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  • Calculating the Size (in Bytes and MB) of a Oracle Coherence Cache

    - by Ricardo Ferreira
    The concept and usage of data grids are becoming very popular in this days since this type of technology are evolving very fast with some cool lead products like Oracle Coherence. Once for a while, developers need an programmatic way to calculate the total size of a specific cache that are residing in the data grid. In this post, I will show how to accomplish this using Oracle Coherence API. This example has been tested with 3.6, 3.7 and 3.7.1 versions of Oracle Coherence. To start the development of this example, you need to create a POJO ("Plain Old Java Object") that represents a data structure that will hold user data. This data structure will also create an internal fat so I call that should increase considerably the size of each instance in the heap memory. Create a Java class named "Person" as shown in the listing below. package com.oracle.coherence.domain; import java.io.Serializable; import java.util.ArrayList; import java.util.HashMap; import java.util.List; import java.util.Random; @SuppressWarnings("serial") public class Person implements Serializable { private String firstName; private String lastName; private List<Object> fat; private String email; public Person() { generateFat(); } public Person(String firstName, String lastName, String email) { setFirstName(firstName); setLastName(lastName); setEmail(email); generateFat(); } private void generateFat() { fat = new ArrayList<Object>(); Random random = new Random(); for (int i = 0; i < random.nextInt(18000); i++) { HashMap<Long, Double> internalFat = new HashMap<Long, Double>(); for (int j = 0; j < random.nextInt(10000); j++) { internalFat.put(random.nextLong(), random.nextDouble()); } fat.add(internalFat); } } public String getFirstName() { return firstName; } public void setFirstName(String firstName) { this.firstName = firstName; } public String getLastName() { return lastName; } public void setLastName(String lastName) { this.lastName = lastName; } public String getEmail() { return email; } public void setEmail(String email) { this.email = email; } } Now let's create a Java program that will start a data grid into Coherence and will create a cache named "People", that will hold people instances with sequential integer keys. Each person created in this program will trigger the execution of a custom constructor created in the People class that instantiates an internal fat (the random amount of data generated to increase the size of the object) for each person. Create a Java class named "CreatePeopleCacheAndPopulateWithData" as shown in the listing below. package com.oracle.coherence.demo; import com.oracle.coherence.domain.Person; import com.tangosol.net.CacheFactory; import com.tangosol.net.NamedCache; public class CreatePeopleCacheAndPopulateWithData { public static void main(String[] args) { // Asks Coherence for a new cache named "People"... NamedCache people = CacheFactory.getCache("People"); // Creates three people that will be putted into the data grid. Each person // generates an internal fat that should increase its size in terms of bytes... Person pessoa1 = new Person("Ricardo", "Ferreira", "[email protected]"); Person pessoa2 = new Person("Vitor", "Ferreira", "[email protected]"); Person pessoa3 = new Person("Vivian", "Ferreira", "[email protected]"); // Insert three people at the data grid... people.put(1, pessoa1); people.put(2, pessoa2); people.put(3, pessoa3); // Waits for 5 minutes until the user runs the Java program // that calculates the total size of the people cache... try { System.out.println("---> Waiting for 5 minutes for the cache size calculation..."); Thread.sleep(300000); } catch (InterruptedException ie) { ie.printStackTrace(); } } } Finally, let's create a Java program that, using the Coherence API and JMX, will calculate the total size of each cache that the data grid is currently managing. The approach used in this example was retrieve every cache that the data grid are currently managing, but if you are interested on an specific cache, the same approach can be used, you should only filter witch cache will be looked for. Create a Java class named "CalculateTheSizeOfPeopleCache" as shown in the listing below. package com.oracle.coherence.demo; import java.text.DecimalFormat; import java.util.Map; import java.util.Set; import java.util.TreeMap; import javax.management.MBeanServer; import javax.management.MBeanServerFactory; import javax.management.ObjectName; import com.tangosol.net.CacheFactory; public class CalculateTheSizeOfPeopleCache { @SuppressWarnings({ "unchecked", "rawtypes" }) private void run() throws Exception { // Enable JMX support in this Coherence data grid session... System.setProperty("tangosol.coherence.management", "all"); // Create a sample cache just to access the data grid... CacheFactory.getCache(MBeanServerFactory.class.getName()); // Gets the JMX server from Coherence data grid... MBeanServer jmxServer = getJMXServer(); // Creates a internal data structure that would maintain // the statistics from each cache in the data grid... Map cacheList = new TreeMap(); Set jmxObjectList = jmxServer.queryNames(new ObjectName("Coherence:type=Cache,*"), null); for (Object jmxObject : jmxObjectList) { ObjectName jmxObjectName = (ObjectName) jmxObject; String cacheName = jmxObjectName.getKeyProperty("name"); if (cacheName.equals(MBeanServerFactory.class.getName())) { continue; } else { cacheList.put(cacheName, new Statistics(cacheName)); } } // Updates the internal data structure with statistic data // retrieved from caches inside the in-memory data grid... Set<String> cacheNames = cacheList.keySet(); for (String cacheName : cacheNames) { Set resultSet = jmxServer.queryNames( new ObjectName("Coherence:type=Cache,name=" + cacheName + ",*"), null); for (Object resultSetRef : resultSet) { ObjectName objectName = (ObjectName) resultSetRef; if (objectName.getKeyProperty("tier").equals("back")) { int unit = (Integer) jmxServer.getAttribute(objectName, "Units"); int size = (Integer) jmxServer.getAttribute(objectName, "Size"); Statistics statistics = (Statistics) cacheList.get(cacheName); statistics.incrementUnit(unit); statistics.incrementSize(size); cacheList.put(cacheName, statistics); } } } // Finally... print the objects from the internal data // structure that represents the statistics from caches... cacheNames = cacheList.keySet(); for (String cacheName : cacheNames) { Statistics estatisticas = (Statistics) cacheList.get(cacheName); System.out.println(estatisticas); } } public MBeanServer getJMXServer() { MBeanServer jmxServer = null; for (Object jmxServerRef : MBeanServerFactory.findMBeanServer(null)) { jmxServer = (MBeanServer) jmxServerRef; if (jmxServer.getDefaultDomain().equals(DEFAULT_DOMAIN) || DEFAULT_DOMAIN.length() == 0) { break; } jmxServer = null; } if (jmxServer == null) { jmxServer = MBeanServerFactory.createMBeanServer(DEFAULT_DOMAIN); } return jmxServer; } private class Statistics { private long unit; private long size; private String cacheName; public Statistics(String cacheName) { this.cacheName = cacheName; } public void incrementUnit(long unit) { this.unit += unit; } public void incrementSize(long size) { this.size += size; } public long getUnit() { return unit; } public long getSize() { return size; } public double getUnitInMB() { return unit / (1024.0 * 1024.0); } public double getAverageSize() { return size == 0 ? 0 : unit / size; } public String toString() { StringBuffer sb = new StringBuffer(); sb.append("\nCache Statistics of '").append(cacheName).append("':\n"); sb.append(" - Total Entries of Cache -----> " + getSize()).append("\n"); sb.append(" - Used Memory (Bytes) --------> " + getUnit()).append("\n"); sb.append(" - Used Memory (MB) -----------> " + FORMAT.format(getUnitInMB())).append("\n"); sb.append(" - Object Average Size --------> " + FORMAT.format(getAverageSize())).append("\n"); return sb.toString(); } } public static void main(String[] args) throws Exception { new CalculateTheSizeOfPeopleCache().run(); } public static final DecimalFormat FORMAT = new DecimalFormat("###.###"); public static final String DEFAULT_DOMAIN = ""; public static final String DOMAIN_NAME = "Coherence"; } I've commented the overall example so, I don't think that you should get into trouble to understand it. Basically we are dealing with JMX. The first thing to do is enable JMX support for the Coherence client (ie, an JVM that will only retrieve values from the data grid and will not integrate the cluster) application. This can be done very easily using the runtime "tangosol.coherence.management" system property. Consult the Coherence documentation for JMX to understand the possible values that could be applied. The program creates an in memory data structure that holds a custom class created called "Statistics". This class represents the information that we are interested to see, which in this case are the size in bytes and in MB of the caches. An instance of this class is created for each cache that are currently managed by the data grid. Using JMX specific methods, we retrieve the information that are relevant for calculate the total size of the caches. To test this example, you should execute first the CreatePeopleCacheAndPopulateWithData.java program and after the CreatePeopleCacheAndPopulateWithData.java program. The results in the console should be something like this: 2012-06-23 13:29:31.188/4.970 Oracle Coherence 3.6.0.4 <Info> (thread=Main Thread, member=n/a): Loaded operational configuration from "jar:file:/E:/Oracle/Middleware/oepe_11gR1PS4/workspace/calcular-tamanho-cache-coherence/lib/coherence.jar!