 2 fewer SSDs: DISK 0 ~ 512GB SSD/MBR/dynamic: (350MB recovery)(60GB boot)(410GB mirrored data) DISK 1 ~ 512GB SSD/MBR/dynamic: (350MB recovery)(30GB cache for the ICH10R mirror)(30GB temp)(410GB mirrored data) DISKS 2, 3: (2xHDD RAID1) Intel's RST allows this, Win 8 allows booting off a MBR/dynamic disk, and the two 60GB SSDs are hardly the fastest SSDs anyway, they'll get repurposed. Moving the caching volume is easy. Moving the boot volume has me stumped. The difficulty is, I'm hitting a wall of knowledge here. I have a UEFI Asus motherboard with an previous traditional MBR/basic boot disk, and I want it to boot from a disk and volume that's MBR/dynamic. The disk copy is physically ok (Partition Wizard Server will copy to dynamic volumes) but then hits a light blue 0xc000000e boot error. No real surprise, I expected to have some boot fixing, but had expected Windows to boot-fix it (all drivers exist), or the usual manual fixes to work. Specifically, I don't know enough, to know what's got to be manually checked and perhaps corrected for the disk to boot (legacy/uefi/bios, odd partitions, boot tables, disk IDs, hidden boot files, oh my!), or if I need to change any of this secure boot/UEFI/legacy stuff in the bios, convert a 512 SSD to basic and then back to dynamic when working, or if the issue is pure OS config using "diskpart", "bootsect" and "bootrec" from the Win8 DVD. The old system disk still boots but I don't know enough to figure what to fix, to make the system boot as I want. The answers probably aren't hard but the real issue is my confusion and missing information. Thanks for helping!

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  • Too many Bind query (cache) denied, DNS attack?

    - by Jake
    Once Bind crashed and I did: tail -f /var/log/messages I see a massive number of logs every second. Is this a DNS attack? or is there something wrong? Sometimes I see a domain in logs like this: dOmAin.com (upper and lower). As you see there is only one single domain in the logs with different IPs Oct 10 02:21:26 mail named[20831]: client 74.125.189.18#38921: query (cache) 'ns1.domain2.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 192.221.144.171#38833: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 74.125.189.17#42428: query (cache) 'ns2.domain2.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 192.221.146.27#37899: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 193.203.82.66#39263: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 8.0.16.170#59723: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 80.169.197.66#32903: query (cache) 'dOmAin.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 134.58.60.1#47558: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 192.221.146.34#47387: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 8.0.16.8#59392: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 74.125.189.19#64395: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 217.72.163.3#42190: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 83.146.21.252#22020: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 192.221.146.116#57342: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 193.203.82.66#52020: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 8.0.16.72#64317: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 80.169.197.66#31989: query (cache) 'dOmAin.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 74.125.189.18#47436: query (cache) 'ns2.domain2.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 74.125.189.16#44005: query (cache) 'ns1.domain2.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 85.132.31.10#50379: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 94.241.128.3#60106: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 85.132.31.10#59118: query (cache) 'domain.com/A/IN' denied Oct 10 02:21:26 mail named[20831]: client 212.95.135.78#27811: query (cache) 'domain.com/A/IN' denied /etc/resolv.conf ; generated by /sbin/dhclient-script nameserver 4.2.2.4 nameserver 8.8.4.4 Bind config: // generated by named-bootconf.pl options { directory "/var/named"; /* * If there is a firewall between you and nameservers you want * to talk to, you might need to uncomment the query-source * directive below. Previous versions of BIND always asked * questions using port 53, but BIND 8.1 uses an unprivileged * port by default. */ // query-source address * port 53; allow-transfer { none; }; allow-recursion { localnets; }; //listen-on-v6 { any; }; notify no; }; // // a caching only nameserver config // controls { inet 127.0.0.1 allow { localhost; } keys { rndckey; }; }; zone "." IN { type hint; file "named.ca"; }; zone "localhost" IN { type master; file "localhost.zone"; allow-update { none; }; }; zone "0.0.127.in-addr.arpa" IN { type master; file "named.local"; allow-update { none; }; };