/tangosol-coherence.xml" 2012-06-23 13:29:31.219/5.001 Oracle Coherence 3.6.0.4 <Info> (thread=Main Thread, member=n/a): Loaded operational overrides from "jar:file:/E:/Oracle/Middleware/oepe_11gR1PS4/workspace/calcular-tamanho-cache-coherence/lib/coherence.jar!/tangosol-coherence-override-dev.xml" 2012-06-23 13:29:31.219/5.001 Oracle Coherence 3.6.0.4 <D5> (thread=Main Thread, member=n/a): Optional configuration override "/tangosol-coherence-override.xml" is not specified 2012-06-23 13:29:31.266/5.048 Oracle Coherence 3.6.0.4 <D5> (thread=Main Thread, member=n/a): Optional configuration override "/custom-mbeans.xml" is not specified Oracle Coherence Version 3.6.0.4 Build 19111 Grid Edition: Development mode Copyright (c) 2000, 2010, Oracle and/or its affiliates. All rights reserved. 2012-06-23 13:29:33.156/6.938 Oracle Coherence GE 3.6.0.4 <Info> (thread=Main Thread, member=n/a): Loaded Reporter configuration from "jar:file:/E:/Oracle/Middleware/oepe_11gR1PS4/workspace/calcular-tamanho-cache-coherence/lib/coherence.jar!/reports/report-group.xml" 2012-06-23 13:29:33.500/7.282 Oracle Coherence GE 3.6.0.4 <Info> (thread=Main Thread, member=n/a): Loaded cache configuration from "jar:file:/E:/Oracle/Middleware/oepe_11gR1PS4/workspace/calcular-tamanho-cache-coherence/lib/coherence.jar!/coherence-cache-config.xml" 2012-06-23 13:29:35.391/9.173 Oracle Coherence GE 3.6.0.4 <D4> (thread=Main Thread, member=n/a): TCMP bound to /192.168.177.133:8090 using SystemSocketProvider 2012-06-23 13:29:37.062/10.844 Oracle Coherence GE 3.6.0.4 <Info> (thread=Cluster, member=n/a): This Member(Id=2, Timestamp=2012-06-23 13:29:36.899, Address=192.168.177.133:8090, MachineId=55685, Location=process:244, Role=Oracle, Edition=Grid Edition, Mode=Development, CpuCount=2, SocketCount=2) joined cluster "cluster:0xC4DB" with senior Member(Id=1, Timestamp=2012-06-23 13:29:14.031, Address=192.168.177.133:8088, MachineId=55685, Location=process:1128, Role=CreatePeopleCacheAndPopulateWith, Edition=Grid Edition, Mode=Development, CpuCount=2, SocketCount=2) 2012-06-23 13:29:37.172/10.954 Oracle Coherence GE 3.6.0.4 <D5> (thread=Cluster, member=n/a): Member 1 joined Service Cluster with senior member 1 2012-06-23 13:29:37.188/10.970 Oracle Coherence GE 3.6.0.4 <D5> (thread=Cluster, member=n/a): Member 1 joined Service Management with senior member 1 2012-06-23 13:29:37.188/10.970 Oracle Coherence GE 3.6.0.4 <D5> (thread=Cluster, member=n/a): Member 1 joined Service DistributedCache with senior member 1 2012-06-23 13:29:37.188/10.970 Oracle Coherence GE 3.6.0.4 <Info> (thread=Main Thread, member=n/a): Started cluster Name=cluster:0xC4DB Group{Address=224.3.6.0, Port=36000, TTL=4} MasterMemberSet ( ThisMember=Member(Id=2, Timestamp=2012-06-23 13:29:36.899, Address=192.168.177.133:8090, MachineId=55685, Location=process:244, Role=Oracle) OldestMember=Member(Id=1, Timestamp=2012-06-23 13:29:14.031, Address=192.168.177.133:8088, MachineId=55685, Location=process:1128, Role=CreatePeopleCacheAndPopulateWith) ActualMemberSet=MemberSet(Size=2, BitSetCount=2 Member(Id=1, Timestamp=2012-06-23 13:29:14.031, Address=192.168.177.133:8088, MachineId=55685, Location=process:1128, Role=CreatePeopleCacheAndPopulateWith) Member(Id=2, Timestamp=2012-06-23 13:29:36.899, Address=192.168.177.133:8090, MachineId=55685, Location=process:244, Role=Oracle) ) RecycleMillis=1200000 RecycleSet=MemberSet(Size=0, BitSetCount=0 ) ) TcpRing{Connections=[1]} IpMonitor{AddressListSize=0} 2012-06-23 13:29:37.891/11.673 Oracle Coherence GE 3.6.0.4 <D5> (thread=Invocation:Management, member=2): Service Management joined the cluster with senior service member 1 2012-06-23 13:29:39.203/12.985 Oracle Coherence GE 3.6.0.4 <D5> (thread=DistributedCache, member=2): Service DistributedCache joined the cluster with senior service member 1 2012-06-23 13:29:39.297/13.079 Oracle Coherence GE 3.6.0.4 <D4> (thread=DistributedCache, member=2): Asking member 1 for 128 primary partitions Cache Statistics of 'People': - Total Entries of Cache -----> 3 - Used Memory (Bytes) --------> 883920 - Used Memory (MB) -----------> 0.843 - Object Average Size --------> 294640 I hope that this post could save you some time when calculate the total size of Coherence cache became a requirement for your high scalable system using data grids. See you!

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  • Cache-control for permanent 301 redirects nginx

    - by gansbrest
    I was wondering if there is a way to control lifetime of the redirects in Nginx? We would liek to cache 301 redirects in CDN for specific amount of time, let say 20 minutes and the CDN is controlled by the standard caching headers. By default there is no Cache-control or Expires directives with the Nginx redirect. That could cause the redirect to be cached for a really long time. By having specific redirect lifetime the system could have a chance to correct itself, knowing that even "permanent" redirect change from time to time.. The other thing is that those redirects are included from the Server block, which according the nginx specification should be evaluated before locations. I tried to add add_header Cache-Control "max-age=1200, public"; to the bottom of the redirects file, but the problem is that Cache-control gets added twice - first comes let say from the backend script and the other one added by the add_header directive.. In Apache there is the environment variable trick to control headers for rewrites: RewriteRule /taxonomy/term/(\d+)/feed /taxonomy/term/$1 [R=301,E=expire:1] Header always set Cache-Control "store, max-age=1200" env=expire But I'm not sure how to accomplish this in Nginx.

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  • Nginx Cache-Control

    - by optixx
    Iam serving my static content with ngnix. location /static { alias /opt/static/blog/; access_log off; etags on; etag_hash on; etag_hash_method md5; expires 1d; add_header Pragma "public"; add_header Cache-Control "public, must-revalidate, proxy-revalidate"; } The resulting header looks like this: Cache-Control:public, must-revalidate, proxy-revalidate Cache-Control:max-age=86400 Connection:close Content-Encoding:gzip Content-Type:application/x-javascript; charset=utf-8 Date:Tue, 11 Sep 2012 08:39:05 GMT Etag:e2266fb151337fc1996218fafcf3bcee Expires:Wed, 12 Sep 2012 08:39:05 GMT Last-Modified:Tue, 11 Sep 2012 06:22:41 GMT Pragma:public Server:nginx/1.2.2 Transfer-Encoding:chunked Vary:Accept-Encoding Why is nginx sending 2 Cache-Control entries, could this be a problem for the clients?