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  • Datanucleus/JDO Level 2 Cache on Google App Engine

    - by Thilo
    Is it possible (and does it make sense) to use the JDO Level 2 Cache for the Google App Engine Datastore? First of all, why is there no documentation about this on Google's pages? Are there some problems with it? Do we need to set up limits to protect our memcache quota? According to DataNucleus on Stackoverflow, you can set the following persistence properties: datanucleus.cache.level2.type=javax.cache datanucleus.cache.level2.cacheName={cache name} Is that all? Can we choose any cache name? Other sources on the Internet report using different settings. Also, it seems we need to download the DataNucleus Cache support plugin. Which version would be appropriate? And do we just place it in WEB-INF/lib or does it need more setup to activate it?

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  • Collections not read from hibernate/ehcache second-level-cache

    - by Mark van Venrooij
    I'm trying to cache lazy loaded collections with ehcache/hibernate in a Spring project. When I execute a session.get(Parent.class, 123) and browse through the children multiple times a query is executed every time to fetch the children. The parent is only queried the first time and then resolved from the cache. Probably I'm missing something, but I can't find the solution. Please see the relevant code below. I'm using Spring (3.2.4.RELEASE) Hibernate(4.2.1.Final) and ehcache(2.6.6) The parent class: @Entity @Table(name = "PARENT") @Cacheable @Cache(usage = CacheConcurrencyStrategy.READ_WRITE, include = "all") public class ServiceSubscriptionGroup implements Serializable { /** The Id. */ @Id @Column(name = "ID") private int id; @OneToMany(fetch = FetchType.LAZY, mappedBy = "parent") @Cache(usage = CacheConcurrencyStrategy.READ_WRITE) private List<Child> children; public List<Child> getChildren() { return children; } public void setChildren(List<Child> children) { this.children = children; } @Override public boolean equals(Object o) { if (this == o) return true; if (o == null || getClass() != o.getClass()) return false; Parent that = (Parent) o; if (id != that.id) return false; return true; } @Override public int hashCode() { return id; } } The child class: @Entity @Table(name = "CHILD") @Cacheable @Cache(usage = CacheConcurrencyStrategy.READ_WRITE, include = "all") public class Child { @Id @Column(name = "ID") private int id; @ManyToOne(fetch = FetchType.LAZY, cascade = CascadeType.ALL) @JoinColumn(name = "PARENT_ID") @Cache(usage = CacheConcurrencyStrategy.READ_WRITE) private Parent parent; public int getId() { return id; } public void setId(final int id) { this.id = id; } private Parent getParent(){ return parent; } private void setParent(Parent parent) { this.parent = parent; } @Override public boolean equals(final Object o) { if (this == o) { return true; } if (o == null || getClass() != o.getClass()) { return false; } final Child that = (Child) o; return id == that.id; } @Override public int hashCode() { return id; } } The application context: <bean id="sessionFactory" class="org.springframework.orm.hibernate4.LocalSessionFactoryBean"> <property name="dataSource" ref="dataSource" /> <property name="annotatedClasses"> <list> <value>Parent</value> <value>Child</value> </list> </property> <property name="hibernateProperties"> <props> <prop key="hibernate.dialect">org.hibernate.dialect.SQLServer2008Dialect</prop> <prop key="hibernate.hbm2ddl.auto">validate</prop> <prop key="hibernate.ejb.naming_strategy">org.hibernate.cfg.ImprovedNamingStrategy</prop> <prop key="hibernate.connection.charSet">UTF-8</prop> <prop key="hibernate.show_sql">true</prop> <prop key="hibernate.format_sql">true</prop> <prop key="hibernate.use_sql_comments">true</prop> <!-- cache settings ehcache--> <prop key="hibernate.cache.use_second_level_cache">true</prop> <prop key="hibernate.cache.use_query_cache">true</prop> <prop key="hibernate.cache.region.factory_class"> org.hibernate.cache.ehcache.SingletonEhCacheRegionFactory</prop> <prop key="hibernate.generate_statistics">true</prop> <prop key="hibernate.cache.use_structured_entries">true</prop> <prop key="hibernate.cache.use_query_cache">true</prop> <prop key="hibernate.transaction.factory_class"> org.hibernate.engine.transaction.internal.jta.JtaTransactionFactory</prop> <prop key="hibernate.transaction.jta.platform"> org.hibernate.service.jta.platform.internal.JBossStandAloneJtaPlatform</prop> </props> </property> </bean> The testcase I'm running: @Test public void testGetParentFromCache() { for (int i = 0; i <3 ; i++ ) { getEntity(); } } private void getEntity() { Session sess = sessionFactory.openSession() sess.setCacheMode(CacheMode.NORMAL); Transaction t = sess.beginTransaction(); Parent p = (Parent) s.get(Parent.class, 123); Assert.assertNotNull(p); Assert.assertNotNull(p.getChildren().size()); t.commit(); sess.flush(); sess.clear(); sess.close(); } In the logging I can see that the first time 2 queries are executed getting the parent and getting the children. Furthermore the logging shows that the child entities as well as the collection are stored in the 2nd level cache. However when reading the collection a query is executed to fetch the children on second and third attempt.