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  • Flush mod_pagespeed cache in Debian

    - by Ivar
    I need a way to flush the mod_pagespeed cache while developing. According to mod_pagespeed documents, I should run the following command: sudo touch /var/mod_pagespeed/cache/cache.flush In Debian it's "su" instead of "sudo". However, it doesn't work for me; there's no "touch" command, nor is there any "cache.flush" file in the defined directory. Have I missed something? You kick-ass Linux users, please be humble - I'm pretty new to these stuff. Thank you in advance!

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  • Force request to miss cache but still store the response

    - by Tom Marthenal
    I have a slow web app that I've placed Varnish in front of. All of the pages are static (they don't vary for a different user), but they need to be updated every 5 minutes so they contain recent data. I have a simple script (wget --mirror) that crawls the entire website every 15 minutes. Each crawl takes about 5 minutes. The point of the crawl is to update every page in the Varnish cache so that a user never has to wait for the page to generate (since all pages have been generated recently thanks to the spider). The timeline looks like this: 00:00:00: Cache flushed 00:00:00: Spider starts crawling to update cache with new pages 00:05:00: Spider finishes crawling, all pages are updated until 1:15 A request that comes in between 0:00:00 and 0:05:00 might hit a page that hasn't been updated yet, and will be forced to wait a few seconds for a response. This isn't acceptable. What I'd like to do is, perhaps using some VCL magic, always foward requests from the spider to the backend, but still store the response in the cache. This way, a user will never have to wait for a page to generate since there is no 5-minute window in which parts of the cache are empty (except perhaps at server startup). How can I do this?

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  • How to increase the disk cache of Windows 7

    - by Mark Christiaens
    Under Windows 7 (64 bit), I'm reading through 9000 moderately sized files. In total, there is more than 200 MB of data. Using Java (JDK 1.6.21) I'm iterating over the files. The first 1400 or so go at full speed but then speed drops off to 4ms per file. It turns out that the main cost is incurred simply by opening the files. I'm opening the files using new FileInputStream (and of course closing them in time to avoid file leaks). After some investigating, I see that Windows' disk cache is using only 100 MB or so of RAM although I have 8 GiB available. I've tried increasing the cache size using the CacheSet tool but any values I provide are considered out of range. I've also tried enabling the LargeSystemCache registry key but (after rebooting) the CacheSet tool still indicates I'm using 100 MB of cache (and doesn't increase during the test run). Does anybody have any suggestions to "encourage" Windows 7 to cache my 9000 files?

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  • SBUG Session: The Enterprise Cache

    - by EltonStoneman
    [Source: http://geekswithblogs.net/EltonStoneman] I did a session on "The Enterprise Cache" at the UK SOA/BPM User Group yesterday which generated some useful discussion. The proposal was for a dedicated caching layer which all app servers and service providers can hook into, sharing resources and common data. The architecture might end up like this: I'll update this post with a link to the slide deck once it's available. The next session will have Udi Dahan walking through nServiceBus, register on EventBrite if you want to come along. Synopsis Looked at the benefits and drawbacks of app-centric isolated caches, compared to an enterprise-wide shared cache running on dedicated nodes; Suggested issues and risks around caching including staleness of data, resource usage, performance and testing; Walked through a generic service cache implemented as a WCF behaviour – suitable for IIS- or BizTalk-hosted services - which I'll be releasing on CodePlex shortly; Listed common options for cache providers and their offerings. Discussion Cache usage. Different value propositions for utilising the cache: improved performance, isolation from underlying systems (e.g. service output caching can have a TTL large enough to cover downtime), reduced resource impact – CPU, memory, SQL and cost (e.g. caching results of paid-for services). Dedicated cache nodes. Preferred over in-host caching provided latency is acceptable. Depending on cache provider, can offer easy scalability and global replication so cache clients always use local nodes. Restriction of AppFabric Caching to Windows Server 2008 not viewed as a concern. Security. Limited security model in most cache providers. Options for securing cache content suggested as custom implementations. Obfuscating keys and serialized values may mean additional security is not needed. Depending on security requirements and architecture, can ensure cache servers only accessible to cache clients via IPsec. Staleness. Generally thought to be an overrated problem. Thinking in line with eventual consistency, that serving up stale data may not be a significant issue. Good technical arguments support this, although I suspect business users will be harder to persuade. Providers. Positive feedback for AppFabric Caching – speed, configurability and richness of the distributed model making it a good enterprise choice. .NET port of memcached well thought of for performance but lack of replication makes it less suitable for these shared scenarios. Replicated fork – repcached – untried and less active than memcached. NCache also well thought of, but Express version too limited for enterprise scenarios, and commercial versions look costly compared to AppFabric.

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  • Missing Dependency Errors when Installing OpenVas Server

    - by David
    I'm trying to install OpenVAS on Red Hat Enterprise Linux 5.5. I've successfully run yum install openvas-client, but yum install openvas-server prints the following errors: --> Finished Dependency Resolution openvas-client-3.0.1-1.el5.art.i386 from installed has depsolving problems --> Missing Dependency: libopenvas_hg.so.3 is needed by package openvas-client-3.0.1-1.el5.art.i386 (installed) openvas-client-3.0.1-1.el5.art.i386 from installed has depsolving problems --> Missing Dependency: libopenvas_nasl.so.3 is needed by package openvas-client-3.0.1-1.el5.art.i386 (installed) openvas-client-3.0.1-1.el5.art.i386 from installed has depsolving problems --> Missing Dependency: libopenvas_omp.so.3 is needed by package openvas-client-3.0.1-1.el5.art.i386 (installed) openvas-scanner-3.2-0.2.el5.art.i386 from atomic has depsolving problems --> Missing Dependency: net-snmp-utils is needed by package openvas-scanner-3.2-0.2.el5.art.i386 (atomic) openvas-client-3.0.1-1.el5.art.i386 from installed has depsolving problems --> Missing Dependency: libopenvas_misc.so.3 is needed by package openvas-client-3.0.1-1.el5.art.i386 (installed) openvas-scanner-3.2-0.2.el5.art.i386 from atomic has depsolving problems --> Missing Dependency: openldap-clients is needed by package openvas-scanner-3.2-0.2.el5.art.i386 (atomic) openvas-client-3.0.1-1.el5.art.i386 from installed has depsolving problems --> Missing Dependency: libopenvas_base.so.3 is needed by package openvas-client-3.0.1-1.el5.art.i386 (installed) Error: Missing Dependency: net-snmp-utils is needed by package openvas-scanner-3.2-0.2.el5.art.i386 (atomic) Error: Missing Dependency: libopenvas_base.so.3 is needed by package openvas-client-3.0.1-1.el5.art.i386 (installed) Error: Missing Dependency: libopenvas_hg.so.3 is needed by package openvas-client-3.0.1-1.el5.art.i386 (installed) Error: Missing Dependency: libopenvas_nasl.so.3 is needed by package openvas-client-3.0.1-1.el5.art.i386 (installed) Error: Missing Dependency: openldap-clients is needed by package openvas-scanner-3.2-0.2.el5.art.i386 (atomic) Error: Missing Dependency: libopenvas_omp.so.3 is needed by package openvas-client-3.0.1-1.el5.art.i386 (installed) Error: Missing Dependency: libopenvas_misc.so.3 is needed by package openvas-client-3.0.1-1.el5.art.i386 (installed) You could try using --skip-broken to work around the problem You could try running: package-cleanup --problems package-cleanup --dupes rpm -Va --nofiles --nodigest The program package-cleanup is found in the yum-utils package. Notice that each of the missing dependencies is followed by the words (installed) or the words (atomic) - for the name of the repository. When I try to install any of these sub-dependencies, the installation fails (either due to missing dependencies or since the rpm is already installed). For example, if I try to install a rpm for "libopenvas_hg.so.3", I get an error message indicating that it is already installed. Yet "libopenvas_hg.so.3" is listed as a missing dependency. Why? Do I need to uninstall all of the "missing" dependences first?

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  • How should I force-enable BIND's persistent cache, or Unbound's persistent cache

    - by Jacob Rabinsun
    I am trying to run a local DNS server on my home computer so that I can both increase DNS lookups speed and reduce bandwidth use, so that both my laptop and my PC can do lookups faster. I have got BIND 9 running very smoothly, there is only one simple problem, and that being the fact that BIND is not a persistent DNS cache, and if I restart its service, the whole cash would be wiped out. So, is there a way that I could make BIND9 keep its cache after system restart? Also, which one is better Unbound or BIND? Which one would you suggest? Does Unbound DNS have a persistent cache or can it be enabled?