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  • Organization &amp; Architecture UNISA Studies &ndash; Chap 4

    - by MarkPearl
    Learning Outcomes Explain the characteristics of memory systems Describe the memory hierarchy Discuss cache memory principles Discuss issues relevant to cache design Describe the cache organization of the Pentium Computer Memory Systems There are key characteristics of memory… Location – internal or external Capacity – expressed in terms of bytes Unit of Transfer – the number of bits read out of or written into memory at a time Access Method – sequential, direct, random or associative From a users perspective the two most important characteristics of memory are… Capacity Performance – access time, memory cycle time, transfer rate The trade off for memory happens along three axis… Faster access time, greater cost per bit Greater capacity, smaller cost per bit Greater capacity, slower access time This leads to people using a tiered approach in their use of memory   As one goes down the hierarchy, the following occurs… Decreasing cost per bit Increasing capacity Increasing access time Decreasing frequency of access of the memory by the processor The use of two levels of memory to reduce average access time works in principle, but only if conditions 1 to 4 apply. A variety of technologies exist that allow us to accomplish this. Thus it is possible to organize data across the hierarchy such that the percentage of accesses to each successively lower level is substantially less than that of the level above. A portion of main memory can be used as a buffer to hold data temporarily that is to be read out to disk. This is sometimes referred to as a disk cache and improves performance in two ways… Disk writes are clustered. Instead of many small transfers of data, we have a few large transfers of data. This improves disk performance and minimizes processor involvement. Some data designed for write-out may be referenced by a program before the next dump to disk. In that case the data is retrieved rapidly from the software cache rather than slowly from disk. Cache Memory Principles Cache memory is substantially faster than main memory. A caching system works as follows.. When a processor attempts to read a word of memory, a check is made to see if this in in cache memory… If it is, the data is supplied, If it is not in the cache, a block of main memory, consisting of a fixed number of words is loaded to the cache. Because of the phenomenon of locality of references, when a block of data is fetched into the cache, it is likely that there will be future references to that same memory location or to other words in the block. Elements of Cache Design While there are a large number of cache implementations, there are a few basic design elements that serve to classify and differentiate cache architectures… Cache Addresses Cache Size Mapping Function Replacement Algorithm Write Policy Line Size Number of Caches Cache Addresses Almost all non-embedded processors support virtual memory. Virtual memory in essence allows a program to address memory from a logical point of view without needing to worry about the amount of physical memory available. When virtual addresses are used the designer may choose to place the cache between the MMU (memory management unit) and the processor or between the MMU and main memory. The disadvantage of virtual memory is that most virtual memory systems supply each application with the same virtual memory address space (each application sees virtual memory starting at memory address 0), which means the cache memory must be completely flushed with each application context switch or extra bits must be added to each line of the cache to identify which virtual address space the address refers to. Cache Size We would like the size of the cache to be small enough so that the overall average cost per bit is close to that of main memory alone and large enough so that the overall average access time is close to that of the cache alone. Also, larger caches are slightly slower than smaller ones. Mapping Function Because there are fewer cache lines than main memory blocks, an algorithm is needed for mapping main memory blocks into cache lines. The choice of mapping function dictates how the cache is organized. Three techniques can be used… Direct – simplest technique, maps each block of main memory into only one possible cache line Associative – Each main memory block to be loaded into any