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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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  • 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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  • 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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  • 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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  • 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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  • 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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  • Dependency Walker Not Showing All the Depended Dll

    - by Ngu Soon Hui
    I have a fortran dll, and I want to know the assemblies that it depends on for redistribution purpose. One thing I found out is that the dependency walker doesn't show all of the dependencies, i.e, there are some dlls that my assembly is dependent on, but dependency walker doesn't show it out. An example would be a dll that makes use of intel mkl LAPACK dlls, but the dependency walker doesn't show that dependency. Why this is so? And any idea how to fix this problem, or is there other more reliable tool that I can use?

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  • Aggregating cache data from OCEP in CQL

    - by Manju James
    There are several use cases where OCEP applications need to join stream data with external data, such as data available in a Coherence cache. OCEP’s streaming language, CQL, supports simple cache-key based joins of stream data with data in Coherence (more complex queries will be supported in a future release). However, there are instances where you may need to aggregate the data in Coherence based on input data from a stream. This blog describes a sample that does just that. For our sample, we will use a simplified credit card fraud detection use case. The input to this sample application is a stream of credit card transaction data. The input stream contains information like the credit card ID, transaction time and transaction amount. The purpose of this application is to detect suspicious transactions and send out a warning event. For the sake of simplicity, we will assume that all transactions with amounts greater than $1000 are suspicious. The transaction history is available in a Coherence distributed cache. For every suspicious transaction detected, a warning event must be sent with maximum amount, total amount and total number of transactions over the past 30 days, as shown in the diagram below. Application Input Stream input to the EPN contains events of type CCTransactionEvent. This input has to be joined with the cache with all credit card transactions. The cache is configured in the EPN as shown below: <wlevs:caching-system id="CohCacheSystem" provider="coherence"/> <wlevs:cache id="CCTransactionsCache" value-type="CCTransactionEvent" key-properties="cardID, transactionTime" caching-system="CohCacheSystem"> </wlevs:cache> Application Output The output that must be produced by the application is a fraud warning event. This event is configured in the spring file as shown below. Source for cardHistory property can be seen here. <wlevs:event-type type-name="FraudWarningEvent"> <wlevs:properties type="tuple"> <wlevs:property name="cardID" type="CHAR"/> <wlevs:property name="transactionTime" type="BIGINT"/> <wlevs:property name="transactionAmount" type="DOUBLE"/> <wlevs:property name="cardHistory" type="OBJECT"/> </wlevs:properties </wlevs:event-type> Cache Data Aggregation using Java Cartridge In the output warning event, cardHistory property contains data from the cache aggregated over the past 30 days. To get this information, we use a java cartridge method. This method uses Coherence’s query API on credit card transactions cache to get the required information. Therefore, the java cartridge method requires a reference to the cache. This may be set up by configuring it in the spring context file as shown below: <bean class="com.oracle.cep.ccfraud.CCTransactionsAggregator"> <property name="cache" ref="CCTransactionsCache"/> </bean> This is used by the java class to set a static property: public void setCache(Map cache) { s_cache = (NamedCache) cache; } The code snippet below shows how the total of all the transaction amounts in the past 30 days is computed. Rest of the information required by CardHistory object is calculated in a similar manner. Complete source of this class can be found here. To find out more information about using Coherence's API to query a cache, please refer Coherence Developer’s Guide. public static CreditHistoryData(String cardID) { … Filter filter = QueryHelper.createFilter("cardID = :cardID and transactionTime :transactionTime", map); CardHistoryData history = new CardHistoryData(); Double sum = (Double) s_cache.aggregate(filter, new DoubleSum("getTransactionAmount")); history.setTotalAmount(sum); … return history; } The java cartridge method is used from CQL as seen below: select cardID, transactionTime, transactionAmount, CCTransactionsAggregator.execute(cardID) as cardHistory from inputChannel where transactionAmount1000 This produces a warning event, with history data, for every credit card transaction over $1000. That is all there is to it. The complete source for the sample application, along with the configuration files, is available here. In the sample, I use a simple java bean to load the cache with initial transaction history data. An input adapter is used to create and send transaction events for the input stream.

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  • The clock hands of the buffer cache

    - by Tony Davis
    Over a leisurely beer at our local pub, the Waggon and Horses, Phil Factor was holding forth on the esoteric, but strangely poetic, language of SQL Server internals, riddled as it is with 'sleeping threads', 'stolen pages', and 'memory sweeps'. Generally, I remain immune to any twinge of interest in the bowels of SQL Server, reasoning that there are certain things that I don't and shouldn't need to know about SQL Server in order to use it successfully. Suddenly, however, my attention was grabbed by his mention of the 'clock hands of the buffer cache'. Back at the office, I succumbed to a moment of weakness and opened up Google. He wasn't lying. SQL Server maintains various memory buffers, or caches. For example, the plan cache stores recently-used execution plans. The data cache in the buffer pool stores frequently-used pages, ensuring that they may be read from memory rather than via expensive physical disk reads. These memory stores are classic LRU (Least Recently Updated) buffers, meaning that, for example, the least frequently used pages in the data cache become candidates for eviction (after first writing the page to disk if it has changed since being read into the cache). SQL Server clearly needs some mechanism to track which pages are candidates for being cleared out of a given cache, when it is getting too large, and it is this mechanism that is somewhat more labyrinthine than I previously imagined. Each page that is loaded into the cache has a counter, a miniature "wristwatch", which records how recently it was last used. This wristwatch gets reset to "present time", each time a page gets updated and then as the page 'ages' it clicks down towards zero, at which point the page can be removed from the cache. But what is SQL Server is suffering memory pressure and urgently needs to free up more space than is represented by zero-counter pages (or plans etc.)? This is where our 'clock hands' come in. Each cache has associated with it a "memory clock". Like most conventional clocks, it has two hands; one "external" clock hand, and one "internal". Slava Oks is very particular in stressing that these names have "nothing to do with the equivalent types of memory pressure". He's right, but the names do, in that peculiar Microsoft tradition, seem designed to confuse. The hands do relate to memory pressure; the cache "eviction policy" is determined by both global and local memory pressures on SQL Server. The "external" clock hand responds to global memory pressure, in other words pressure on SQL Server to reduce the size of its memory caches as a whole. Global memory pressure – which just to confuse things further seems sometimes to be referred to as physical memory pressure – can be either external (from the OS) or internal (from the process itself, e.g. due to limited virtual address space). The internal clock hand responds to local memory pressure, in other words the need to reduce the size of a single, specific cache. So, for example, if a particular cache, such as the plan cache, reaches a defined "pressure limit" the internal clock hand will start to turn and a memory sweep will be performed on that cache in order to remove plans from the memory store. During each sweep of the hands, the usage counter on the cache entry is reduced in value, effectively moving its "last used" time to further in the past (in effect, setting back the wrist watch on the page a couple of hours) and increasing the likelihood that it can be aged out of the cache. There is even a special Dynamic Management View, sys.dm_os_memory_cache_clock_hands, which allows you to interrogate the passage of the clock hands. Frequently turning hands equates to excessive memory pressure, which will lead to performance problems. Two hours later, I emerged from this rather frightening journey into the heart of SQL Server memory management, fascinated but still unsure if I'd learned anything that I'd put to any practical use. However, I certainly began to agree that there is something almost Tolkeinian in the language of the deep recesses of SQL Server. Cheers, Tony.

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  • Dependency diagramming / mapping tool [closed]

    - by Lars
    I am looking for a tool that allows me to easily create and maintain dependency maps of our mission critical servers, apps, processes, etc. It needs to be intuitive and easy to work with and be able to generate diagrams that clearly show the dependencies graphically. What would be some good tools for this? I have looked at videos for AssetGen Sysmap and BluePrint from Pathwaysystems.com, and they both seem to fit my needs, but there has got to be more good systems like them that I can look at. I want to make sure I pick the best system for our needs (and limited budget).

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