line of the cache Set Associative – exhibits the strengths of both the direct and associative approaches while reducing their disadvantages For detailed explanations of each approach – read the text book (page 148 – 154) Replacement Algorithm For associative and set associating mapping a replacement algorithm is needed to determine which of the existing blocks in the cache must be replaced by a new block. There are four common approaches… LRU (Least recently used) FIFO (First in first out) LFU (Least frequently used) Random selection Write Policy When a block resident in the cache is to be replaced, there are two cases to consider If no writes to that block have happened in the cache – discard it If a write has occurred, a process needs to be initiated where the changes in the cache are propagated back to the main memory. There are several approaches to achieve this including… Write Through – all writes to the cache are done to the main memory as well at the point of the change Write Back – when a block is replaced, all dirty bits are written back to main memory The problem is complicated when we have multiple caches, there are techniques to accommodate for this but I have not summarized them. Line Size When a block of data is retrieved and placed in the cache, not only the desired word but also some number of adjacent words are retrieved. As the block size increases from very small to larger sizes, the hit ratio will at first increase because of the principle of locality, which states that the data in the vicinity of a referenced word are likely to be referenced in the near future. As the block size increases, more useful data are brought into cache. The hit ratio will begin to decrease as the block becomes even bigger and the probability of using the newly fetched information becomes less than the probability of using the newly fetched information that has to be replaced. Two specific effects come into play… Larger blocks reduce the number of blocks that fit into a cache. Because each block fetch overwrites older cache contents, a small number of blocks results in data being overwritten shortly after they are fetched. As a block becomes larger, each additional word is farther from the requested word and therefore less likely to be needed in the near future. The relationship between block size and hit ratio is complex, and no set approach is judged to be the best in all circumstances.   Pentium 4 and ARM cache organizations The processor core consists of four major components: Fetch/decode unit – fetches program instruction in order from the L2 cache, decodes these into a series of micro-operations, and stores the results in the L2 instruction cache Out-of-order execution logic – Schedules execution of the micro-operations subject to data dependencies and resource availability – thus micro-operations may be scheduled for execution in a different order than they were fetched from the instruction stream. As time permits, this unit schedules speculative execution of micro-operations that may be required in the future Execution units – These units execute micro-operations, fetching the required data from the L1 data cache and temporarily storing results in registers Memory subsystem – This unit includes the L2 and L3 caches and the system bus, which is used to access main memory when the L1 and L2 caches have a cache miss and to access the system I/O resources

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  • centos 100% disk full - How to remove log files, history, etc?

    - by kopeklan
    mysqld won't start because disk space is full: 101221 14:06:50 [ERROR] /usr/libexec/mysqld: Error writing file '/var/run/mysqld/mysqld.pid' (Errcode: 28) 101221 14:06:50 [ERROR] Can't start server: can't create PID file: No space left on device running df -h: Filesystem Size Used Avail Use% Mounted on /dev/sda2 16G 3.2G 12G 23% / /dev/sda5 4.8G 4.6G 0 100% /var /dev/sda3 430G 855M 407G 1% /home /dev/sda1 76M 24M 49M 33% /boot tmpfs 956M 0 956M 0% /dev/shm du -sh * in /var: 12K account 56M cache 24K db 32K empty 8.0K games 1.5G lib 8.0K local 32K lock 221M log 16K lost+found 0 mail 24K named 8.0K nis 8.0K opt 8.0K preserve 8.0K racoon 292K run 70M spool 8.0K tmp 76K webmin 2.6G www 20K yp in /dev/sda5, there is website files in /var/www. because this is first time, I have no idea which files to remove other than moving /var/www to other partition And one more, what is the right way to remove log files, history, etc in /dev/sda5?

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  • Is it OK to set "Cache-Control: public" when sending “304 Not Modified” for images stored in the dat

    - by Emilien
    After asking a question about sending “304 Not Modified” for images stored in the in the Google App Engine datastore, I now have a question about Cache-Control. My app now sends Last-Modified and Etag, but by default GAE alsto sends Cache-Control: no-cache. According to this page: The “no-cache” directive, according to the RFC, tells the browser that it should revalidate with the server before serving the page from the cache. [...] In practice, IE and Firefox have started treating the no-cache directive as if it instructs the browser not to even cache the page. As I DO want browsers to cache the image, I've added the following line to my code: self.response.headers['Cache-Control'] = "public" According to the same page as before: The “cache-control: public” directive [...] tells the browser and proxies [...] that the page may be cached. This is good for non-sensitive pages, as caching improves performance. The question is if this could be harmful to the application in some way? Would it be best to send Cache-Control: must-revalidate to "force" the browser to revalidate (I suppose that is the behavior that was originally the reason behind sending Cache-Control: no-cache) This directive insists that the browser must revalidate the page against the server before serving it from cache. Note that it implicitly lets the browser cache the page.

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  • Hard disk with bad clusters

    - by Dan
    I have been trying to backup some files up to DVD recently, and the burn process failed saying the CRC check failed for certain files. I then tried to browse to these files in Windows explorer and my whole machine locks up and I have to reboot. I ran check disk without the '/F /R' arguments and it told me I had bad sectors. So I re-ran it with the arguments and check disk fails during the 'Chkdsk is verifying usn journal' stage with this error: Insufficient disk space to fix the usn journal $j data stream The hard disk is a 300GB Partition on a 400GB Disk, and there is 160GBs of free space on the partition. My os (Windows 7) is installed on the other partition and is running fine. Any idea how I fix this? or repair it enough to copy my files off it?

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  • Squid refresh_pattern won't cache "Expires: ..."

    - by Marcelo Cantos
    Background I frequent the OpenGL ES documentation site at http://www.khronos.org/opengles/sdk/1.1/docs/man/. Even though the content is completely static, it seems to force a reload on every single page I visit, which is very annoying. I have a squid 3.0 proxy set up (apt-get install squid3 on Ubuntu 10.04), and I added a refresh_pattern to force the pages to cache: refresh_pattern ^http://www.khronos.org/opengles/sdk/1\.1/docs/man/ … 1440 20% 10080 … override-expire ignore-reload ignore-no-cache ignore-private ignore-no-store This is all on one line, of course. While this appears to work for the XHTML documents (e.g., glBindTexture), it fails to cache the linked content, such as the DTD, some .ent files (?) and some XSL files. The delay in fetching these extra files delays rendering of the main document, so my principal annoyance isn't fixed. The only difference I can glean with these ancillary files is that they come with an Expires: header set to the current time, whereas the XHTML document has none. But I would have expected the override-expire option to fix this. I have confirmed that documents have the same base URL. I have also truncated the pattern to varying degrees, with no effect. My questions Why does the override-expire option not seem to work? Is there a simple way to tell squid to unconditionally cache a document, no matter what it finds in the response headers? (Hopefully) relevant output cache.log Jan 01 10:33:30 1970/06/25 21:18:27| Processing Configuration File: /etc/squid3/squid.conf (depth 0) Jan 01 10:33:30 1970/06/25 21:18:27| WARNING: use of 'override-expire' in 'refresh_pattern' violates HTTP Jan 01 10:33:30 1970/06/25 21:18:27| WARNING: use of 'ignore-reload' in 'refresh_pattern' violates HTTP Jan 01 10:33:30 1970/06/25 21:18:27| WARNING: use of 'ignore-no-cache' in 'refresh_pattern' violates HTTP Jan 01 10:33:30 1970/06/25 21:18:27| WARNING: use of 'ignore-no-store' in 'refresh_pattern' violates HTTP Jan 01 10:33:30 1970/06/25 21:18:27| WARNING: use of 'ignore-private' in 'refresh_pattern' violates HTTP Jan 01 10:33:30 1970/06/25 21:18:27| DNS Socket created at 0.0.0.0, port 37082, FD 10 Jan 01 10:33:30 1970/06/25 21:18:27| Adding nameserver 192.168.1.1 from /etc/resolv.conf Jan 01 10:33:30 1970/06/25 21:18:27| Accepting HTTP connections at 0.0.0.0, port 3128, FD 11. Jan 01 10:33:30 1970/06/25 21:18:27| Accepting ICP messages at 0.0.0.0, port 3130, FD 13. Jan 01 10:33:30 1970/06/25 21:18:27| HTCP Disabled. Jan 01 10:33:30 1970/06/25 21:18:27| Loaded Icons. Jan 01 10:33:30 1970/06/25 21:18:27| Ready to serve requests. access.log Jun 25 21:19:35 2010.710 0 192.168.1.50 TCP_MEM_HIT/200 2452 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/glBindTexture.xml - NONE/- text/xml Jun 25 21:19:36 2010.263 543 192.168.1.50 TCP_MISS/304 322 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/xhtml1-transitional.dtd - DIRECT/74.54.224.215 - Jun 25 21:19:36 2010.276 556 192.168.1.50 TCP_MISS/304 370 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/mathml.xsl - DIRECT/74.54.224.215 - Jun 25 21:19:36 2010.666 278 192.168.1.50 TCP_MISS/304 322 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/xhtml-lat1.ent - DIRECT/74.54.224.215 - Jun 25 21:19:36 2010.958 279 192.168.1.50 TCP_MISS/304 322 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/xhtml-symbol.ent - DIRECT/74.54.224.215 - Jun 25 21:19:37 2010.251 276 192.168.1.50 TCP_MISS/304 322 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/xhtml-special.ent - DIRECT/74.54.224.215 - Jun 25 21:19:37 2010.332 0 192.168.1.50 TCP_IMS_HIT/304 316 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/ctop.xsl - NONE/- text/xml Jun 25 21:19:37 2010.332 0 192.168.1.50 TCP_IMS_HIT/304 316 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/pmathml.xsl - NONE/- text/xml store.log Jun 25 21:19:36 2010.263 RELEASE -1 FFFFFFFF D3056C09B42659631A65A08F97794E45 304 1277464776 -1 1277464776 unknown -1/0 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/xhtml1-transitional.dtd Jun 25 21:19:36 2010.276 RELEASE -1 FFFFFFFF 9BF7F37442FD84DD0AC0479E38329E3C 304 1277464776 -1 1277464776 unknown -1/0 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/mathml.xsl Jun 25 21:19:36 2010.666 RELEASE -1 FFFFFFFF 7BCFCE88EC91578C8E2589CB6310B3A1 304 1277464776 -1 1277464776 unknown -1/0 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/xhtml-lat1.ent Jun 25 21:19:36 2010.958 RELEASE -1 FFFFFFFF ECF1B24E437CFAA08A2785AA31A042A0 304 1277464777 -1 1277464777 unknown -1/0 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/xhtml-symbol.ent Jun 25 21:19:37 2010.251 RELEASE -1 FFFFFFFF 36FE3D76C80F0106E6E9F3B7DCE924FA 304 1277464777 -1 1277464777 unknown -1/0 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/xhtml-special.ent Jun 25 21:19:37 2010.332 RELEASE -1 FFFFFFFF A33E5A5CCA2BFA059C0FA25163485192 304 1277462871 1221139523 1277462871 text/xml -1/0 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/ctop.xsl Jun 25 21:19:37 2010.332 RELEASE -1 FFFFFFFF E2CF8854443275755915346052ACE14E 304 1277462872 1221139523 1277462872 text/xml -1/0 GET http://www.khronos.org/opengles/sdk/1.1/docs/man/pmathml.xsl

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  • Cloning single disk drive to multiple drives simultaneously

    - by mr.b
    Hi, I am looking for a way to clone single disk drive to more than one disk drive at the same time. I have prepared system images on 1TB disks, and it takes almost 2 hours to clone one disk to another, and then it goes up exponentially, in order to have say 30 disks cloned. If it was possible to clone one disk to more than single target, it would simplify whole procedure a lot. Also, is there something that prevents this kind of operation? I mean, is there some special reason why every disk cloning software that I know about supports only single target drive? Thanks! P.S. This question is cross-post from superuser, I hope nobody minds.

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  • zeroing a disk with dd vs Disk Utility

    - by jdizzle
    I'm attempting to zero a disk on my Mac OS X machine. I'm going for complete zeros and unformatted, so I think of dd. Unfortunately the maximum throughput I've managed to get out of dd is 7MB/s. Just for grins I tried disk utility and it has a throughput of 19MB/s. What gives? I've tried changing the bs option on dd to all sorts of values, but it still hovers around 7MB/s. Why is disk utility so much faster?

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  • How to limit disk performance?

    - by DrakeES
    I am load-testing a web application and studying the impact of some config tweaks (related to disk i/o) on the overall app performance, i.e. the amount of users that can be handled simultaneously. But the problem is that I hit 100% CPU before I can see any effect of the disk-related config settings. I am therefore wondering if there is a way I could deliberately limit the disk performance so that it becomes the bottleneck and the tweaks I am trying to play with actually start impacting performance. Should I just make the hard disk busy with something else? What would serve the best for this purpose? More details (probably irrelevant, but anyway): PHP/Magento/Apache, studying the impact of apc.stat. Setting it to 0 makes APC not checking PHP scripts for modification which should increase performance where disk is the bottleneck. Using JMeter for benchmarking.

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  • Beginners advice on Small business network disk(s)

    - by Rob
    We are having 10 PCs used by various user and presently use one network disk (a LaCie NAS) for all our data. Everything is Windows Vista and our collective IT hardware knowledge is minimal. This worked well generally. However, recently the disk freqently loses connection from the network (2-3 times per week) and the only way back seems to be the "turn it off and back on" trick. This obviously cant be any good for the disk. I understand that there are various more sophisticated ways of storing data and was wondering what people would recommend. One of the worries is obviously disk failure (either in part or as a whole) and the lack of continued availability due to network issues. I would guess that a disk which replicates data wouldnt work as a sole solution due to the network connection, but dont know what hardware (and/or software) would/could work in our case. In terms of size, we are looking at very small amounts, ie. less than 500 GB in total.

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  • virtualbox 2 vmware disk

    - by anol
    I have a virtualbox disk I'd like to convert to a vmware disk. The disk is dynamic which makes it a lot more trickier. If I follow the instructions at http://xpapad.wordpress.com/2010/02/21/migrating-from-virtualbox-to-vmware-in-linux, the vdi-to-raw conversion will result in a 2 TB file. I don't even have that much disk space! The first step therefore seems to be a dynamic to static conversion of the virtualbox disk, right? How do I do that or is there perhaps a better way to convert to vmware? Help!

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  • using one disk as cache for others

    - by HugoRune
    Hi Given a PC with several hard drives: Is it possible to use one fast disk as a giant file cache? I.e. automatically copying frequently accessed data to that one disk, and transparently redirecting reads and writes to that disk, so that other drives would only have be accessed occassionally. (writes would have to be forwarded to the other disks after a while of course) Advantages: the other drives could be powered down most of the time; reducing power, heat, noise speed of the other drives would not matter much. cache disk could be solid state. How can I set such a system up? What OS supports these options? Is this possible at all using Windows or Linux?

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  • Help create a unit test for test response header, specifically Cache-Control, in determining if cach

    - by VajNyiaj
    Scenario: I have a base controller which disables caching within the OnActionExecuting override. protected override void OnActionExecuting(ActionExecutingContext filterContext) { filterContext.HttpContext.Response.Cache.SetExpires(DateTime.UtcNow.AddDays(-1)); filterContext.HttpContext.Response.Cache.SetValidUntilExpires(false); filterContext.HttpContext.Response.Cache.SetRevalidation(HttpCacheRevalidation.AllCaches); filterContext.HttpContext.Response.Cache.SetCacheability(HttpCacheability.NoCache); //IE filterContext.HttpContext.Response.Cache.SetNoStore(); //FireFox } How can I create a Unit Test to test this behavior?

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  • Clear Asp.Net cache from outside of application (not using source code)

    - by TheJudge
    Hi, I have a asp.net web application and I'm using cache (HttpRuntime.Cache) to save some stuff from db. I also update db from time to time so that data in db does not match the data in my application's cache. Is there any way how to clear my application's cache without modifying any source code or republishing the page? I tried to restart IIS and to clear browsers cache but nothing helps. Please help.

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  • Working with the IE cache from C# & WPF

    - by Eric
    I'm writing a program in C# using the WPF framework. I need to display images, and I'd like to cache them to avoid downloading them constantly. I can code my own cache, however, IE already has a caching system. I can find code to read entries out of the IE cache, however I've found nothing dealing with the issue of adding items to the cache. Is there a good way to do it, or should I just implement a separate cache?

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  • Possible disk IO issue

    - by Tim Meers
    I've been trying to really figure out what my IOPS are on my DB server array and see if it's just too much. The array is four 72.6gb 15k rpm drives in RAID 5. To calculate IOPS for RAID 5 the following formula is used: (reads + (4 * Writes)) / Number of disks = total IOPS. The formula is from MSDN. I also want to calculate the Avg Queue Length but I'm not sure where they are getting the formula from, but i think it reads on that page as avg que length/number of disks = actual queue. To populate that formula I used the perfmon to gather the needed information. I came up with this, under normal production load: (873.982 + (4 * 28.999)) / 4 = 247.495. Also the disk queue lengh of 14.454/4 = 3.614. So to the question, am I wrong in thinking this array has a very high disk IO? Edit I got the chance to review it again this morning under normal/high load. This time with even bigger numbers and IOPS in excess of 600 for about 5 minutes then it died down again. But I also took a look at the Avg sec/Transfer, %Disk Time, and %Idle Time. These number were taken when the reads/writes per sec were only 332.997/17.999 respectively. %Disk Time: 219.436 %Idle Time: 0.300 Avg Disk Queue Length: 2.194 Avg Disk sec/Transfer: 0.006 Pages/sec: 2927.802 % Processor Time: 21.877 Edit (again) Looks like I have that issue solved. Thanks for the help. Also for a pretty slick parser I found this: http://pal.codeplex.com/ It works pretty well for breaking down the data into something usable.

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  • Disk (EXT4) suddenly empty without any sign of why

    - by Ohnomydisk
    I have a Ubuntu 10.04 server with several disks in it. The disks are setup with a union filesystem, which presents them all as one logical /home. A few days ago, one of the disks appears to have suddenly 'become empty', for lack of better explanation. The amount of data on the /home mount almost halved within minutes - the disk appears to have had just over 400 GB of data prior to 'becoming empty'. I have absolutely no idea what happened. I was not using the server at the other time, but there are half a dozen other users who may have been (without root access and without the ability to hose a whole disk). I've ran SMART tests on the disk and it comes back clean. The filesystem checks fine (it has 12 GB used now, as some user software continued downloading after the incident). All I know is that around around midnight on October 19, the disk usage changed dramatically: The data points are every 15 minutes, and the full loss occured between captures: 2012-10-18 23:58:03.399647 - has 953.97/2059.07 GB [46.33 percent] 2012-10-19 00:13:15.909010 - has 515.18/2059.07 GB [25.02 percent] Other than that, I have not much to go off :-( I know that: There's nothing interesting in log files at that time Nobody appeared to be logged in via SSH at the time it occured (most users do not even use SSH) The server was online through whatever occured (3 months uptime) None of the other disks were affected and everything else on the server looks completely normal I have tried using "extundelete" on the disk and it didn't really find anything (some temporary files, but they looked new anyway) I am completely at a loss to what could have caused this. I was initially thinking maybe root escalation exploit, but even if someone did maliciously "rm" the disk contents, it would take more than 15 minutes for 400 GB?

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